A novel binary toxin-positive non-027, non-078 Clostridium difficile strain designated LC693 whose sequence type was ST201 was isolated from the fecal sample of a patient with severe diarrhea in China. To understand the pathogenesis basis of C. difficile ST201, this recently recovered isolate LC693 was then chosen for whole genome sequencing. The project finally generated an estimated genome size of approximately 4.07 Mbp. The genome sequence was then analyzed together with the other two ST201 strains VL-0104 and VL-0391 and compared to the epidemic 027/ST1 and 078/ST11 strains. Phylogenetic analysis demonstrated that the ST201 strains belonged to clade 3. Genome size of the three ST201 strains ranged from 4.07 Mb~4.16 Mb, with an average GC content between 28.5%~28.9%. The ST201 genomes contained more than 40 antibiotic resistance genes and 15 of them were predicted to be associated with vancomycin-resistance, suggesting that they may have a strong antibiotic resistance. The ST201 strains contained a typical clade 3 specific PaLoc with a Tn6218 element inserted, and those genes harbored on their PaLoc that participated in the toxin expression and regulation were highly homologues to the epidemic 027 and 078 strains, with the exception of tcdC. A truncated TcdC was found in the ST201 strains, which is suggestive to have a contribution to the toxin production of the ST201 strains. In addition, the ST201 strains contained intact binary toxin coding and regulation genes, which is also proposed to contribute to the virulence. Genome comparison of the ST201 strains with the epidemic 027 and 078 strain identified 641 genes specific for the C. difficile ST201, and a number of them were predicted as fitness and virulence associated genes. The identification of those genes also contributes to the pathogenesis of the ST201 strain. To our knowledge, this is the first study that the genome sequence of C. difficile ST201 was discussed in detail, and the present study would have a contribution to understanding the pathogenesis basis of C. difficile ST201.

Black ginseng is a type of processed ginseng that is traditionally used as herbal medicine in East Asian countries. It is prepared from fresh, white, or red ginseng by undergoing a process of steaming and drying nine times. However, the chemical differentiation of black ginseng with different processing levels is not well understood. The aim of this study is to propose a new method for discriminating and quantifying black ginseng. Six ginsenosides from black ginseng were accurately quantified, and based on this, the black ginseng samples were divided into uncompleted and completed black ginseng. Ultra-high-performance liquid chromatography-quadrupole-time of flight/mass spectrometry (UPLC-Q-TOF/MS) combined with a multivariate statistical analysis strategy was then employed to differentiate the two groups. A total of 141 ions were selected as analytical markers of black ginseng, with 45 of these markers being annotated by matching precise m/z and MS/MS data from prior studies.

The aim of this article was to evaluate properties of new polymer material, based on PEEK, for FPDs in patients with parafunctions. Methods: Prospective clinical study involved patients with diagnosed signs and symptoms of parafunctions, restored with 100 units, PEEK based FPDs, veneered with composite material. During one year observation period the following parameters were evaluated: color changes, periodontal indices, treatment success using FDI criteria, occlusal parameters, patient-reported outcome measures (OHIP-49) and occurrence of complications. Results: Spectrophotometry revealed that 63% of bridges experienced changes for L*, a*, b* color parameters, but it was not clinically significant for patients. Periodontal indices (PI, GI, BOP) revealed statistically significant higher values around restorations. According to FDI criteria, therapy was considered successful and 86.7% of FPDs were acceptable. Computerized occlusal analysis demonstrated no significant difference in the number of occlusal contacts and the intensity of relative forces between restoration and control side. Results of OHIP-49 questionnaire showed improved patient’s quality of life. Complications appeared in a limited number of cases. Conclusion: PEEK based FPDs could be indicated for successful prosthodontic rehabilitation in patients with parafunctional activities, but further material improvements and long-term clinical trials should be conducted to analyze performance of this material in challenging cases, for patients with CMD and in implant therapy.

As entrepreneurial interest is believed to represent a causal factor increasing entrepreneurship, research has begun to explore how family systems affect youth entrepreneurial interests. In the present study, we attempt to identify different types of family influence on the entrepreneurial interests of young people. A questionnaire was used to obtain data from 1,633 Spanish youths, who were 15 to 18 years old, and another questionnaire was used to obtain data from 839 parents. Principal Component Analysis identified unique family types and revealed that they have differential associations to entrepreneurial interest among youths. These findings reaffirm the influence of family on the entrepreneurial ecosystem and the promotion of an entrepreneurial family cuture. This study further suggests that early attention should focus on the detection of entrepreneurial interest among youths so that actions can be implemented in the families of low-interest youths to incentivize an entrepreneurial family culture.

A solid structure, such as a road, building wall or envelop, used as a solar collector is considered an effective and new way to use renewable energy. This paper focused on the temperature characteristics of four structures exposed to sunshine: asphalt, red brick, composite cement and concrete road slab. Furthermore, the collected heat based on a hydraulic system was investigated experimentally. For the four structure slabs, their temperature differences are due to solar radiation absorption varied greatly by the material’s heat absorptance and color. Through the test, asphalt slab attained the highest temperature and had the weakest reflection among the structures. Compared with the others, the temperature of the asphalt slab was greater by 8.1%, 14.9% and 16.4% than the brick, composite cement and concrete, respectively. The reflection intensity growth ratio was defined and indicates the growth potential for absorbing radiation in the solid slab surface. From the experiments, it was concluded that a suitable selection of road materials can greatly improve the thermal absorption, conduction and penetration into the solid slab. The collected heat capability was approximately 250 W/m2 to 350 W/m2 in the natural summer condition. A black coating or a surface modification can collect more heat, reaching greater than 250 W/m2. The solar collecting heat efficiency with a surface configuration of the road slab can reach above 30% in the summer time.

This study describes an eco-friendly synthesis of ZnO nanoparticles using aqueous oat extract. The advanced electrochemical and optical features of green synthesized ZnONPs displayed excellent antibacterial activity, and exhibited an important role in pharmaceutical determinations. The formation of nanoscale ZnO was confirmed using various spectroscopic and microscopic investigations. The formed nanoparticles were found to be around 100 nm. The as-prepared ZnONPs were monitored for their antibacterial potential against different bacterial strains. The inhibition zones for ZnONPs were found as E. coli (16 mm), P. aeruginosa (17 mm), S. aureus (12 mm) and B. subtilis (11 mm) using 30 µg mL-1 sample concentration. Also, ZnONPs exhibited significant antioxidant effects 58 to 67 % with an average IC50 value of 0.88 ± 0.03 scavenging activity and 53 to 71 % (IC50 value 0.73 ± 0.05) against the DPPH and ABTS scavenging free radicals, respectively. The photocatalytic potential of ZnONPs for Rhodamine B dye degradation dye under UV irradiation was performed. The photodegradation process was carried out as a function of time-dependent and the complete degradation (nearly 98 %) with color removal after 120 min. Conclusively, the synthesized ZnONPs using oat biomass might provide a great promise in the future for biomedical applications.

Scientific and technological advances in telecommunications and onboard electronics, and advances in sustainability standards, dictated major changes to various industrial sectors, including the automotive industry, where hard and soft approaches to manufacturing are vying for market dominance. This work presents a prospective analysis of the autonomous vehicle (AV) market, analyzing three of the main US AV technology firms, Tesla, Waymo and Apple. Their designs and solutions are compared, and prospective scenarios were constructed based on an analysis of their strengths, weaknesses, opportunities and threats (SWOT). The results suggest that Tesla currently exhibits the greatest market leadership in the group studied. However, it was concluded that in the medium term, Waymo would surpass Tesla and assume market leadership. In the long run, it was concluded that Apple will overcome its rivals and dominate this market.

Human eye is a specialized organ with complex anatomy and physiology, because it is characterized by different cell types with specific physiological functions. Given the complexity of the eye, ocular tissues are finely organized and orchestrated. In the last few years many in vitro models have been developed, in order to meet the 3Rs principle (Replacement, Reduction and Refinement) for eye toxicity testing. This procedure is highly necessary to ensure that the risks associated with ophthalmic products meet appropriate safety criteria. In vitro preclinical testing is now a well-established practice of significant importance for evaluating the efficacy and safety of cosmetic, pharmaceutical, and nutraceutical products. Along with in vitro testing, also computational procedures, herein described, for evaluating the pharmacological profile of potential ocular drug candidates including their toxicity, are in rapid expansion. In this review the ocular cell types and functionality are described providing an overview about the scientific challenge for the development of three-dimensional in vitro models.

Objective: The aim of study is to find key genes and enriched pathways associated with lung cancer. Participants and Methods: Differentially expressed genes (DEGs) data of 54674 genes based on stage, tumor and status of lung cancer was taken from 66 patients of African American (AAs) origin. 2392 DEGs were found based on stage, 13502 DEGs were found based on tumor, 2927 DEGs were found based on status having p value (p<0.05). Results: Total 33 common DEGs were found from stage, tumor and status of lung cancer. Gene ontology (GO) and KEGG pathway enrichment analysis was performed and 49 significant pathways were obtained, out of which 10 pathways were found to be exclusively involved in lung cancer development. Protein-protein interaction (PPI) network analysis found 69 nodes and 324 edges and identified 10 hub genes based on their highest degrees. Module analysis of PPI found that ‘Viral carcinogenesis’, ‘pathways in cancer’, ‘notch signaling pathway’, ‘AMPK signaling pathways’ had a close association with lung cancer. Conclusion: These identified DEGs regulate other genes which play important role in growth of lung cancer. The key genes and enriched pathways identified can thus help in better identification and prediction of lung cancer.

Global differences in changes in the numbers of population-adjusted daily test-positive cases (NPDP) and deaths (NPDD) by COVID-19 were analyzed for 49 countries, including developed and developing countries. The changes as a proportion of national population were compared, adjusting by the beginning of test-positive cases increase (BPI) or deaths increase (BDI). Remarkable regional differences of more than 100 fold in NPDP and NPDD were observed. The trajectories of NPDD after BDI increased exponentially within 20 days in most countries. Machine learning analysis suggested that NPDD on 30 days after BDI was the highest in developed Western countries (1180 persons per hundred million), followed by countries in the Middle East (128), Latin America (97), and Asia (7). Furthermore, in Western countries with positive rates of the PCR test of less than 7.0%, the increase in NPDP was slowing-down two weeks after BPI, and subsequent NPDD was only 15% compared with those with higher positive rates, which suggested that the situation of testing might have affected the velocity of COVID-19 spread. The causes behind remarkable differences between regions possibly include genetic factors of inhabitants because distributions of the race and of the observed infection increasing rates were in good agreement globally.

During the recent decades, global warming by greenhouse gas evolution has attracted worldwide attention and evermore strict regulations thereon have become institutionalized as international policies. In the process, more environment-friendly power generation technologies have been developed utilizing fossil fuels with a view to timely commercialization. As one of such “clean coal” technology, Integrated Gasification Fuel Cell system is a promising power generation means and COS hydrolysis reactor is installed downstream of coal syngas to remove acidic gas constituents such as H2S and COS. The most significant design parameters affecting performance of the COS hydrolysis reactor were selected to be GHSV, (catalytic) reaction temperature, and length ratio and numerical modeling was performed considering heat and fluid flow transfer as well as chemical reaction kinetics. Effect of the selected design parameters on the variation of conversion rate and reactant gas mixture concentration were comprehensively investigated to predict performance of COS hydrolysis reactor. Stochastic modeling of reactor performance was finally performed using Monte Carlo simulation and linear regression fitting.

Background: With the rapid improvement of the healthcare technologies, security and privacy of the most sensitive data, health data is at risk. Protecting patient privacy has many components, even though the data are in electronic format. Although patient privacy is thoroughly discussed in the literature, there is no study presenting all components of patient privacy protection. Methods: In this study, a complete evaluation framework is presented, as an evaluation tool, an inventory is developed, reliability and validity of the inventory is examined. Study is conducted in three phases: Conceptual framework development, inventory development and an evaluation case study. In the evaluation, fuzzy conjoint analysis is employed to handle the subjectivity and am-biguity. As the result of the evaluation, the institution is given a patient privacy protection ma-turity grade, between 1 and 5, where 1 is the worst and 5 is the best grade. Results: In the case study, XXX’s biggest hospital, employing 800 nurses, is evaluated. Half of the nurses, 400, have partici-pated in the study. Literature tells healthcare institutions do not invest enough to protect patient privacy, and the study results support this finding. Institution’s maturity grade resulted as level 2, which is bad. Conclusion: The study measures he privacy maturity with many evaluation com-ponents. Result of the evaluation explains to the patients and the public if their data is safe or not. With this maturity grade, patients have an idea about which institution to choose, public can conclude the institutions’ reliability in terms of patient privacy.

Fault diagnosis of the gearbox is a decisive part of the modern industry to find the many gearbox defects like gear tooth crack, chipped or broken, etc. But sometimes, the nonstationary properties of vibration signal and low energy of minimal faults make this procedure very challenging. Previously, many types of techniques have been developed for gearbox condition monitoring. But most of the methods are dealing with conventional techniques of the gearbox condition monitoring, such as time-domain analysis or frequency domain analysis. Most of the conventional methods are not suitable for the nonstationary vibration signal. Thus, this paper presents a novel gearbox fault diagnosis technique using conditional temporal moments and an optimizable support vector machine (SVM). This work also presents an integrated features extraction technique based on the standard features, i.e., statistical and spectral features with the combinations of moment features. The impact of the four conditional temporal moments of each gearbox condition is also presented. This work shows that the proposed method successfully classifies and categorizes the gearbox faults at an early stage.

We have developed additive logistic models for the occurrence of lightning, large (≥ 2 cm), and very large (≥ 5 cm) hail to investigate the evolution of these hazards in the past, in the future, and for forecasting applications. The models, trained with lightning observations, hail reports, and predictors from atmospheric reanalysis, assign an hourly probability to any location and time on a 0.25° × 0.25° × 1-hourly grid as a function of reanalysis-derived predictor parameters, selected following an ingredients- based approach. The resulting hail models outperform the Significant Hail Parameter and the simulated climatological spatial distributions and annual cycles of lightning and hail are consistent with observations from storm report databases, radar, and lightning detection data. As a corollary result, CAPE released above the -10°C isotherm was found to be a more universally skilful predictor for large hail than CAPE. In the period 1950–2021, the models applied to the ERA5 reanalysis indicate significant increases of lightning and hail across most of Europe, primarily due to rising low-level moisture. The strongest modelled hail increases occur in northern Italy with increasing rapidity after 2010. Here, very large hail has become 3 times more likely than it was in the 1950s. Across North America trends are comparatively small, apart from isolated significant increases in the direct lee of the Rocky Mountains and across the Canadian Plains. In the southern Plains, a period of enhanced storm activity occurred in the 1980s and 1990s.

This study investigated and classified typical structures in rural village and analyzed the vulnerability of various typical types of structures. Based on the statistics of earthquake damages with magnitudes above 5 from 1996 to 2013 in China, the damage matrixes of different types of structures in rural village are obtained. And The vulnerability index and the vulnerability equation of structure are crucial to assess the earthquake losses of typical structures under different magnitudes earthquakes. According to the seismic loss of different types of structures under different earthquake magnitudes, there are possible to improve the seismic resilience of the buildings in rural village. Moreover, the regional vulnerability is analyzed by β probability distribution function, and the comprehensive seismic performance index of different types of agricultural buildings in the region is obtained. The main research is to predict the loss of different types of structures under different earthquake magnitudes in the future, and to provide technical support for different types of building in rural village reinforcement.

Recent prolonged dry periods and lack of irrigation water have severely affected the productivity of coffee farms’ in the Central Highlands of Vietnam. This paper analyzes the efficiency of irrigation water use for Robusta coffee (Coffea canephora) in Lam Dong province, Highlands, Vietnam. A Cobb-Douglas production function was used to determine coffee productivity’s response to the application of irrigation water and other production factors using data collected from 194 farmers while the Technical Efficiency (TE) and Irrigation Water Use Efficiency (IWUE) were analyzed using a Data Envelopment Analysis (DEA) model. The correlation of different factors to IWUE was determined using the Tobit model. The production function analysis using Cobb-Douglas shows that the volume of irrigation water, amount of working capital, labor and farm size significantly influence coffee productivity. It also shows that indigenous farmers are more efficient in utilizing irrigation water than the (mostly Kinh) migrant farmers. The Tobit result, on the other hand. indicates that farmers’ experience, education level, distance of farm to water source, security of access to water source, extension contact and credit access significantly affect IWUE. The study findings further suggest that mitigating water shortages in coffee farms require sub-regional and national policy support such as better access to credit and extension services, training, land management and household-level effort to improve farming practices, through the application of appropriate technologies and traditional knowledge.

It has often been said that Gaelic football (GF) and Australian Rules Football (ARF) are similar sports, and amateur Gaelic footballers have been recruited to play in the professional Australian Football League. Using Wilcoxons’s test, the significant (p < 0.05) factors differentiating winning and losing performance in ARF were indirectly compared to the differentiating factors between winning and losing in GF. Of the 15 key performance indicators (KPIs) that were seen to be significant between ARF teams, six (40 %) were also seen to be significant in discriminating between winning and losing GF teams. A Mann-Whitney U test was then completed to directly compare ARF and GF, twenty-six of the 30 KPIs were seen to be significantly different to each other (p < 0.05). The findings suggest that recruiting a Gaelic footballer based on the belief that their Gaelic background will aid their transition to ARF is not an appropriate decision. Instead, staying with more traditional methods of talent identification – for example anthropometric measurements – is advocated until further research is undertaken in this area.

This article presents the suitability analysis of onshore wind farm sites using an AHP (Hierarchical Analysis Process) multi-criteria decision-making (MCDM) approach based on geographic information system modeling (GIS). This analysis is based on different technical aspects (wind speed, elevation, slope), economic (proximity to the electricity network, proximity to roads) and social (proximity to residential areas, population density). The introduction of the factor “population density” and the constraint “required area” rarely used in previous studies makes it possible to refine the results. The data is classified into several categories in a gradual manner, namely unsuitable (0); less suitable (1); suitable (2); highly suitable (3) and most suitable (4). The results of the study show us that the largest percentage relating to spatial coverage is observed in the “suitable” range with 50.89% and the corresponding TWPP (theoretical wind power potential) is 1377.926 GW; it is followed by “highly suitable” 5.67% and a TWPP of 153.652 GW; then “less suitable” with 1.04% and a TWPP of 28.163GW. No areas were identified in “most suitable” and finally the “unsuitable” range with 42.4%. The results show that the most favorable areas are located in the part of the far north of the country corresponding to the strong wind zone. The sensitivity analysis was carried out based on scenario tests (technical, economic and equal-weight) in order to give greater visibility of choice to the different stakeholders who would like to invest in onshore wind energy in Cameroon. This study is useful in more than one way because the results obtained can help investors, the government and other stakeholders to identify potential areas for the deployment of wind energy systems in Cameroon. Furthermore, a study of hybrid solar-wind systems could improve the efficiency of electricity parks in Cameroon.

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data was uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below most of the comparable system. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.

This paper proposes an analytical model of the double-stator spoke-type permanent-magnet vernier machine (DSSTVM) using the subdomain model (SDM), which can be used to calculate the mag-netic field distribution and corresponding electromagnetic parameters of the DSSTVM. The whole field domain is divided into several subdomains according to the magnetic characteristics of each region, within which Laplace's and Poisson's equations are solved accordingly in terms of magnetic vector potential (MVP). Then, the magnetic flux density distribution, back electromotive force (EMF), and electromagnetic torque of the DSSTVM can be obtained correspondingly. Ultimately, finite element analysis (FEA) is adopted to validate the proposed analytical model's effectiveness for quickly predicting the no-load and on-load performances of the DSSTVM.

In this paper, a new design of a helium-assisted hybrid drone is proposed for flight time enhancement. As is widely known, most of the drones with a VTOL (vertical take-off and landing) feature have a short operation time, limiting their capability to carry out sustainable operations for the given missions. Thus, with the clear goal of enhancing the flight time, this study aims to develop a hybrid drone system, where a helium balloon is used to provide a lifting force for this purpose. The proposed design for the hybrid drone has several benefits including easiness to manufacture and relatively small size when compared to other types of hybrid drones. Various analyses are conducted for the design of the hybrid drone system including the balloon shape and size, buoyant force, flight time, and connector design. Since stability and performance are one of the most important issues for the new design, the pole location analysis is conducted based on the control theory. This rigorous analysis provides that the proposed hybrid drone design is stable as well as robust against swinging motions. To validate the effectiveness of the proposed design and flight time enhancement, simulations were conducted and experimental results are also provided using the manufactured hybrid drone system. Through the real experiments, it is proved that the hybrid drone can increase the flight time more than 2.5 times while guaranteeing stable motions.

This study presents a dynamic model of a passenger vehicle and analyzes its dynamic characteristics and responses. A dynamic vehicle model with seven degrees of freedom was established to analyze the behavior of a driving vehicle. The vehicle model had three degrees of freedom for the motion of the sprung mass and four degrees of freedom for the unsprung masses. For this model, the equations of motion were derived using Lagrange’s equation. To verify the model, the suspension deformations computed using the model were compared with the deformations measured through three actual vehicle driving tests which are the slalom, double lane change, and step steer tests. Furthermore, we investigated the effects of suspension stiffness, suspension damping, and anti-roll bar torsional stiffness on the dynamic characteristics and responses of the vehicle model. The developed dynamic vehicle model may help vehicle designers predict the dynamic responses of a vehicle through simulation without performing a driving test.

The drug delivery systems receive more and more attention for the control and treatment of many diseases due to their increased number in recent years. The main aims of this study were to develop and to characterize a polyurethane drug delivery system that it is intended to be used for the transmembrane transport of nucleosides. Three different samples have been synthesized based on aliphatic diisocyanates (hexamethylene diisocyanate, isophorone diisocyanate and lysine diisocyanate) as organic phase, respectively a mixture of polyethylene glycol, polycaprolactone diol and low molecular weight diols as aqueous phase. The samples were characterized by pH, refractivity index and solubility measurements, drug loading efficacy and release investigations, thermal behavior, Zetasizer, cells viability and irritation tests on mice skin. The results indicate the obtaining of different populations of neutral acido-basic particles with size between 132 and 190 nm; the Zeta potential values and SEM images show a medium tendency to form clusters, while UV-Vis evaluations indicate an almost 70% encapsulation efficacy, a prolonged release (45% of encapsulated substance is released in the first 5 days) and around 70% of particles have penetrated an artificial membrane in the first 24h. The synthesized products can be tested in further clinical trials, the currently tests on cells culture and mice skin revealing no side effects.

Coincident changes in abundance and behavior pose a challenge for interpreting abundance data from monitoring programs. In the San Francisco Estuary, long-term monitoring documented the declines of many species including the anadromous Longfin Smelt (Spirinchus thaleichthys). We identified seasonal patterns in reginal presence of Longfin Smelt through its life cycle using monitoring data and generalized additive modelling. We then investigated the year-to-year variability in the seasonal patterns of presence using functional data analysis (FDA). FDA separated the variability due to population size from variability due to differences in timing of presence. We found that Longfin Smelt have consistent seasonal distribution patterns and that two trawl types were needed to accurately describe those patterns. After accounting for variability due to year-class strength, shifts in the timing of presence were evident in three regions. The most variable period for the upstream regions Suisun Bay and West Delta was for age-0 fish in summer and for the downstream region Central Bay was for age-0 fish in late fall. This manifested as a delay in the typical fall re-occupation of upstream regions that comprise the study area for another monitoring study (Fall Midwater Trawl). Thus, a portion of the recent reductions in Fall Midwater Trawl abundance of Longfin Smelt resulted from changes in behavior rather than a decline in abundance. The presence of multiple monitoring surveys allowed analysis of distribution from one data set to aid interpretation of patterns in abundance from another monitoring survey. This study highlights how identifying portions of the life cycle with the most and least variability in distribution can help inform the types of management strategies that will be most effective. It also illustrates an analytical method that can be used to address the problem of confounded effects of abundance and behavior on patterns in monitoring data.

Badminton tactics refer to the techniques and strategies employed by players to win a match. Analyzing these tactics can help players improve their performance and outsmart their opponents. To study the tactics of top players, we use a gaming tree to analyze matches between two of the most powerful badminton players in history: Lin and Lee. By employing the Nash Equilibrium, we can discover the most beneficial strategies for both players, which reflect their most powerful techniques. Additionally, with the help of this gaming tree, we can precisely predict how players will implement their tactics. Empirical experimental results demonstrate that our proposed method not only evaluates and identifies each player's weaknesses and strengths but also has powerful capabilities to predict their tactics.

BACKGROUND: The National Lung Screening Trial (ILST) and the NELSON study showed that lung cancer-specific mortality can be reduced by 20-24% using low-dose computed tomography (LDCT) screening in high-risk populations, due to an increase in early-stage diagnoses. RESEARCH QUESTION: How much lung cancer-related direct costs may be reduced using a LDCT screening programme based on the ILST protocol in a public healthcare system?STUDY DESIGN AND METHODS: Cost analysis of lung cancer screening (LCS) vs. usual care in the framework of the retail price of the Catalan public healthcare system. The LCS group included costs of screening (ILST protocol), treatment cost according to weighted average distribution of TNM staging in the NLST and NELSON trials, lung cancer detection rate (CDR) and smoking cessation intervention. The usual care group included treatment costs based on distribution of TNM staging registered in the Spanish index hospital. RESULTS: In the usual care group, treatment costs were €91,959. In the 5-year duration of the LCS programme, the average expected costs per subject were €1,342 (range €1,054-1,832 depending on malignancy in detected nodules) for screening and €32,431 for treatment, with an expected reduction of €952 based on an average CDR of 1.6%. The decrease in cost resulting from stage shift offsets 70.6% of the costs of the screening programme. INTERPRETATION: Baseline 5-year LCS costs are low according to the ILST protocol. In the Catalan public healthcare system, the expense reduction from the stage shift due to the LCS programme compensates a substantial part of its costs.

The purpose of this study was to develop a mathematical model for non-contacting face seals to analyze how their performance is affected by thermoelastic phenomena. The model was used to solve thermal conductivity and thermoelasticity problems. The primary goal was to calculate the values of thermal deformations of the sealing rings in a non-contacting face seal with a flexibly mounted rotor (FMR) for a turbomachine. The model assumes conversion of mechanical energy into heat in the fluid film. The heat flux generated in the fluid film is transferred first to the sealing rings and then to the fluid surrounding them. An asymmetric distribution of temperature within the sealing rings leads to the occurrence of thermal stresses and, consequently, a change in the rings geometry. The model is solved analytically. The distributions of temperature fields for the sealing rings in the cross-sections are calculated using the Fourier-Bessel series as a superficial function of two variables (r,z). The thermoelasticity problems described by the Navier equations are solved by applying the Boussinesq harmonic functions and Goodier’s thermoelastic displacement potential function. The proposed method involves solving various theoretical and practical problems of thermoelasticity in FMR-type non-contacting face seals. The calculated thermal deformations of the sealing rings are used to determine the most important seal performance parameters such as the leakage rate and power loss.

Topological Data Analysis (TDA) refers to a collection of methods that find the structure of shapes in data. Although recently, TDA methods have been used in many areas of data mining, it has not been widely applied to text mining tasks. In most text processing algorithms, the order in which different entities appear or co-appear is being lost. Assuming these lost orders are informative features of the data, TDA may play a significant role in the resulted gap on text processing state of the art. Once provided, the topology of different entities through a textural document may reveal some additive information regarding the document that is not reflected in any other features from traditional text processing methods. In this paper, we introduce a novel approach that hires TDA in text processing in order to capture and use the topology of different same-type entities in textural documents. First, we will show how to extract some topological signatures in the text using persistent homology-i.e., a TDA tool that captures topological signature of data cloud. Then we will show how to utilize these signatures for text classification.

Medical tests' cost-effectiveness analysis reveals a strong correlation between their accuracy and analysis results. Theoretically, we provide two lower bounds that medical tests' accuracy must exceed to be considered cost-effective. Theoretically, we provide two lower bounds that the accuracy of medical tests meeting cost-effectiveness must exceed. First, medical tests' accuracy should surpass the lower bound of matching requirements for each patient to receive the most appropriate intervention based on their test results. Second, the overall lower limit should be exceeded for the medical testing to be cost-effective for the patients as a whole. Furthermore, the conclusions from this model also provide an economic explanation for the under-diagnosis of rare diseases and the challenges faced in implementing precision medicine for minority patients. A numerical simulation example validates the conclusions of this paper.

Animal behavior analysis is a crucial tasks for the industrial farming. In an indoor farm setting, extracting Key joints of animal is essential for tracking the animal for longer period of time. In this paper, we proposed a deep network that exploit transfer learning to trained the network for the pig skeleton extraction in an end to end fashion. The backbone of the architecture is based on hourglass stacked dense-net. In order to train the network, key frames are selected from the test data using K-mean sampler. In total, 9 Keypoints are annotated that gives a brief detailed behavior analysis in the farm setting. Extensive experiments are conducted and the quantitative results show that the network has the potential of increasing the tracking performance by a substantial margin.

Soil is an extremely significant resource for human survival, and agglomerates, as the basic unit of soil structure, not only enhance soil fertility and control the biological validity of nutrients, but also strengthen the soil's erosion resistance. The mass application of fertilizers may have a significant impact on crop growth and soil structure, and the rational application and dispensing of fertilizers will be an urgent issue to be addressed. Therefore, the effect of fertilizer application on the stability of water-stable soil aggregates needs to be studied under different meteorological and soil conditions to draw more general and feasible conclusions. Our Meta-analysis of data from 220 of 56 published studies found that fertilizer application increased mean weight diameter (MWD) by an average of 18% compared to the no-fertilizer treatment. Among the nitrogen (N), phosphate (P), and organic (OM) fertilizer treatments, the organic fertilizer treatment had a greater stimulatory effect on MWD (26%). Among the different fertilizer levels, low level of phosphorus (<40kg·hm-2·yr-1), high level of N (>120kg·hm-2·yr-1), and low level of organic fertilizer (<5000kg·hm-2·yr-1) increased MWD by 19%, 14%, and 41% respectively. Across soil types and land use types, the response to MWD was positive for red soils and paddy fields, and the stimulatory effect of organic fertilizer was more significant compared to chemical fertilizer. The regression model showed that the response ratio of MWD was negatively correlated with the response ratio of soil pH and bulk density (BD), and positively correlated with the response ratio of soil organic carbon (SOC) and microbial mass carbon (MBC). Meanwhile, the PLS-SEM model showed that average annual temperature was the main factor affecting the stability of soil aggregates, and the average annual rainfall is the secondary factor. Therefore, this study found that the long-term use of organic fertilizers in place of some chemical fertilizers was more effective than chemical fertilizer alone. Temperature and rainfall have greater effects on the response of fertilizer to soil aggregate stability.

Graphyne is a material that has unique mechanical properties, but little is known about how these properties change when the material has holes. In this work, the effect of hole geometry, considering circular, triangle, and rhombus hole configurations, on the mechanical nonlinear response of γ-graphyne structures is studied. Graphyne, graphdiyne, graphyne-3, and graphyne-4 structures are under investigation. An efficient nonlinear finite element analysis (FEA) method is adequately implemented under large deformations for this purpose. The study varied the size and shape of the holes to understand how these changes affect the nanostructure's mechanical response. The results indicate that the hole geometry significantly impacts the mechanical nonlinear response of γ-graphyne structures. The holes' size and shape affect the structures' elastic behavior, deformation, and strength. The findings can be used to optimize the design of γ-graphyne structures for specific mechanical applications. The study highlights the importance of considering the hole geometries in the design and fabrication of these materials.

The ends of linear chromosomes of most eukaryotes consist of protein-bound DNA arrays called telomeres, which play essential roles in protecting genome integrity. Despite general evolutionary conservation in function, telomeric DNA is known to drastically vary in length and sequence between different eukaryotic lineages. Bryophytes are a group of early diverging land plants that include mosses, liverworts, and hornworts. This group of ancient land plants recently emerged as a new model for important discoveries in genomics and evolutionary biology, as well as for understanding plant adaptations to a terrestrial lifestyle. We measured telomere length in different ecotypes of model Bryophyte species, including Physcomitrium patens, Marchantia polymorpha, Ceratodon purpureus, and in Sphagnum isolates. Our data indicate that all analyzed moss and liverwort genotypes have relatively short telomeres. Furthermore, all analyzed ecotypes and isolates of model mosses and liverworts display evidence of substantial natural variation in telomere length. Interestingly, telomere length also differs between male and female strains of the dioecious liverwort M. polymorpha and dioecious moss C. purpureus. Given that Bryophytes are extraordinarily well-adapted to different ecological niches from polar to tropical environments, our data will contribute to understanding the impact of natural telomere length variation on evolutionary adaptations in this ancient land plant lineage.

Technologies providing copyright-infringing IPTV content are a commonly used, as an illegal alternative to legal IPTV subscriptions and services, as they usually have lower monetary costs, and can be more convenient for the users that follow content from different sources. These infringing IPTV technologies may include websites, software, software add-ons, and physical set-top boxes. Due to the free or low cost of illegal IPTV technologies, illicit IPTV content providers will often resort to intrusive advertising, scams, and the distribution of malware to increase their revenue. We developed an automated solution for collecting and analysing malware from illegal IPTV technologies and used it to analyse a sample of illicit IPTV websites, application (app) stores, and software. Our results show that our IPTV Technologies Malware Analysis Framework (IITMAF) classified 32 of the 60 sample URLs tested as malicious, compared to running the same test using publicly available online anti-virus solutions, which only detected 23 of the 60 sample URLs as malicious. Moreover, the IITMAF framework also detected malicious URLs and files from 31 of the sample’s websites, one of which had reported ransomware behaviour.

Advanced 1H Nuclear Magnetic Resonance (NMR) relaxometry and diffusometry methods and VIS-nearIR spectroscopy combined with pH, electric conductivity (EC) and totally dissolved solids (TDS) measurements were used to assess the properties of wastewater collected from a chicken slaughterhouse in each step of the treatment process (wastewater before treatment, biologically treated wastewater, chemically treated wastewater and discharged wastewater) and from sludge. The 1H NMR Carr-Purcell-Meiboom-Gill (CPMG) and Pulsed-Gradient-Stimulated-Echo (PGSE) decay curves recorded for all samples of wastewater were analyzed by inverse Laplace transform (ILT) to obtain the distributions of transverse relaxation times T2 and diffusion coefficient D. The VIS-nearIR total absorbance, T2-values, D-values, pH, EC and TDS parameters were used for statistical analysis in principal component analysis (PCA). The 1H T2-distributions measured for the slaughterhouse wastewater lie in two main regions reflecting the amount of dissolved solids or the distribution of undissolved solids mobility. The PCA analysis successfully differentiates between polluted and less polluted types of wastewater and sludge. The wastewater treatment applied by the slaughterhouse is efficient. The recommended methods for wastewater monitoring are the NMR T2- and D-distributions and EC, TDS and NMR-D diffusion coefficient. Finally, Machine Learning AI-algorithms are used to provide prediction maps of wastewater treatment stage.

Global pandemic COVID-19 is in stage 4 of widespread local transmission in Bangladesh- the country which did not have a noteworthy health policy and legislative structures to combat COVID-19 like a pandemic. Early strategic planning and groundwork for evolving and established challenges are crucial to assemble resources and react in an appropriate timely manner. This article, therefore, focuses on the public perception of comparative lockdown scenario analysis and how they may affect the sustainable development and the strategic management regime of COVID-19 pandemic in Bangladesh socioeconomically. Response from 159 respondents was collected via a purposive sampling survey method through a questionnaire, and 54 statements were collected for scenario analysis. Datasets were analyzed through a set of statistical techniques including Principal Component Analysis (PCA), hierarchical Cluster Analysis (CA), Pearson’s correlation matrix (PCM), Linear regression analysis (LRA), and psychometric characteristics were included in the Classical Test Theory (CTT) analysis. There were good associations among the lockdown scenarios and response strategies to be formulated. A strong significant positive relationship was observed between people who will start moving towards regular life and the formal and informal economic activities will be started in lockdown scenario 1(r=0.671, p<0.01). The scenario one describes how the death and infection rate will increase if Govt withdraw the partial lockdown before 40 to 50 days. Scenario 2 outlines people’s movement will enable low-level community transmission of COVID-19 with the infection and death rate will increase slowly (r=0.540, p<0.01). Moreover, there will be less supply of necessities of daily use with a price hike (r= 0.680, p<0.01). Scenario 3, full lock down will reduce the community transmission and death from COVID-19 (r=0.545, p<0.01). Moreover, along with the other problems gender discrimination and gender-based violence will increase rapidly (r=0.661, p<0.01). Form regression analysis, due to full lockdown, the formal and informal business, economy and education sector will be hampered severely (R=0.695), there was a strong association between the loss of livelihood and unemployment rate will increase due to business shutdown (p<0.01) and poor communities both in urban and rural areas will be affected severely (p<0.01).All these will further aggravate the humanitarian needs of the most vulnerable groups in the country in the coming months to be followed which needs to be dealt with proper response plans. It will undoubtedly affect the targets of global sustainable development goals (SDGs) of 2030 and all other development targets.

The city of Barcelona, ​​like other cities in the world, suffers strong internal socio-economic inequalities. Numerous works have sought to detect, quantify, characterize and / or map existing intra-urban differences, almost always based on quantitative methodologies. With this contribution, we intend to illuminate the complementary role that qualitative methodologies can play in studies on urban socio-environmental vulnerability. We consider aspects that are not quantifiable but that may be inherent to many such vulnerable spaces, both in the constructed environment and in the social ambit. These questions are considered through selected neighborhoods of Barcelona which have been shown (in prior works, mainly studies of quantitative manufacturing) to possess elements of vulnerability including a high presence of immigrants from less-developed countries, low per capita income, aging populations, or low educational levels. The results reveal the multidimensionality of vulnerability in the neighborhoods analyzed, as well as the essential complementarity among methodologies that detect and support possible public actions aimed at reducing or eliminating intra-urban inequalities.

Cocaine addiction is a global health problem that causes substantial damage to the health of addicted individuals around the world. Dopamine synthesizing neurons in the brain play a vital role in the addiction to cocaine. But the underlying molecular mechanisms that help cocaine exert its addictive effect have not been very well understood. Bioinformatics can be a useful tool in the attempt to broaden our understanding in this area. In the present study, Gene Set Enrichment Analysis (GSEA) was carried out on the upregulated genes from a dataset of Dopamine synthesizing neurons of post-mortem human brain of cocaine addicts. As a result of this analysis, 3 miRNAs have been identified as having significant influence on transcription of the upregulated genes. These 3 miRNAs hold therapeutic potential for the treatment of cocaine addiction.

This paper establishes a prognosis of the long term environmental impact of various car subsidy concepts. The CO2 emissions of the German car fleet impacted by the purchase subsidies are determined. A balance model of the CO2 emissions of the whole car life cycle is developed. Consideration of production-, use- and End-of-Life processes are taken into account. The implementation of different subsidy scenarios directly affects the forecasted composition of the vehicle population and therefore the resulting life cycle assessment. All scenarios compensate the additional emissions required by the production pull-in within the considered period and hence reduce the accumulated CO2 emissions until 2030. The exclusive funding of BEVs is most effective with a break-even in 2025.

The COVID-19 epidemic is an emerging infectious disease of the viral zoonosis type caused by the corona-virus strain SARS-CoV-2, is classified as a human-to-human communicable disease and is currently a pandemic worldwide. In this paper, we propose conceptual mathematical models the epidemic dynamics of four compartments. We have collected data from the Djibouti health ministry. We define the positivity, boundedness of solutions and basic reproduction number. Then, we study local and global stability and bifurcation analysis of equilibrium to examine its epidemiological relevance. Finally, we analyze the fit of the data in comparison with the result of our mathematical results, to validate the model and estimating the important model parameters and prediction about the disease, we consider the real cases of Djibouti from 23th March to 10th June, 2020.

: This study presents energy and exergy analyses conducted on a Gifford-McMahon cycle refriger-ator. Through detailed numerical simulations, the performance of the refrigerator is thoroughly assessed, with a particular focus on energy and exergy losses. The obtained results are compared with Sankey and Grassman diagrams, providing visual representations of energy and exergy flows within the system. The localization of exergy losses is carefully analysed to identify key areas for optimization aimed at enhancing efficiency and power output. By pinpointing sources of ex-ergy destruction, targeted improvements can be implemented to maximize the overall perfor-mance of the refrigerator. Overall, this research contributes to a deeper understanding of the thermodynamic behaviour of Gifford-McMahon cycle refrigerators and provides valuable insights for optimizing their performance in practical applications.

In recent years, the high-frequency abnormal price fluctuations of small-scale agricultural products have been detrimental to market stability. The traditional method based on statistical data of production factors can no longer accurately predict abnormal price fluctuations. Taking scallions as a case, the paper identifies price influencing factors from massive network information, and analyzes the causes and changing characteristics of price fluctuations from four perspectives: market supply, economic environment and market sentiment, and attention. Initially, the study employs an LDA topic model to extract factors from online sentiment data over the past four years, including market supply and demand, economic conditions, related agricultural prices, and market attention. Subsequently, using the SO-PMI algorithm to expand domain-specific lexicons and employing semantic and sentiment analysis with syntactic parsing, the study aims to improve the accuracy of sentiment quantification in text analysis. Furthermore, cointegration and Granger causality tests validate the significant impact of these factors on scallion price fluctuations, mitigating spurious regression issues. Finally, by employing a TVP-VAR model, the study compares the impulse responses and shock magnitudes of various factors in different time and spatial dimensions. It also delves into the mechanisms of heterogeneous impacts and trends in scallion prices, considering the corresponding socio-economic context and public sentiment events within specific time windows, thereby providing valuable decision-making insights for the healthy development of agricultural markets.

Objectives: COVID-19 pandemic interrupted the Spanish professional football competition until May 2020, when it was restarted following a surveillance protocol established by LaLiga. The aims were to describe the infective and serological status of professional football players (PLY) and staff (STF) between May 5th 2020 until April 22nd 2021, to analyze the spatial-temporal distribution of the COVID-19 disease in this cohort and its comparison to the Spanish population. Methods: a prospective observational cohort study was carried out. Differences between PLY and STF were assessed by Chi-squared test and test of equality of proportions. Pearson correlation test was used to measure the presence of an association between the percentages of positivity in population and LaLiga cohort. Results: 137,420 RT-PCR and 20,376 IgG serology tests were performed in 7,112 professionals. Positive baseline serology was detected in 10.57% of PLY and 6.38% of STF. Among those who started the follow-up as not infected and before STF vaccination, 11.87% of PLY and 5.03% of STF became positive. Before summer 2020 the prevalence of infection was similar than the observed at national level. The percentage of positivity in the Spanish population was higher than in LaLiga cohort, but both series showed a similar decreasing trend.

This article articulates the significance of conceptual analysis (CA) methods for the postgraduate philosophy scholar at the G-CAR institution. In order to clarify the significance, first the concepts were defined, their context examined and the problems posed by the concepts looked at. The activity of scholars who are bent on researching for concepts meaning, was highlighted and the methods scholars use in determining meaning were carefully examined. Three major methods discussed by philosophers, namely, constructive, reductive and detective were explained as well as their significance in helping philosophy scholars do qualitative research in meaning determination. Meaning was found to be a discovery of the observer’s relationship to the objective reality as depicted by concepts. The common factor in these three methods was the dominant role both the observer and the human subjects played in determining meaning – a constructive engagement. It was clarified too, that the ability to explicate concepts’ meaning is the tool kit or skill sets that all scholars need to navigate the semantic jungle in the 21^st Century.

Our comprehension of fundamental cosmological principles hinges on a thorough understanding of the early universe. The Lambda Cold Dark Matter (ΛCDM) model has emerged as a valuable framework for elucidating various cosmic phenomena, uniting a cosmological constant (Λ) with cold dark matter (CDM). Nevertheless, recent observations, particularly data from the Planck, WMAP, and DES missions that probe the early universe, alongside local measurements of the Hubble constant, have unveiled significant inconsistencies. These inconsistencies have magnified the perplexing issue known as the Hubble Tension, thereby challenging our current understanding of the universe. To address this conundrum, we adopt a comprehensive approach encompassing a review of cosmological fundamentals, an exploration of the ΛCDM model, an examination of the cosmic microwave background (CMB), power spectrum, and perturbation theory, a thorough review of existing research, and finally, an investigation of real data. By undertaking these early universe processes, we aim to understand more about Hubble constant and its consequence in ΛCDM model.

Misinformation can amplify humanity's most significant challenges. As the novel coronavirus spreads across the world, concerns regarding the spreading of misinformation about it and also people downplaying the severity of it are also growing. This article investigates social media activity in May 2020, specifically Twitter, with respect to COVID-19, the themes of tweets, where the discussion is emerging from, disinformation shared about the virus, and its relationship with COVID-19 incidence rate at the state and county level. A geodatabase of all geotagged COVID-19 related tweets was compiled. Multiscale Geographically Weighted Regression was employed to examine the association between social media activity, population, and the spatial variability of disease incidence; our results suggest that MGWR could explain 96.7% of the variations. Moreover, Covid-19 related twitter dataset content analysis reveals a meaningful strong spatial relationship that exists between social media activity and known cases of COVID-19. Discourses analysis was conducted on tweets to index tweets downplaying the Pandemic or disseminating disinformation; the discourses analysis findings suggest that states in where twitter users spread more misinformation and showed more resistance to pandemic management measures in May are experiencing a surge in the number of cases in July.

Maximally similar sets (MSS) are sets of elements that share a neighborhood in a high-dimensional space defined by symmetric, reflexive similarity relation. Each element of the universe is employed as the kernel of a neighborhood of a given size (number of members), and elements are added to the neighborhood in order of similarity to the current members of the set until the desired neighborhood size is achieved. The set of neighborhoods is then reduced to the set of unique maximally similar sets by eliminating all sets that are permutations of an existing set. Subsequently, the within-MSS variability of attributes associated with the elements is compared to random sets of the same size to estimate the probability of obtaining variability as low as observed. Individual attributes can be compared for effect size by the ratio of within-MSS variability to random set variability, correcting for statistical power as necessary. The analyses performed identify constraints, as opposed to determinants, in the triangular distribution of pair-wise element similarity. In the example given here, the variability in spring temperature, summer temperature, and growing degree days of forest vegetation samples shows the greatest constraint on forest composition of a large set of candidate environmental variables

The lateral pressure generated by liquefied soil on pile is a critical parameter in the analysis of soil-pile interaction in liquefaction-susceptible sites. Previous studies have shown that liquefied sand behaves like a non-Newton fluid, and its effect on piles has rate-dependent properties. In this study, a simplified pseudo-static method for liquefiable soil-pile interaction analysis is proposed by treating the liquefied soil as a thixotropic fluid, which considers the rate-dependent behavior. The viscous shear force generated by the relative movement between the viscous fluid (whose viscosity coefficient varies with excess pore pressure and shear strain rate) and the pile was assumed to be the lateral load on the pile. The results from the simplified analysis show that the distribution of bending moment is in good agreement with experiments data. Besides, the effects of various parameters, including relative density, thickness ratio of non-liquefiable layer to liquefiable layer, and frequency of input ground motion, on the pile-soil rate-dependent interaction were discussed in detail.

Hearing impairment (HI) is a common sensory disorder that is defined as the partial or complete inability to detect sound in one or both ears. This diverse pathology is associated with a myriad of phenotypic expressions and/or syndromes. HI can be caused by various intrinsic, environmental and/or unknown factors. Some ontologies capture some relevant HI forms, phenotypes and syndromes, but there is no comprehensive knowledge portal which includes aspects specific to the HI disease state. This hampers inter-study comparability, integration and interoperability within and across disciplines. This work describes the HI Ontology (HIO) that was developed based on the Sickle Cell Disease Ontology (SCDO) model. This is a collaboratively developed resource built around the 'Hearing Impairment' concept by a group of experts in different aspects of HI and ontologies. HIO is the first comprehensive, standardized, hierarchical and logical representation of existing HI knowledge. HIO allows researchers and clinicians alike to readily access standardized HI-related knowledge in a single location and promote collaborations and HI information sharing, including epidemiological, socio-environmental, biomedical, genetic and phenotypic information. Furthermore, this ontology illustrates the adaptability of the SCDO framework for use in developing a disease-specific ontology.

Parkinson-like events are side effects that may persist for an extended period even after drug administration is discontinued. Although these events can be triggered by various drugs, the mechanisms underlying their diverse symptoms remain largely unclear. To investigate this, we used the Japanese Adverse Drug Event Reporting Database, which is maintained by the Phar-maceuticals and Medical Devices Agency, to analyze the risk factors associated with Parkinson-like events along with associated drug trends and characteristics. Our findings indicate that similar to　Parkinson’s disease, age-related differences affect the onset of these events, with older individuals being more susceptible. Hierarchical clustering and principal component analysis revealed that the mechanisms triggering these Parkinson-like events are consistent across reports, suggesting a common underlying cause. However, even with a consistent mechanism, side effects can vary depending on the site of action. These insights underline the importance of swift identification of the drugs suspected of causing these events and implementation of measures to reduce their side effects.

1960s Europe saw a large number of residential neighborhoods built to house those migrating from the countryside. Today, more than 50 years later, these neighborhoods suffer high levels of functional, social, and technical obsolescence. In response to this, University of Seville developed the Aura Strategy as an intervention methodology to find global solutions to issues in outdated neighborhoods. To provide visibility to this aspect of the Aura strategy, the retrofit proposal presented in this article provides a solution to improve the roofing of buildings in a case study neighborhood (Polígono de San Pablo, Seville) and an analysis of the results in terms of energy and financial savings for local residents. The results show that for a population of roughly 18,000 (in 2018), net savings, including energy and retrofitting costs, ranging from nearly 6.5 to over 8.6 million can be made over the 20-year life cycle span. Likewise, the results obtained on the reduction of thermal loads indicate a 72% decrease in energy consumption, equivalent to a saving of close to 4,500 tons of greenhouse gas emissions for the district and the entire life cycle time period, with the consequent benefits on the impact on air quality and and the fight against climate change. climate change.

The healthcare sector in Kuwait has been nurtured for many decades by the government where the Ministry of Health (MoH) controls up to 80 percent of the country’s health services. General and specialized public hospitals represent a significant proportion of health expenditures in Kuwait; amidst dwindling health resources, a declining global oil market in an oil-dependent welfare state, and a heavy reliance on a non-national clinical workforce. Although healthcare services in public sector hospitals are at highly subsidized rates, causing private sector involvement in healthcare to be considerably low, the growing demands for private delivery of care burgeoned participation of private hospitals in Kuwait, and improving hospital efficiency and productivity is more critical and timelier than ever. This paper aims to analyze public health system efficiency and hospital performance in the State of Kuwait, where we begin by evaluating the input-oriented technical efficiency (TE) of MoH hospitals in 2015-2019 and identifying potential areas for efficiency improvement by exploring influencing institutional and environmental factors. Over the five years between 2015 and 2019, TE in MoH hospitals has decreased by an average of 2.98% solely based on technical regress, where the six MoH general hospitals reported a pooled mean efficiency of 86.58%, and the nine sampled MoH specialized hospitals had a five-year pooled average of 65.47% efficiency. MoH policymakers should focus on improving allocative efficiency in the public health system, and healthcare policy reforms should focus on strengthening management structures in Kuwait’s public hospitals to improve production efficiency and financial sustainability.

This study’s primary goal was to conduct an analysis on the flood propensity of the Tabua (Ribeira Brava) drainage basin’s main watercourse. In addition to that, this study also recommends two different methodologies in order to mitigate flood’s impacts, namely by dimensioning a detention basin and adjusting the riverbed roughness coefficient. Regarding the study on the flood propensity, it was necessary to resort to geomorphological data, which was obtained when characterizing the watershed; that data was crucial to determine the expected peak flow rate, according to the Gumbel Distribution methodology and considering a 100 years return period, and to perform necessary tasks in the SIG ArcGIS software. Lastly, it was also analyzed the drainage capacity of this drainage basin’s river mouth, in order to conclude whether it would have the capacity to drain the total volume of rainwater if an extreme flood event was to happen. Indeed, the main results point out that this watershed’s river mouth doesn’t have the necessary drainage capacity to cope with an extreme event, for the return period that was considered. As a consequence of that, the two mitigation measures aforementioned were developed considering Tabua (Ribeira Brava) drainage basin’s specific features: the sizing of the detention basin was estimated through the Dutch Method and the Simplified Triangular Hydrograph Method, while the adjustment of the roughness coefficient was considered a valid solution to enhance the drainage capacity of this river mouth.

Nickel superalloys occupy a reasonably broad niche in the industry. One of this group's most common and well-known alloys is Inconel 718. Parts from Inconel 718 are used in aerospace, energy, automotive, and some other vital industries, which creates a demand for research and improvement of the machining conditions of this alloy. This article is devoted to the systematization and generalization of the accumulated experience of machining of Inconel 718 at turning operation. The research methodology is based on the Pareto distribution. In the study, more than 50 articles devoted to turning Inconel 718 were analyzed. The tool materials, methods, and types of coating used in turning Inconel 718 were analyzed. Finite element method modeling of processing processes has been considered. The trends in the selection of cutting parameters and the geometry of the cutting tool and finite element method modeling of processing processes have been considered. Lacuna for promising future research was formulated.

Universal Stress Protein (USP) plays an important regulatory role in the response to abiotic stress. Most of the research related to USP so far has been conducted on plant models such as Arabidopsis, rice, and cotton. The potato is one of the four major food crops in the world. Therefore, it is of great significance to study the expression pattern of the potato StUSP gene family under abiotic stress conditions. In this study, a total of 108 USP genes were identified from the genome of the Atlantic potato, divided into four subgroups. And based on the genetic structure, the physical and chemical properties of proteins and other aspects of their biological characteristics are comprehensively analyzed. Collinear analysis showed that the homologous genes of StUSPs and four other representative species (Arabidopsis, rice, coyote tobacco, and tomato) were highly conserved. The cis-regulatory elements of the StUSPs promoter are involved in plant hormones, environmental stress, mechanical damage, and light response. RNA-seq analysis showed that under different abiotic stresses, there are differences in the expression patterns of members of each subgroup. WGCNA co-expression analysis of the central gene showed that the differential co-expression gene is mainly involved in the plant-pathogen response process, plant hormone signal transduction, and the biosynthesis process of secondary metabolites. Through qRT-PCR analysis, it was confirmed that StUSP13, StUSP14, StUSP15, and StUSP41 may be important candidate genes involved in the response to adversity stress in potatoes. The results of this study provide the basis for further research on the functional analysis of StUSPs in the potato response to adversity stress.

The EigenAnt algorithm has been proposed to find the shortest path between two nodes with a proof of convergence. Two novel features are noticeable in EigenAnt compared to the conventional ant colony optimization algorithms. Pheromone evaporation in the EigenAnt algorithm is done locally for the selected path and it does not use heuristic information in its selection phase. On the other hand, a simple version of ant colony optimization that also does not use heuristic information has been used for analyzing convergence, considering parameter impact analysis, in the problem of finding the shortest path for 1-node binary chain problems. In this paper, we propose to improve the EigenAnt algorithm by adding additional parameters in such a way as to be able to take advantage of the analysis developed for the simple ant colony optimization algorithm. We demonstrate through our analysis that since the Improved EigenAnt algorithm has one parameter more than ant colony optimization algorithms, tuning of convergence speed is decoupled from tuning of convergence features in the algorithm. Thus, IEigenAnt, which can be interpreted as having two uncorrelated intensification and diversification components, makes it possible to achieve a balance between intensification and diversification. In order to illustrate the advantages of this decoupling, we present experimental results for the routing network problem.

The goal of our study was to identify signatures of selection in the Turopolje pigs and other commercial pig breeds. We conducted a comprehensive analysis of five datasets, including one local pig breed (Turopolje) and four commercial pig breeds (Large White, Landrace, Pietrain, and Duroc), using strict quality control measures. Our final dataset consisted of 485 individuals and 54,075 single nucleotide polymorphisms (SNPs). To detect selection signatures within these pig breeds, we utilized the XP-EHH and XP-nSL methodologies, which allowed us to identify candidate genes that have been subject to positive selection. Our analysis consistently highlighted the PTBP2 and DPYD genes as commonly targeted by selection across all studied breeds. Both of these genes are associated with muscular development in pigs and other species. Furthermore, in the Large White breed a number of genes were detected by the two methods, such as ATP1A1, CASQ2, CD2, IGSF3, MAB21L3, NHLH2, SLC22A15, VANGL1. In Duroc breed a different set of genes was detected, such as ARSB, BHMT, BHMT2, DMGDH, JMY. The function of these genes was related to body weight, production efficiency and meat quality, average daily gain and other similar traits. Overall, our results have identified a number of genomic regionsthat are under selective pressure between local and commercial pig breeds. This information can help to improve our understanding of the mechanisms underlying pig breeding, and ultimately contribute to the development of more efficient and sustainable pig production practices. Our study highlights the power of using multiple genomic methodologies to detect genetic signatures of selection, and provides important insights into the genetic diversity of pig breeds.

Decision makers have to work on resolving multiple forms of problems within their organizations. The problems can be structured, semi-structured, or unstructured. Handling these issues requires intensive time dedication and resource allocation. Looking at the perspective of strategic decision making in Saudi Arabia, current operations are lacking in various sectors. This indulges an immediate need to introduce proper systems and highlight the loopholes that would allow the leaders make informed decisions aided by a support system. For this reason, the researchers set out to work on two research questions. The first question focused on how decision makers ensured accuracy of the decisions they are making. It would allow to identify why such system is important that should also guarantee consistency and accuracy. The second question asked about the proper process that would govern and control the outcome of decision. Considering the above questions, this research aimed at identifying the ideal framework, and whether the proposal developed would fit into the Saudi organizations. A case study in health sector has been reviewed containing various models across the world. Following this, interviews with five decision makers in their organization were conducted to perform the qualitative research. The researcher identified that most organizations lacked systems to ensure their decisions were accurate. The research also concluded that the tools and elements presented in the proposed informed decision framework would fit into most Saudi organizations to eliminate the identified problem, and especially due to the inclusion of the non-digital sources of information as part to the decision process. Furthermore, the analysis was conducted and discussion was determined stating validations of the study.

Floods and droughts are driven, in part, by spatial patterns of extreme rainfall. Heat waves are driven by spatial patterns of extreme temperature. It is therefore of interest to design statistical methodologies that allow the identification of likely patterns of extreme rain or temperature from observed historical data. The standard work-horse for identifying patterns of climate variability in historical data is Principal Component Analysis (PCA) and its variants. But PCA optimizes for variance not spatial extremes, and so there is no particular reason why the first PCA spatial pattern should identify, or even approximate, the types of patterns that may drive these phenomena, even if the linear assumptions underlying PCA are correct. We present an alternative pattern identification algorithm that makes the same linear assumptions as PCA, but which can be used to explicitly optimize for spatial extremes. We call the method Directional Component Analysis (DCA), since it involves introducing a preferred direction, or metric, such as `sum of all points in the spatial field'. We compare the first PCA and DCA spatial patterns for US rainfall anomalies on a 6 month timescale, using the sum metric for the definition of DCA in order to focus on total rainfall anomaly over the domain, and find that they are somewhat different. The definitions of PCA and DCA result in the first PCA spatial pattern having the larger explained variance of the two patterns, while the first DCA spatial pattern, when scaled appropriately, has a higher likelihood and greater total rainfall anomaly, and indeed is the pattern with the highest total rainfall anomaly for any given likelihood. In combination these two patterns yield more insight into rainfall variability and extremes than either pattern on its own.

Migratory flows and international travel are triggering an increase in imported cases of schistosomiasis in non-endemic countries, with serology being the recommended method for screening migrants. The present study aims to evaluate, for the first time, the LAMP technique on patients’ urine samples for the diagnosis of imported schistosomiasis in a non-endemic area in comparison to a commercial immunochromatographic test and microscopic examination of feces and urine. A prospective observational study was conducted in sub-Saharan migrants attending at the Tropical Medicine Unit, Almería, Spain. For schistosomiasis diagnosis, serum samples were tested using an immunochromatographic test (Schistosoma ICT IgG-IgM). Stool and urine samples were examined by microcopy. Urine samples were evaluated by combining three LAMP assays for the specific detection of Schistosoma mansoni and S. haematobium, and an assay for the genus Schistosoma. To evaluate the diagnostic accuracy of Schistosoma-LAMP in comparison to microscopy and Schistosoma ICT IgG-IgM test, a Latent Class Analysis (LCA) was performed. A total of 115 patients were included; 21 patients (18.3%) were diagnosed with schistosomiasis confirmed by microscopy, with S. haematobium being the most frequent species identified (18/115; 15.7%). The Schistosoma ICT IgG-IgM test resulted 100% positive and Schistosoma-LAMP was 61.9% positive, reaching as high as 72.2% for S. haematobium. The sensitivity and specificity estimated by LCA, respectively, were: 92% and 76% for Schistosoma ICT IgG-IgM, 68% and 44% for Schistosoma-LAMP and, 46% and 97% for microscopy. The Schistosoma-LAMP technique presented a higher sensitivity than microscopy for the diagnosis of imported urinary schistosomiasis, which could improve the diagnosis both in referral centers and in centers with limited experience or scarce resources and infrastructure.

The aim of this article is to extend the local as well as the semi-local convergence analysis of multi-point iterative methods using center Lipschitz conditions in combination with our idea, of the restricted convergence region. It turns out that this way a finer convergence analysis for these methods is obtained than in earlier works and without additional hypotheses. Numerical examples favoring our technique over earlier ones completes this article.

Increased proliferation of IT services in all sectors has reinforced the adoption and diffusion across all levels of education and training institutions. However, lack of awareness of and knowledge about the key challenges and opportunities of elearning, seem to allude policymakers, resulting in low adoption or increased failure rate of many e-learning projects. Our study tries to address this problem through a review of relevant literature in e-learning. Our goal was to draw from the existing literature, insights into the opportunities and challenges of e-learning diffusion, and the current state-of-research in the field. To do this, we employed a systematic review of literature on some of the salient opportunities and challenges of e-learning innovation for educational institutions. These results aimed to inform policymakers and suggest some interesting issues to advance the research and adoption and diffusion of e-learning. Moreover, the bibliometric analysis shows that the field is experiencing high research attraction among scholars. However, several research areas in the field witnessed relatively low research paucity. Based on these findings, we discussed topics for possible future research.

Present study offers mathematical calculations of the roll-coating procedure lubricated with upper Convected Maxwell Fluid. An incompressible isothermal viscoelastic fluid is considered with roll and the porous web having uniform velocities. By employing Lubrication Approximation Theory, the desired equations of motion for the fluid concerned over porous web are modelled and analyzed. The suction rate on the web and injection rate at the roll surface are anticipated proportionate. Results for velocity profile and pressure gradient are obtained analytically. Fluid parameters of industrial significance i.e. detachment point, pressure, sheet /roll separating force, power contribution and coating thickness are also calculated numerically. Substantial and monotonic increase is witnessed in these quantities with the increase of flow parameters.

In an era when medical literature is increasing daily, researchers in biomedical and clinical areas have joined efforts with language engineers to analyze large amount of biomedical and molecular biology literature (such as PubMed), patient data or health records. With such a huge amount of reports, evaluating their impact has long seized to be a trivial task. In this context, this paper intends to introduce a non-scientific factor that represents an important element in the effort of gaining acceptance of claims. Thus, we postulate that the confidence the author is expressing in his work plays an important role in shaping the first impression that influences the reader’s perception of the paper. The results discussed in this paper are based on a series of experiments ran over data from the Open Archives Initiative (OAI) corpus that provides interoperability standards in order to facilitate the effectiveness dissemination of the content. This method can be useful to the direct beneficiaries (authors, who are engaged in medical or academic research), but, also, researchers in the fields of BioNLP and NLP, etc.

Background: Antiplatelet medications such as aspirin and clopidogrel are used following thrombotic stroke or transient ischemic attack (TIA) to prevent a recurrent stroke. However, the antiplatelet treatments fail frequently, and patients experience recurrent stroke. One approach to lower the rates of recurrence, may be the individualized antiplatelet therapies (antiplatelet therapy modification (ATM)) based on the results of platelet function analysis (PFA). This review was undertaken to gather and analyse the evidence about the effectiveness of such approaches. Methods: We searched Medline, CINAHL, Embase, Web of Science and Cochrane databases to 7 January 2020. Results: Two observational studies involving 1136 patients were included. The overall effects of PFA-based ATM on recurrent strokes (OR 1.05; 95% CI 0.69 to 1.58), any bleeding risk (OR 1.39; 95% CI 0.92 to 2.10) or death hazard from any cause (OR 1.19; 95% CI 0.62 to 2.29) were not significantly different from the standard antiplatelet therapy without ATM. Conclusions: The two studies showed opposite effects of PFA-guided ATM on the recurrent strokes in aspirin non-responders, leading to an insignificant difference in the subgroup meta-analysis (OR 1.59; 95% CI 0.07 to 33.77), while the rates of any bleeding events (OR 1.04; 95% CI 0.49 to 2.17) or death from any cause (OR 1.17; 95% CI 0.41 to 3.35) were not significantly different between aspirin non-responders with ATM and those without ATM. There is a need for large randomized controlled trials which account for potential confounders such as ischemic stroke subtypes, technical variations in the testing protocols, patient adherence to therapy, and pharmacogenetic differences.

Poso regency, Central Sulawesi, Indonesia, has a coastal area that has marine tourism potential to be developed. It is expected that marine tourism can bring socio-economic impact to the com-munity. This research was conducted with the objective of assessing the suitability of the area to be developed as a marine and coastal tourism site to provide benefits to the coastal community. Hydrodynamic model will be used in this research as coastal area mapping. As an approach, Analysis Hierarchy Process (AHP) is utilized whose parameters consist of depth, coast type, coast width, brightness, current speed, water base materials, observation of dangerous biota and availability of fresh water. Based on the overall mapping area of 98,644 ha, the research results show that the area that can be utilized is 7,979 ha with a very suitable category, while there is an area of 1,045 ha which can still be classified in the appropriate category.

Any change in the amount and annual distribution of rainfall causes a major socioeconomic and environmental problem where rainfed agriculture is predominant. For that reason, the objective of this study was to determine the long-term variability and trends of precipitation in the Wabi Shebele River Basin (WSRB), Ethiopia. The basin was discretized into 7 local agroclimatic zones (ACZ) based on annual rainfall and elevation. In this study, the coefficient of variation (CV) was used to check the variability of rainfall and modified Mann-Kendall (MK) and Innovative Trend Analysis (ITA) methods were used to detect rainfall trends. For each ACZ, stations with long-term records and less than 10 % missing data were selected for further analysis. The mean annual rainfall in the basin ranges from 227.2 mm to 1047.4 mm. The study revealed most of the ACZs showed a very high variation in Belg/Spring season rainfall (CV % > 30) than Kiremt/Summer and annual rainfall. Trend analysis revealed that no uniform trend was detected among ACZs at each temporal scale. But, most ACZ in the arid and semi-arid areas showed a non-significant decreasing trend. In comparison, a similar result was observed using MK and ITA methods.

Abstract: Botnets pose a grave cybersecurity threat, enabling widescale malicious activities through networks of compromised devices. Detecting botnets is challenging given their frequent use of evasion techniques like encryption. Traditional signature-based methods fail against modern botnets capable of zero-day attacks. This paper surveys recent advances applying machine learning for botnet detection based on analysis of network traffic payloads, flows, DNS data, and hybrid feature fusion. Core machine learning models include support vector machines, neural networks, random forests, and deep learning architectures, which extract patterns to separate benign and botnet behaviors automatically. Results demonstrate machine learning's capabilities in identifying heterogeneous botnets using artefacts in network streams. However, challenges remain around limited labeled data, real-time streaming, adversarial evasion, and model interpretability. Promising directions involve semi-supervised learning, adversarial training, scalable analytics, and explainable AI to address these gaps. Beyond the technical aspects, responsible development and deployment of botnet detection systems raise ethical considerations around privacy, transparency, and accountability. With diligent cross-disciplinary collaboration, machine learning promises enhanced, generalizable, and trustworthy techniques to combat the serious threat posed by continuously evolving botnets across the digital ecosystem.

Searching, recognizing and retrieving a video of interest from a large collection of a video data is an instantaneous requirement. This requirement has been recognized as an active area of research in computer vision, machine learning and pattern recognition. Flower video recognition and retrieval is vital in the field of floriculture and horticulture. In this paper we propose a model for the retrieval of videos of flowers. Initially, videos are represented with keyframes and flowers in keyframes are segmented from their background. Then, the model is analysed by features extracted from flower regions of the keyframe. A Linear Discriminant Analysis (LDA) is adapted for the extraction of discriminating features. Multiclass Support Vector Machine (MSVM) classifier is applied to identify the class of the query video. Experiments have been conducted on relatively large dataset of our own, consisting of 7788 videos of 30 different species of flowers captured from three different devices. Generally, retrieval of flower videos is addressed by the use of a query video consisting of a flower of a single species. In this work we made an attempt to develop a system consisting of retrieval of similar videos for a query video consisting of flowers of different species.

This study investigates the transformative impact of ChatGPT, a cutting-edge AI language model developed by OpenAI, on the financial sector. We explore ChatGPT's applications in financial analysis, focusing on its ability to perform tasks traditionally handled by human analysts. By creating a set of multi-step and advanced reasoning financial tasks, and establishing several task-specific evaluation metrics, our experimental results demonstrate that ChatGPT-4o can effectively perform both basic and some complex financial analysis tasks comparable to those executed by humans. However, our empirical evaluation reveals limitations in its performance on complex tasks that require comprehensive understanding and critical thinking. The study highlights the necessity for meticulous task formulation and robust evaluation to achieve consistent functionality. While ChatGPT enhances efficiency in financial analysis, its integration with human expertise remains crucial for effective decision-making. This study contributes to the understanding of AI's role in finance and provides insights into its potential and constraints, guiding future integration of AI technologies in financial practices.

Huntington's disease (HD) is a rare genetic neurodegenerative disorder that causes motor disorders, neuropsychiatric symptoms and a progressing deterioration of cognitive functions. Complex issues resulting from the hereditary nature of HD, the complexity of symptoms and the concealed onset of the disease have a great impact on the quality of life of family carers. The caregivers are called as “forgotten people” in HD family, also in genetic counseling. This study aims to explore the reliability and validity of the Huntington’s Disease Quality of Life Battery for carers (HDQoL-C) within a Polish population. 90 carers recruited from the Enroll-HD study in Polish research centres of the European Huntington`s Disease Network completed a polish translation of the HDQoL-C. Data was subjected to Principle Components Analysis and reliability measures. The Polish version of the shortened versions of the HDQoL–C is similarly valid compared to the original English version and suitable for use within this population. The HDQoL-C has previously demonstrated a wide range of benefits for practitioners in capturing and understanding carer experience and these benefits can now be extended to Polish speaking populations.

Triple negative breast cancers (TNBCs) are molecularly heterogeneous, and the link between their aggressiveness with African ancestry is not established. We investigated primary TNBCs for gene expression among self-reported race (SRR) groups of African American (AA, n=42) and European American (EA, n=33) women. Using The Cancer Genome Atlas (TCGA) approaches, we analyzed RNA sequencing data to measure changes in genome-wide expression and used logistic regressions to identify ancestry-associated gene expression signatures. To determine global ancestry, GATK best practices were followed for variant calling, and used the 1000 Genomes Project as reference data. We identified >150 African ancestry-associated genes and found that, compared to SRR, quantitative genetic analysis was a more robust method to identify racial/ethnic-specific genes that were differentially expressed. A subset of African ancestry-specific genes that were upregulated in TNBCs of our AA patients were validated in TCGA data. In AA patients, there was a higher incidence of basal-like 2 tumors and altered TP53, NFB1, and AKT pathways. The distinct distribution of TNBC subtypes and altered oncologic pathways show that the ethnic variations in TNBCs are driven by shared genetic ancestry. Thus, to appreciate the molecular diversity of TNBCs, tumors from patients of various ancestral origins should be evaluated.

This study presents the interaction with human host metabolism of SARS-CoV-2 ORF7b protein (43 aa), using a Protein-Protein-Interaction Networks analysis. After pruning, we selected from BioGRID the 51 most significant proteins among 2,753 proven interactions and 1,708 interactors, specific to ORF7b. We used these proteins as functional seeds and we got a significant network of 551 nodes via STRING. We performed topological analysis and calculated topological distributions by Cytoscape. Seven high ranked proteins as hub and seven as bottleneck following a hub-and-spoke network-architectural-model were found. Through this interaction model, we identified significant GO-processes (5,057 terms in 15 categories) induced in human metabolism by ORF7b. High statistical significance processes of dysregulated molecular cell mechanisms by the action of ORF7b were discovered. We detected disease-related human proteins and their involvement in metabolic roles, how they relate in a distorted way to signaling and/or functional systems, in particular intra- and inter-cellular signaling systems and the molecular mechanisms that supervise to programmed cell death, with mechanisms similar to that of cancer metastasis diffusion. A cluster analysis showed 10 compact and significant functional clusters, where two of them overlap in a Giant-Connected-Components core of 206 total nodes. These two clusters contain most of the high-rank nodes. ORF7b mainly acts through these two clusters, inducing most of the metabolic dysregulation. We conducted a co-regulation, transcriptional analysis by hub and bottleneck proteins. This analysis allowed us to define the transcription factors and miRNAs that control the high-ranking proteins and the dysregulated processes, within the limits of the poor knowledge that these sectors still impose.

With the emergence of autonomous functions in road vehicles, there has been an increased use of Advanced Driver Assistance Systems comprising various sensors to perform automated tasks. Light Detection and Ranging (LiDAR) is one of the most important types of optical sensor that detects the positions of obstacles, representing them as clusters of points in 3-dimensional space. LiDAR performance degrades significantly when driving in rain as raindrops adhere to the outer surface of the sensor assembly. The performance degradation behaviors include missing points and reduced reflectivity of the points. It was found that the extent of degradations is highly dependent on the interface material properties, which subsequently affects the shapes of the adherent droplets, causing different perturbations to the optical rays. A fundamental investigation is performed on the protective polycarbonate cover of the LiDAR assembly coated with four classes of materials – hydrophilic, almost-hydrophobic, hydrophobic, and superhydrophobic. Water droplets are controllably dispensed onto the cover to quantify the signal alteration due to each droplet of various sizes and shapes. To further understand the effects of droplet motion on LiDAR signals, sliding droplet conditions are simulated using numerical analysis and validated with physical optical tests using a 905 nm laser source and receiver to mimic the LiDAR detection mechanism. Comprehensive explanations are presented on LiDAR performance degradation in rain from both material and optical perspectives. These can aid component selection and the development of signal enhancing strategies for integrating LiDARs in vehicle designs to minimize the impact of rain.

A heat pump with thermal storage system is a system that operates a heat pump during night-time using inexpensive electricity; during this time, the generated thermal energy is stored in a thermal storage tank. The stored thermal energy is used by the heat pump during daytime. Based on a model of a dual latent heat storage tank and a heat pump, this study conducts control simulations using both conventional and advanced methods for heating in a building. Conventional methods include the thermal storage priority method and the heat pump priority method, while advanced approaches include the region control method and the dynamic programming method. The heating load required for an office building is identified using TRNSYS, used for simulations of various control methods. The thermal storage priority method shows a low coefficient of performance (COP), while the heat pump priority method leads to high electricity costs due to the low use of thermal storage. In contrast, electricity costs are lower for the region control method, which operates using the optimal part load ratio of the heat pump, and for dynamic programming, which operates the system by following the minimum cost path. According to simulation results for the winter season, the electricity costs using the dynamic programming method are 17% and 9% lower than those of the heat pump priority and thermal storage priority methods, respectively. The region control method shows results similar to the dynamic programming method with respect to electricity costs. In conclusion, advanced control methods are proven to have advantages over conventional methods in terms of power consumption and electricity costs.

Adequate public spaces and urban green areas are key criteria for urban development and infrastructure implementation of healthy cities. Latterly, there has been an increasing number of research methods using artificial intelligence (AI) to monitor, quantify, and control the state of these spaces with an aim towards pioneering research in Urban Studies. However, in informal areas, open-data access tends to lack adequate and updated information, making it difficult to use AI methods. Hence, we propose a methodology for restricted open data collection and preparation for future use in machine learning or spatial data science models for similar areas. To that extent, we examine two peripheral and low-income neighborhoods in Quito, Ecuador, La Bota and Toctiuco, to analyze their public spaces, urban green areas, points of interest, and road networks, and how they address healthy cities criteria. We develop an original methodological approach that combines an index of proximity, accessibility, quantity, and quality of these spaces with geospatial and network analysis techniques. Results indicate that the connectivity and structure of these spaces are centralized and nodal, representing exclusion and segregation. This work provides insights on potential healthy spaces, and information to urban planners and policymakers in decision-making for healthy urban infrastructure.

Glioblastoma multiforme (GBM), the most prevalent primary malignant brain tumor in adults, exhibits a dismal 6.9% five-year survival rate post-diagnosis. Thymoquinone (TQ), the most abundant bioactive compound in Nigella Sativa, has been extensively researched for its anti-cancer properties across various human cancers. Yet, its specific anticancer mechanisms and pathways in glioblastoma remain to be elucidated. In this study, we assessed the impact of different TQ concentrations on the viability of A172 cells using WST-8 and Toluidine blue assays, followed by RNA sequencing (RNA-seq) to identify differentially expressed genes (DEGs). We confirmed their expression levels through quantitative RT-PCR and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for these DEGs. RNA-seq revealed no significant gene expression changes at 2.5 µM and 5 µM TQ concentrations. However, at 25 µM and 50 µM, TQ significantly reduced cell viability in a dose-dependent manner. We identified 1548 DEGs at 25 µM TQ (684 upregulated, 864 downregulated) and 2797 DEGs at 50 µM TQ (1528 upregulated, 1269 downregulated), with 1202 DEGs common to both concentrations. TQ inhibited key pathways such as PI3K-Akt signaling, calcium signaling, focal adhesion, and ECM-receptor interaction in A172 cells. It downregulated several potential oncogenes (e.g., AEBP1, MIAT) and genes linked to GBM proliferation and migration (e.g., SOCS2, HCP5), while modulating Wnt signaling and upregulating tumor suppressor genes (e.g., SPRY4, BEX2). TQ also affected p53 downstream targets, maintaining p53 levels. This study elucidates the anticancer mechanisms of TQ in A172 GBM cells, underscoring its effects on multiple signaling pathways and positioning TQ as a promising candidate for innovative glioblastoma treatment strategies.

Drought and salinity are among the most important abiotic stress factors that limit the productivity in economically important plants. Although genes from wild relatives or unadapted germplasm have often been used for stress tolerance breeding, only few studies have examined wild relatives to understand the genetic and molecular basis of stress tolerance. In this study, we monitored the changes in gene expression profiles of leaf and root tissues of two wild barleys (Hordeum spontaneum L.) accessions from Israel after treatment with salt (NaCl) and drought (PEG) stress. We identified a total of 641 differentially expressed genes across 24 conditions (2 accessions, 2 stress conditions, 2 tissues and 3 time points). Our gene expression analyses revealed large numbers of differentially expressed genes in different accessions by different stress treatments. In addition, the number of genes altered in the leaves of stress-exposed plants of both genotypes was higher than those altered in the roots. Interestingly, there was a relatively little overlap between the leaf and the root stress-responsive gene expression patterns, suggesting that different stress-associated processes might be operating in these tissues during stress adaptation. Overall, our results revealed a number of candidate genes and plant processes associated with stress tolerance in wild barley. Wild barley would be a useful source of new genetic variation for drought and salt stress tolerance. Our results could provide new insights into the mechanisms of drought and salt stress tolerance in wild barley and should be useful for genetic improvement of salt-drought tolerance in cultivated barley.

The present work describes the optimization and validation of a highly selective and sensitive analytical method using solid phase extraction and liquid chromatography tandem mass spectrometry (SPE LC-MS/MS) for the determination of some frequently prescribed pharmaceuticals in urban wastewater received and treated by Sharjah sewage treatment plant (STP). The extraction efficiency of different SPE cartridges was tested and the simultaneous extraction of pharmaceuticals was successfully accomplished using hydrophilic-lipophilic-balanced reversed phase Waters^® Oasis HLB cartridge (200 mg/ 6 mL) at pH 3. The analytes were separated on an Aquity BEH C₁₈ column (1.7 µm, 2.1 mm x 150 mm) using gradient elution and the mass spectrometric analysis were performed in multiple reactions monitoring (MRM) selecting two precursor ions to produce ion transition for each pharmaceutical using positive electrospray ionization (+ESI) mode. The correlation coefficient values in the linear calibration plot for each target compound exceeded 0.99 and the recovery percentages of the investigated pharmaceuticals were more than 84%. Limit of detection (LOD) varied between 0.1-1.5 ng/L and limit of quantification (LOQ) was 0.3-5 ng/L for all analytes. The precision of the method was calculated as the relative standard deviation (RSD%) of replicate measurements and was found to be in the ranges of 2.2% to 7.7% and 2.2% to 8.6% for inter and intra-day analysis, respectively. All of the obtained validation parameters satisfied the requirements and guidelines of analytical method validation.

Solar radiation impacts diverse aspects of city life, such as harvesting energy with PV panels, passive heating of buildings in winter, cooling loads of air-conditioning systems in summer, and urban microclimate. Urban digital twins and 3D city models can support solar studies in the process of urban planning and provide valuable insights for data-driven decision support. This study examines the calculation of solar incident radiation at the city scale in Sofia using remote sensing data for the large shading context in a mountainous region and 3D vector building data. The building footprints and the terrain are preprocessed using GIS. The solar calculation uses a raytracing algorithm in 3D CAD at the city scale. To evaluate the performance, two cases were considered at the city and district scale, respectively. The total face count of meshes for the simulations constituted approximately 2 000 000 faces. 64379 roofs for the whole city and 4796 buildings for one district were selected. All calculations were performed in one batch and visualized in a 3D web platform. Using a 3D CAD environment establishes a seamless process of updating 3D models and simulations while preprocessing in GIS ensures working with large-scale datasets. The proposed method showed moderate computation time for both cases and could be extended to include reflected radiation and dense photogrammetric meshes in the future.

Recently, the Third Generation Partnership Project (3GPP) introduced the New Radio (NR) technology for Vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing Long Term Evolution (LTE) Cellular-Vehicle-to-Everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the 5th generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from Dedicated Short Range Communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.

Clinical agreement between two quantitative measurements on a group of subjects is generally assessed with the help of the Bland-Altman (B-A) limits. The interpretation regarding agreement is based on whether B-A limits are within the pre-specified clinical tolerance. Thus, clinical tolerance limits are necessary for this method. We argue in this communication that such limits of clinical tolerance can be directly used for assessing agreement and plead that this nonparametric approach is simple and robust to the distribution pattern and outliers. Such direct use of clinical tolerance limits has more flexibility, and it is more effective in assessing the extent of agreement.

A quantum time-dependent spectrum analysis, or simply, quantum spectral analysis (QSA) is presented in this work, and it’s based on Schrödinger’s equation. In the classical world, it is named frequency in time (FIT), which is used here as a complement of the traditional frequency-dependent spectral analysis based on Fourier theory. Besides, FIT is a metric which assesses the impact of the flanks of a signal on its frequency spectrum - not taken into account by Fourier theory and let alone in real time. Even more, and unlike all derived tools from Fourier Theory (i.e., continuous, discrete, fast, short-time, fractional and quantum Fourier Transform, as well as, Gabor) FIT has the following advantages, among others: a) compact support with excellent energy output treatment, b) low computational cost, O(N) for signals and O(N2) for images, c) it does not have phase uncertainties (i.e., indeterminate phase for a magnitude = 0) as in the case of Discrete and Fast Fourier Transform (DFT, FFT, respectively). Finally, we can apply QSA to a quantum signal, that is, to a qubit stream in order to analyze it spectrally.

The road traffic injuries risk factors such as driving offenses and average speed are concerns for health organizations to reduce the number of injuries. Without any comprehensive view of each road, one cannot decide about the effective policy. In this manner, the data-driven policy will help to improve and assess the decisions. The count data near the road of two airports is surveyed for investigating the time-varying speed zones. The descriptive statistics, ANOVA, and functional data analysis were used. The hourly data of traffic counts for four different locations at the entrance of the two airports, international and domestics, were collected for one the year 2018 to 2019.The hourly pattern of driving offenses for each road was assessed and the to and from airport roads had different peaks (<0.05). The hour, weekdays, type of airport, direction and their interactions were statistically significant (<0.05) for the chance of driving offenses. The speed average during the day was statistically different (<0.5) by the number of different types of vehicles. The traffic count data is a great resource for decision making in safe driving subjects such as driving offenses. With functional data analysis, we can analyze them to get the most of the characteristics of this data. The airports are public places with high traffic demand in all countries that yields the different pattern of traffic transportation, therefore we extract the factors that affect the driving offenses. Finally, we conclude that conducting a time-varying speed zone near the airports seems vital.

The aim of this paper is to optimize thickness variation function of simply supported and cantilever beams, in terms of maximizing gaps between chosen neighboring frequencies and analyze obtained results. The optimization results are examined in terms of achieving the objective function (related to eigenproblem), but also in terms of their dynamics stiffness (forced vibrations excited by point harmonic load). In the optimization process, a Genetic Algorithm was used. Problems related to structural dynamics were solved by FEM implementation into the algorithm. Sample results were presented, and the developed algorithm was analyzed in terms of result convergence by examining several variable parameters. The authors demonstrated the validity of applying the described optimization tool to the presented problems. Conclusions were drawn regarding the correlation between stiffness and mass distribution in the optimized beams and the natural frequency modes in terms of which they were optimized.

Adopting Building Information Modeling (BIM) within the Architecture, Engineering, and Construction (AEC) industry presents an opportunity to address enduring challenges, including chronic productivity deficits and emerging imperatives such as cleaner production and sustainability. However, the pace of BIM implementation (BIMI) across European Union (EU) countries is uneven due to varying contexts and the inherent complexity of BIM adoption. Notably, Portugal lags in BIMI among these countries. Using the Portuguese context, the manuscript endeavours to identify the main barriers to BIMI by the late-adopting countries in the EU and, based on the dynamics of their relationships, design effective mitigation measures to lessen their impact and eventually leverage BIMI. A comprehensive list of barriers was compiled from the literature and ranked by experts through a Delphi survey, resulting in 15 critical barriers. Subsequently, an Interpretive Structural Modelling (ISM) model was constructed to elucidate the hierarchical relationships among these barriers, while a fuzzy Impact Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) analysis was employed to determine their driving and dependence powers. The resulting main barriers were the lack of evaluation and feedback mechanisms on BIMI, ignorance of BIM capabilities/benefits, scarcity of BIM-capable professionals, lack of experience and cooperation in BIMI, resistance to change, and lack of support from top management. Finally, experts formulated mitigation measures aimed at tackling these main barriers. These measures were designed to not only address each barrier individually but also to have a comprehensive and practical impact on the entire barrier system as a whole. These findings will assist researchers, policymakers, and practitioners in comprehending the barriers and hierarchical structures affecting BIMI in EU late-adopting countries.

Non-timber forest products (NTFP) have contributed in various ways to the living, livelihoods, and trading of households and communities who live in rural and urban areas all over the world. This paper analyses the value chain of Jiaogulan (Gynostemma pentaphyllum) in the Cao Bang Geopark, Vietnam. 106 actors were surveyed using a semi-structured questionnaire: experts (agricultural extension officers, farmers' associations), harvesters, traders, primary processors, processors, distributors, and consumers. The results show that a map of Jiaogulan value chain, value added of households in main market channels, and impact of value added and distribution of Jiaogulan products on household income.

Prefrontal cortex (PFC) is one of the brain regions with more prominent changes in human aging. The molecular processes related to the aging cognitive decline and mood changes are not completely understood. In order to improve our knowledge, we integrated transcriptomic data of four studies of human PFC from old people -58-80 years old- compared with young people -20-40 years old- using a meta-analytic approximation combined with molecular signature analysis. We identified 1816 differentially expressed genes -561 up-regulated and 1256 down-regulated. Pathway analysis revealed down-regulation of synaptic genes with conservation of gene expression of other neuronal regions. Additionally, we identified up-regulation of markers of astrogliosis with transcriptomic signature compatible with A1 neurotoxic astrocytes and A2 neuroprotective astrocytes. Response to interferon is related to A1 astrocytes and the A2 phenotype is mediated in aging by activation of SHH pathway and up-regulation of metallothioneins I and genes of the family EZR -ezrin, radixin, and moesin-. The main conclusions of our study are the confirmation that in aged PFC there is a global dysfunction of the synapses and we reported for the first time opposite phenotypes of astrogliosis because of brain aging.

The work presented in this paper makes multiple scientific contributions with a specific focus on the analysis of misinformation about COVID-19 on YouTube. First, the results of topic modeling performed on the video descriptions of YouTube videos containing misinformation about COVID-19 revealed four distinct themes or focus areas - Promotion and Outreach Efforts, Treatment for COVID-19, Conspiracy Theories regarding COVID-19, and COVID-19 and Politics. Second, the results of topic-specific sentiment analysis revealed the sentiment associated with each of these themes. For the videos belonging to the theme of Promotion and Outreach Efforts, 45.8% were neutral, 39.8% were positive, and 14.4% were negative, for the videos belonging to the theme of Treatment for COVID-19, 38.113% were positive, 31.343% were neutral, and 30.544% were negative, for the videos belonging to the theme of Conspiracy Theories regarding COVID-19, 46.9% were positive, 31.0% were neutral, and 22.1% were negative, and for the videos belonging to the theme of COVID-19 and Politics, 35.70% were positive, 32.86% were negative, and 31.44% were negative. Third, topic-specific language analysis was performed to detect the various languages in which the video descriptions per topic were published on YouTube. This analysis revealed multiple novel insights. For instance, for all the themes, English and Spanish were the most widely used and second-most widely used languages, respectively. Fourth, the patterns of sharing these videos on other social media channels such as Facebook and Twitter were also investigated. The results revealed that videos containing video descriptions in English were shared the highest number of times on Facebook and Twitter. Finally, correlation analysis was performed by taking into account multiple characteristics of these videos. The results revealed that the correlation between the length of the video title and the number of Tweets as well as the correlation between the length of the video title and the number of Facebook posts was statistically significant.

As of 2020, public data for game ratings provided by Game Ratings And Administration Committee(GRAC) are more limited than public data for movie and video ratings provided by Korea Media Ratings Board and do not provide data which allow us to see information on ratings clearly and in detail. To get information on game ratings, we need to find information by searching for specific target on homepage which is inconvenient for us. In order to improve such inconvenience and extend scope of provision in public data, the author of this paper intends to study public data API which has been extended based on information on video ratings. To draw items to be extended, this study analyzes data for ratings on homepage of GRAC and designs collection system to build database. This study intends to implement system that provides data collected based on extended public data items in a form which users want. This study is expected to provide information on ratings to GRAC which will strengthen fairness and satisfy game users and people’s rights to know and contribute to promotion and development of game industry.

The article contains the results of experimental studies for a multi-opening orifice with substitute constriction factor of β = 0.5 (m = 0.25), mounted in a DN50 hydraulic measuring flume. Flow measurements were taken for a progressing turbulent flow within Reynolds numbers (Re = 4700...19500). Based on experimental data, flow characteristics as well as discharge coefficient C characteristics were determined. Relative expanded uncertainty of determining a discharge coefficient C was estimated within the changes of volume flow qv from 0.35 dm3/s to 0.68 dm3/s. The value does not exceed 1.25% within the changes of Reynolds numbers 9800 ≤ Re ≤ 19500.

In the crustacean immune system, leucine-rich repeat (LRR) is one of the major structures for recognizing pathogen-associated molecular patterns (PAMPs). LRR domain-containing proteins belong to the LRR family, which is a large group of proteins with more than 6000 genes in the database. They are involved in very diverse physiological functions mainly by interacting with other proteins. In a previous study, the LvLRRm, a transmembrane protein containing only LRR domain, was identified in the white leg shrimp, Litopenaeus vannamei. Its versatile role in performing multiple immunomodulation activities has been reported. However, there is still a lack of research on its efficient function at the protein level. To investigate its interactions with other proteins, we applied a convenient method called ‘Hybrid LRR technique’ to produce a recombinant LvLRRm. The LvLRRm and hagfish’s variable lymphocyte receptors (VLRs) fragments were fused to the conserved LxxLxLxxN motif while retaining the β-strand. In addition, we established interactions between hybrid-proteins and flagellin of Salmonella by performing surface plasmon resonance (SPR) analysis. The designed hybrid proteins showed potential for universal applications without losing protein functions.

Revealing the multidimensional value of cultivated land resources, improving calculation methods, and exploring their spatial distribution characteristics and influencing factors are crucial for the enhancement of natural resource accounting system and the protection of cultivated land resources. This study take Guangxi, China as an example and construct a comprehensive multidimensional value evaluation system for cultivated land resources at the county scale. The income capitalization method, substitution market method, and value equivalent correction method were applied to calculate the economic, social, and ecological values of cultivated land resources in 111 counties. Furthermore, spatial autocorrelation analysis was employed to study their distribution characteristics and influencing factors. The results show that: (1) The total value of cultivated land resources in Guangxi reach 19,729.2 billion yuan, and the average cultivated land value is 5.3511 million yuan/hm2, of which the ratio of economic, social and ecological values is about 6:1:2; (2) The value of cultivated land has a certain distribution pattern in the global space, and the average economic and social value show the spatial distribution patterns of "low in the west and high in the east" and "low in the northwest and high in the southeast" respectively."(3) There is a significant spatial clustering effect of all values of cultivated land resources in local space, with "high-high" and "low-low" clustering. (4) Economic development level is significantly negatively correlated with the ecological value of cultivated land, while has a significant positive correlation with social value; (5) Natural conditions, economic development and agricultural development approaches are the key factors affecting the value of cultivated land resources.

Background: Jerusalem artichoke (Helianthus tuberosus L.) is tolerant to salinity stress and has high economic value. The salt tolerance mechanisms of Jerusalem artichoke are still unclear. Especially in the early stage of Jerusalem artichoke exposure to salt stress, the plant physiology, biochemistry and gene transcription are likely to undergo large changes. Elucidating these changes may be of great significance to understanding the salt tolerance mechanisms of it. Results: We obtained high-quality transcriptome from leaves and roots of Jerusalem artichoke exposed to salinity (300 mM NaCl) for 0 h, 6 h, 12 h, 24 h and 48 h, with 150,129 unigenes and 9023 DEGs (Differentially Expressed Genes). The RNA-seq data were clustered into time-dependent groups (nine clusters each in leaves and roots); gene functions were distributed evenly among the groups convergence. KEGG enrichment analysis showed the genes related to plant hormone signal transduction were enriched in almost all treatment comparisons. Under salt stress, genes belongs to PYL (abscisic acid receptor PYR / PYL family), PP2C (Type 2C protein phosphatases), GH3 (Gretchen Hagen3), ETR (ethylene receptor), EIN2/3 (ethylene-insensitive protein 2/3), JAZ (Genes such as jasmonate ZIM-domain gene) and MYC2 (Transcription factor MYC2) had extremely similar expression patterns. The results of qPCR of 12 randomly selected genes confirmed the accuracy of RNA-seq. Conclusions: Under the impact of high salinity (300mM) environment, Jerusalem artichoke in the seedling stage was difficult to survive for a long time, and the phenotype was severe in the short term. Based on the expression of genes on the time scale, we found that the distribution of gene functions in time is relatively even. Upregulation of the phytohormone signal transduction had a crucial role in the response of Jerusalem artichoke seedlings to salt stress, the genes of abscisic acid, auxin, ethylene, and jasmonic acid had the most obvious change pattern.

Introduction: To indentify the most effective actors(authors, countries, and journals) about composite resin restorations in the period 2000-2020.Material and Methods: An electronis research was conducted in the Scopus database by selecting the words ‘composite resin’ and ‘restoration and English language, article and review types, dentistry field. Their bibliometric data including publication title, authorship, citation count, citation dentistry, year of publication, country and institution of origin, journal of publication, study design, and keywords were extracted and analyzed.Results and Discussion: To our knowledge, this is the first bibliometric article on composite resin restorations. This study provides information about authors, institutions and countries that contribute to significant improvements in composite resin restorations. From 2000 to 2020, there were 7118 articles published from 99 countries.Articles originate primarily from the USA and Brazil. Results indicate that the USA, Brazil, Germany, Turkey, the United Kingdom, Japan, Swtizerland, Italy, Netherlands and India are the leading countries in composite resin restoration research and account for 51.8% of the total number of publications. The total number of citations are 158.404, corresponding to 22 citations per paper publication. During the time period examined, 776 hot articles and 228 classic articles on composite resin restorations were found.The journal with the most publications is ‘Operative Dentistry’. The publishing houses of the top 10 journals are from 4 countries: USA(6),Netherlands(2),Germany(1),Japan(1). The most cited article within the boundaires of this study is Ferracane’s article titled ‘Resin composite-State of the art’, which was published in Dental Materials in 2011 and received 913 citations.

Legacy risks from infrastructures and industrial installations often reveal themselves when a potential for failure has been discovered much later than at the stage of the design and construction of a structure. In which case, there might already be a problem with the legacy installation, or even a crisis, without having had an accident. When the hazard cannot be taken away, the question arises as to how much effort, if any, should be spent on improving the situation. The usefulness of the three archetypical approaches to this problem: setting a standard, the As Low As Reasonably Practicable approach and a case-by-case discourse approach are discussed for their applicability for these legacy risks. Although it would be desirable to retrofit legacy risks to previously set legal requirements as is the case when acceptability limits are set in law or demonstration of ALARP is demanded, it may be impossible to reduce the residual risk to an otherwise acceptable level without taking away or replacing the infrastructure, which is not acceptable either. Therefor in conclusion the only available solution to persistent legacy risk problems seems to be to have a thorough discussion with all relevant stakeholders until an agreement is in some way found.

The reality of people’s lives has shifted from rural to urban areas, where an ever-increasing proportion of the world’s population lives. Providing infrastructure to serve these areas, especially in the Global South, is a key task of sustainable development. A deep understanding of the spatial arrangement and scales of these urban structures and their temporal evolution can help to develop innovative solutions to issues of energy, water, or transportation infrastructures. For this purpose, in this work we study the temporal evolution of urban built-up structures (Global Artificial Impervious Area) and population distributions (Global Human Settlement Population) in four regions of the Global South (Argentina, India, Egypt, and Nigeria). We qualitatively analyze regularity through the pair correlation function and subsequently identify typical scales within the different interurban systems. In doing so, we identify that especially the large settlement objects arrange themselves in a regular way and thus typical scales exist in urban systems. Thus, settlement objects are usually located about 20 to 40 km apart from each other. This information can be used to develop sustainable infrastructure concepts, for example for passenger transport between settlements.

This paper introduces an ecological approach to quantifying diversity in musical compositions. The approach considers notations with distinct pitches and duration as equivalents of species in ecosystems, measures within a composition as equivalents of ecosystems, and the sum of measures (i.e., the entire composition) as a landscape in which ecosystems are embedded. Structural diversity can be calculated at the level of measures (“alpha diversity”) and the entire composition (“gamma diversity”). An additional metric can be derived that quantifies the structural differentiation between measures in a composition (“beta diversity”). We demonstrate the suitability of the approach in music using specifically composed examples and real songs that vary in complexity. We discuss the potential of the approach with selected examples from a potentially ample spectrum of applications within musicology research. The method seems particularly suitability for hypothesis testing to objectively identify many of the intricate phenomena in music. Because the approach extracts information present in the compositions – it lets the songs tell their structure – it can complement more complex modeling approaches used by music scholars. Combined such approaches provide opportunities for interdisciplinary research. They can help to fill knowledge gaps, stimulate further research and increase our understanding of music.

Black yeasts are a highly specified group of fungi, which are characterized by a high resistance against stress factors. There are several factors enabling the cells to survive harsh environmental conditions. One aspect is the pigmentation, besides the melanin black yeasts often display a highly diverse carotenoid spectrum. Determination and characterization of carotenoids depend on an efficient extraction and separation, therefore especially for black yeast, characterized by thick cell walls specific protocols are needed to ensure analyses regarding stress responses in these fungi. Here we present both, a method to extract and analyze carotenoids and the unusual carotenoid composition of the black yeast Knufia petriola A95. Mechanical treatment combined with an acetonitrile extraction gave us very good extraction rates with a high reproducibility. The presented extraction and elution protocol allows the separation of the main carotenoids (7) in K. petricola A95 and should be suitable for the detection of additional carotenoids in other species. K. petricola A95 displays an unusual carotenoid composition, with mainly didehydrolycopene, torulene and lycopene. The pigment composition varied in dependency to oxidative stress but remained relatively constant if the cells were cultivated under low temperature. Black yeasts are a promising source for carotenoid production and other substances. To unravel the potential of these fungi new methods and studies are needed. The established protocol allows the determination carotenoid composition in black yeasts. Oxidative stress results in an adaptation in pigment composition in K. petricola A95. Future experiments have to be carried out to determine if didehydrolycopene functions as a protective agent itself or if it serves as a precursor for antioxidative pigments like torulene and torularhodin, which could be produced after induction under stress conditions.

In this article, we estimate the role of Political Stability and Absence of Violence and Terrorism-PS in the context of Environmental, Social and Governance-ESG data at world level. We analyse data from 193 countries in the period 2011-2020. We apply Panel Data with Fixed Effects, Panel Data with Random Effects and Pooled Ordinary Least Square-OLS. We found that PS is positively associated, among others, to Population Density and Government Effectiveness, and negatively associated, among others, to Research and Development Expenditure and Maximum 5-day Rainfall. Furthermore, we apply the k-Means algorithm optimized with the application of the Elbow Method and we find the presence of four clusters. Finally, we propose a confrontation among eight different machine-learning algorithms for the prediction of PS and we find that the Polynomial Regression shows the higher performance. The Polynomial Regression predicts an increase in the level of PS of 0.25% on average for the analysed countries.