Aforementioned Analyses Research Articles

Utilize proteomic analysis to identify potential therapeutic targets for combating sepsis and sepsis-related death.

Sepsis is an inflammatory disease that leads to severe mortality, highlighting the urgent need to identify new therapeutic strategies for sepsis. Proteomic research serves as a primary source for drug target identification. We employed proteome-wide Mendelian randomization (MR), genetic correlation analysis, and colocalization analysis to identify potential targets for sepsis and sepsis-related death. Genetic data for plasma proteomics were obtained from 35,559 Icelandic individuals and an initial MR analysis was conducted using 13,531 sepsis cases from the FinnGen R10 cohort to identify associations between plasma proteins and sepsis. Subsequently, significant proteins underwent genetic correlation analysis, followed by replication in 54,306 participants from the UK Biobank Pharma Proteomics Project and validation in 11,643 sepsis cases from the UK Biobank. The identified proteins were then subjected to colocalization analysis, enrichment analysis, and protein-protein interaction network analysis. Additionally, we also investigated a MR analysis using plasma proteins on 1,896 sepsis cases with 28-day mortality from the UK Biobank. After FDR correction, MR analysis results showed a significant causal relationship between 113 plasma proteins and sepsis. Genetic correlation analysis revealed that only 8 proteins had genetic correlations with sepsis. In the UKB-PPP replication analysis, only 4 proteins were found to be closely associated with sepsis, while validation in the UK Biobank sepsis cases found overlaps for 21 proteins. In total, 30 proteins were identified in the aforementioned analyses, and colocalization analysis revealed that only 2 of these proteins were closely associated with sepsis. Additionally, in the 28-day mortality MR analysis of sepsis, we also found that only 2 proteins were significant. The identified plasma proteins and their associated metabolic pathways have enhanced our understanding of the complex relationship between proteins and sepsis. This provides new avenues for the development of drug targets and paves the way for further research in this field.

Association between neighbourhood poverty and type 2 diabetes risk. Does moving from a high to lower poverty neighbourhood reduce diabetes risk?

ObjectivesDiabetes, a pressing global public health crisis, Diabetes, a global health crisis, is significantly impacted by social determinants, including neighborhood characteristics. This study aims to to assess whether relocation from a high poverty to lower poverty neighbourhood is associated with a reduction in T2D incidence. Methods and ResultsThis population-based, propensity-matched cohort study will use linked administrative health to examine the association between neighborhood relocation and T2D incidence. The study population will include adults (age ≥20 years) residing in high poverty urban neighborhoods between April 1st, 2002, to March 31st, 2021, as defined by an area Low-Income Measure - After Tax value of 38,730 dollars for a household size 4 based on the Canadian Census. Individuals will be followed for a new diagnosis of T2D using a validated algorithm based on hospitalization and physicians’ claims data. Propensity score matching will used to match individuals who moved from high-to-lower poverty neighbourhoods to one of two comparison groups: those moving from high-to-high poverty areas and those who remain in their original neighbourhood. Time-to-event analysis utilizing Cox proportional hazards regression with a robust variance estimator will be used to compare T2D incidence between matched groups. As a sensitivity analysis, non-propensity score modeling will be conducted using neighbourhood of residence as a time-varying covariate. The results of the aforementioned analyses will be presented during the conference. ImplicationsBy clarifying the link between neighborhood poverty and T2D incidence, the results will guide focused interventions to alleviate health inequalities in socioeconomically disadvantaged areas.

Pretreatment method for oxygen stable isotope ratio analysis of the sugar-rich fraction in fruit juice via isotope ratio mass spectrometry.

The oxygen stable isotope ratio (δ18O) of the sugar-rich fraction of fruit juice is important as a tracer of the geographical origin of raw material. This study sought to minimize the inter-day variation of δ18O attributable to the influence of water to accurately monitor geographical origin labeling. Two drying devices (freeze dryer and vacuum oven) were compared. Then, two humidity levels (normal and low humidity) at which the samples were placed after drying were compared. The low-humidity environment was constructed using a glove bag and pure argon gas. δ18O was measured using thermal conversion elemental analyzer/isotope ratio mass spectrometry. Improvements were made to the measurement method based on aforementioned analyses results, and the performance of the initial and improved methods was compared. δ18O of juice dried in a vacuum oven was 3.30‰ lower than that of juice dried in a freeze dryer. Moreover, δ18O of juice samples exposed to normal humidity was 3.74‰ lower than that of samples exposed to low humidity. The combined inter-day and intra-day standard deviation was reduced from 1.20‰ in the initial method to 0.42‰ in the improved method. This study describes a pretreatment method for δ18O measurement in the sugar-rich fraction of fruit juice with less inter-day variation, and it will be useful for monitoring geographical origin labeling.

Commercial Application of Artificial Intelligence in Chinese Pan-mental Healthcare Industry

In the modern educational landscape, the mounting academic demands imposed on adolescents, coupled with a noticeable trend of enrolling children into formal schooling at increasingly younger ages, have become influential factors in the exacerbation of emotional stress experienced by young individuals. This burgeoning concern for mental health matters within contemporary society necessitates a heightened level of attention and thorough investigation. This comprehensive report unfolds as an intricate exploration of multifaceted dimensions. It begins with a meticulous literature review, which delves into the existing body of knowledge surrounding the issue. Subsequently, the report investigates the practical applications of AI in the domain of mental healthcare, recognizing the potential benefits and advancements AI technology can bring to the field. Moreover, an external analysis is conducted to contextualize the broader societal and environmental factors contributing to the observed challenges. A competitive landscape assessment is performed to understand the existing solutions and their strengths and weaknesses. Further, strategies related to market segmentation and targeting are formulated to better address the diverse needs of the audience. The report also provides a detailed description of the target customer base, offering insights into the demographics and psycho-graphics of the individuals who stand to benefit most from the proposed AI-driven solution. A mock product is meticulously designed, integrating the learning from the aforementioned analyses. Furthermore, conjoint analysis and data analysis techniques are applied to discern the nuanced preferences and needs of the target audience. The study, rooted in empirical research, investigates the intricate web of psychological challenges teenagers face in present-day society. In response to the identified issues, the report outlines the development of an AI-exclusive application. This application is thoughtfully tailored to ameliorate the mental health challenges specific to teenagers, offering a promising avenue for providing timely and effective support to this vulnerable demographic.

Microbial diversity across tea varieties and ecological niches: correlating tea polyphenol contents with stress resistance.

Microorganisms exhibit intricate interconnections with tea plants; however, despite the well-established role of microorganisms in crop growth and development, research on microbes within the tea plant remains insufficient, particularly regarding endophytic microorganisms. In this study, we collected samples of leaves and rhizosphere soils from 'Zhuyeqi', 'Baojing Huangjincha#1', 'Baiye#1', and 'Jinxuan' varieties planted. Our analyses revealed significant variations in tea polyphenol contents among tea varieties, particularly with the 'Zhuyeqi' variety exhibiting higher levels of tea polyphenols (>20% contents). Microbiome studies have revealed that endophytic microbial community in tea plants exhibited higher host specificity compared to rhizospheric microbial community. Analyses of across-ecological niches of the microbial community associated with tea plants revealed that soil bacteria serve as a significant reservoir for endophytic bacteria in tea plants, Bacillus may play a crucial role in shaping the bacterial community across-ecological niche within the tea plants with higher tea polyphenol levels. In the aforementioned analyses, the microbial community of 'Zhuyeqi' exhibited a higher degree of host specificity for leaf endophytic microorganisms, the topological structure of the co-occurrence network is also more intricate, harboring a greater number of potential core microorganisms within its nodes. A closer examination was conducted on the microbial community of 'Zhuyeqi', further analyses of its endophytic bacteria indicated that its endophytic microbial community harbored a greater abundance of biomarkers, particularly among bacteria, and the enriched Methylobacterium and Sphingomonas in 'Zhuyeqi' may play distinct roles in disease resistance and drought resilience in tea plants. In summary, this study has shed light on the intricate relationships of tea plant varieties with their associated microbial communities, unveiling the importance of microorganisms and tea varieties with higher tea polyphenols, and offering valuable insights to the study of microorganisms and tea plants.

A parametric life cycle framework to promote sustainable-by-design product development: Application to a hydrogen production technology

The European Ecodesign Directive is an effective normative framework that has been extensively proven to support the energy transition of numerous European industrial sectors. From an analytical standpoint, it provides practitioners with the EcoReport tool, a simplified life cycle spreadsheet that is aimed at guiding the development of ecodesign measures of mandatory compliance in European countries. In this regard, several studies have highlighted the limitations of the EcoReport tool when addressing emerging technologies like those tied to the hydrogen sector. These works also propose to further integrate material criticality and social metrics in order to enlarge the scope of the European Directive and foster the shift from ecodesign to sustainable-by-design product development. In this situation, building upon the principles of the EcoReport tool and recognizing the outcomes of the aforementioned critical analyses, the conceptualization of a novel sustainable-by-design framework is presented and applied to a Solid Oxide Electrolysis Cell (SOEC) stack for hydrogen production. The operationalization of the framework is conducted, for the first time in the context of sustainable design, by combining the use of the Brightway2 and lca_algebraic Python packages. Overall, the proposed approach succeeds in providing a complete sustainability perspective to the design of emerging technologies. Regarding the tangible lessons learned on the hydrogen-related case study, product concepts are proven to progressively improve the sustainability performance of the technology. It is noticeable that the enhancement of the economic competitivity is more limited than that achieved at the remaining sustainability indicators (i.e., environmental, social and material criticality metrics). In line with the outcomes of the life cycle contribution assessment, multi-criteria decision analysis ratings lead to concluding that a sustainable-by-design SOEC stack product concept should prioritize limiting its material intensity.

Conjoint impact of modified HHO system, compression ratio variation, and tribological enhancement of steel piston ring substrates on performance and emission characteristics of a spark-ignition engine

In these times of efforts to develop high-efficiency hydrogen-adopted or hydrogen-fueled internal combustion engines (ICEs), and concerns related to electric vehicles such as battery cost, drawbacks in harsh winter climates, low range, etc., it is important to generate original ideas in line with the manufacturing of ICEs with improved efficacy. Accordingly, this study presents a comprehensive experimental investigation to observe the cooperated effects of pulse width modulation (PWM)-controlled hydroxy (HHO) gas introduction and improvement in tribological performance of piston ring-cylinder liner mechanism on performance and emission characteristics of a spark-ignition (SI) engine (co-system). The variation of compression ratio (CR), influence of two electrolyzer types of tube electrolyzer (TE) and plate electrolyzer (PE) for production of HHO gas, and zeta potential (ZP)-based dynamic light scattering (DLS) analysis to specify the optimal catalyst concentration in de-ionized water were also observed. Electron cyclotron resonance-chemical vapor deposition (ECR-CVD) method was utilized to bombard the piston ring substrates with diamond-like carbon (DLC) atoms under high energy plasma to improve the mechanical strength of the friction surface. Using linear tribometer, the uncoated (UPR) and coated piston rings (CPR) underwent friction tests to determine the wear rate (WR), and coefficient of friction (COF) which have substantial contribution to frictional losses. The surfaces of the samples were visualized via scanning electron microscopy (SEM) and atomic force microscopy (AFM) before and after abrasion tests to analyze carbon coating and its effects on tribological performance. The observations depicted that HHO flowrate needs to be varied as the engine load and CR change, and based on these observations, PWM control unit was designed, manufactured and reprogrammed so as to adjust electrical power consumption of HHO system when needed. The aforementioned analyses ensured optimization of the overall system so as to maximize the efficiency of the test engine. The co-system with optimized parameters (CPR + HHO + PWM) yielded an increase in average brake power (Pe) up to 31%, and average reductions in specific fuel consumption (β), carbon monoxide (CO), unburned hydrocarbon (UHC), and nitrogen oxide (NOx) emissions by 17%, 25%, 19%, and 14%, respectively at engine load range from 20% to 100%. It is expected that this study will be a good guide in terms of developing high-efficient ICEs due to promising results provided by the co-system.

Development of SPARC plasma disruption database through electromagnetic predictive MAXFEA modelling

The evaluation of electromagnetic (EM) behaviour of tokamak components under disruption loads and its correlation with vertical instabilities is critical and characterizing the design considerations. This is particularly crucial for high-field and burning plasma experiments such as the SPARC tokamak, the compact prototype currently under construction by Commonwealth Fusion Systems (CFS). The combination of high toroidal magnetic field (12.2 T) and high plasma current (8.7 MA) makes SPARC less tolerant to disruptions than currently operating research devices. In this context, recent efforts have been made to characterize the disruption loads in SPARC by conducting a series of analyses to estimate the EM response of the SPARC vacuum vessel (VV) during a plasma vertical displacement event (VDE). In this paper, the aforementioned analyses are used as starting point for a comprehensive characterization of VDE scenarios. These analyses are utilized to create a database of predictive disruption simulations, serving as synthetic experiments to generate and collect pertinent data for upcoming research studies. In this context, the paper discusses a preliminary description of an innovative VV synthetic displacements diagnostic, designed starting from disruption database, presented here, and by exploiting the MAXFEA-ANSYS combined use methodology as further step. The disruptive plasma scenarios database will be functional in estimating the displacement and strain fields of SPARC components using a limited set of VV indirect displacement measurements and integral plasma observations, such as plasma current and effective plasma position.

Impact of preprocedural left ventricular systolic function on the safety and durability of percutaneous coronary intervention.

Percutaneous coronary intervention (PCI) is considered less safe in patients with reduced ejection fraction (EF), an impression based on older data. Whether the safety and durability of contemporary PCI are different in patients with reduced EF compared with normal EF patients is unknown. Patients from the BIOFLOW II, IV and V clinical trials were grouped as normal EF (≥50%) and reduced EF (30%-50%). Using multivariable logistic regression and cox proportional hazards regression, we determined relations of EF category with procedural safety (a composite of cardiac death, myocardial infarction, stroke and urgent coronary artery bypass grafting within 30 days of PCI) and target lesion failure (TLF; comprising cardiac death, target vessel myocardial infarction, target vessel revascularization within 1 year of PCI) respectively. In sensitivity analyses, we regrouped patients into EF < 45% and ≥55% and repeated the aforementioned analyses. In 1685 patients with normal EF (mean age 65 years; 27% women; mean EF 61%) and 259 with low EF (mean age 64 years; 17% women; mean EF 41%), 101 safety and 148 TLF events occurred. Compared with patients in the normal EF group, those with reduced EF had neither a statistically significant higher proportion of safety events, nor a higher multivariable-adjusted risk for such events. Similarly, patients with reduced EF and normal EF did not differ in terms of TLF event proportions or multivariable-adjusted risk for TLF. The results were similar in sensitivity analyses with EF groups redefined to create a 10% between-group EF separation. PCI safety and durability outcomes are similar in patients with mild-moderately reduced EF and normal EF.

Deciphering the maternal ancestral lineage of Greek Cypriots, Armenian Cypriots and Maronite Cypriots.

Cyprus was conquered from several populations because of its special geographical location. In this study, 406 unrelated Cypriot samples were tested based on their mitochondrial DNA. In more detail, 185 were Greek Cypriots, 114 Armenian Cypriots and 107 Maronite Cypriots. This is the first time where the mitochondrial DNA of Greek Cypriots, Armenian Cypriots and Maronite Cypriots is compared with the aim of characterizing the maternal ancestry of Cypriots. The control region of the mtDNA is the most informative in terms of studying maternal ancestry and consists of three hypervariable regions (HVS-I, HVS-II, HVS-III). The hypervariable regions can provide important information regarding the maternal ancestor of the tested samples. The entire control region of the mtDNA was used to determine the mitotypes and subsequently the haplogroups of all the Cypriot DNA samples. Based on the aforementioned analyses, Greek Cypriots were found to be genetically closer to Armenian Cypriots, while Greek Cypriots and Armenian Cypriots showed moderate genetic differentiation with Maronite Cypriots. The most prevalent haplogroups among Cypriots were haplogroups H and U, while R0 is common but in different frequencies for Greek Cypriots, Armenian Cypriots and Maronite Cypriots. It is proposed that the maternal ancestor may have originated during the Neolithic period and/or the Bronze age.

Overview of Computational Methods to Predict Flutter in Aircraft

Abstract Aeroelastic flutter is a dynamically complex phenomenon that has adverse and unstable effects on elastic structures. It is crucial to better predict the phenomenon of flutter within the scope of aircraft structures to improve the design of their wings. This review aims to establish fundamental guidelines for flutter analysis across subsonic, transonic, supersonic, and hypersonic flow regimes, providing a thorough overview of established analytical, numerical, and reduced-order models as applicable to each flow regime. The review will shed light on the limitations and missing components within the previous literature on these flow regimes by highlighting the challenges involved in simulating flutter. In addition, popular methods that employ the aforementioned analyses for optimizing wing structures under the effects of flutter—a subject currently garnering significant research attention—are also discussed. Our discussion offers new perspectives that encourage collaborative effort in the area of computational methods for flutter prediction and optimization.

Čo pre Slovákov znamenal a v súčasnosti znamená štátny sviatok Slovenského národného povstania?

Intention of the authors is to analyse the importance of national holiday of the Slovak National Uprising in a context of collective and historical memory of the Slovak nation. The aim of submitted paper is therefore to analyse various lines of opinion on the Slovak National Uprising, especially from the perspective of two totalitarian ideologies, as well as from the perspective of contemporary historiography after 1989. In the context of aforementioned analyses, authors deal with recent and historical interpretations from the historical and collective memory point of view, with an emphasis on legal consequences. The first years of independence and democracy brought to Slovakia re-increase of pro-fascist (ľudák’s) interpretation of history, which, however, did not succeeded in settling down in the collective memory of the Slovak nation. Nevertheless, the last years once again brought relativisation and misinterpretation of historical context of the Slovak National Uprising and its specific standing in society as a national holiday in particular. Reflecting these negative trends, the authors also consider possibilities of effective protection of national holidays, not only with the use of law.

Monitoring Minimal Residual Disease in Patients with Multiple Myeloma By Tracking Serum M-Protein Using Mass Spectrometry

Objective： We performed this retrospective real-world study to evaluate the clinical utility of blood-based mass spectrometry for longitudinal M-protein monitoring during the disease course of patients of multiple myeloma（MM）. Methods：We initially analyzed serial serum samples of 56 patients with MM by clonal peptide MS Method named EasyM MS, then we compared the MS results with those determined using serum immunofixation electrophoresis (IFE),multiparameter flow cytometry (MFC), next-generation flow cytometry (NGF), to establish the concordance between different disease-monitoring protocols. A sensitivity for MFC MRD negativity of at least 1×10 -4 anda sensitivity for NGF MRD negativity of at least 1×10 -5.Additionally, we determined the optimal cutoff value of EasyM MS negativity to satisfy both the sensitivity and specificity between MS and NGF, and explored the prognostic impact of MS-MRD status. Results：The median diagnostic age of 56 NDMM patients was 56 years old, including 34 males (60.7%). There were 38 patients with IgG type MP and 18 patients with IgA type MP. 47 patients (83.9%) chose to receive proteasome inhibitor - based induction therapy, and a total of 44 patients (78.6%) received first-line ASCT treatment.Among 56 MM patients，38 patients with IgG type MP can further identify IgG subclasses through mass spectrometry. Additionally, 56 patients with MM have successfully used MS to identify their unique clonal peptides of MP. 56 patients provided a total of 447 samples of serum and all sequential serum samples were successfully measured by using the MS method. The initial comparison of the performance of blood-based MS-MRD with that of the conventional M-protein diagnostic method IFE used . Among the 397 comparable results collected at similar timepoints, 70.8% (281/397) of the samples were both M-MRD- and IFE-positive, 3.3% (13/397) were both MS-MRD- and IFE-negative, 25.7% (102/397) were MS-MRD-positive and IFE-negative.Flow cytometry MRD was performed on 218 bone marrow aspirates in this cohort, including 126 analyzed with MFC and 92 analyzed with NGF. MS-MRD detection was also performed on comparable serum acquired at the same time points. The sensitivity of MS-MRD was 100.0% in comparison with both MFC and NGF, with specificity of 1.9% and 22.2%, respectively. Considering the overly high sensitivity of EasyM at a cutoff of dotp &amp;gt;0.9, we used the sensitivity of NGF as the standard for detection (i.e., a detection sensitivity ≤10 -5) and set the EasyM MS negative cutoff value using ROC analysis, determined to be &amp;lt;1.86% of that at first diagnosis. Using these assumptions, we again performed the aforementioned analyses comparing the sensitivity and specificity between EasyM and IFE, MFC, and NGF. Under this scenario, the sensitivity of EasyM remained high in comparison with IFE (88.3%), MFC (85.1%), and NGF (93.2%), but the specificity was also greatly increased to 90.4%, 55.8%, and 93.2%, respectively. We then divided patients into MS-positive and MS-negative groups according to whether they achieved MS-MRD negativity, and median PFS and OS were calculated for each group. The median PFS of the MS-positive and MS-negative groups was 20.6 (95% CI: 16.6, 24.6) and 61.8 (39.7, 83.9) months, respectively . The median OS was 55.7 (95% CI:31.8, 79.5) months for the MS-positive group and not reached (NR) for the MS-negative group ,Both mPFS and mOS were significantly longer among the MS-negative group compared with the MS-positive group (both p &amp;lt; 0.001). Conclusion：EasyM exhibits high sensitivity and minimally invasive technological advantages, and also has important significance in efficacy evaluation and prognosis evaluation. In the future, EasyM MS detection can be used as a minimally invasive MRD monitoring method based on peripheral blood in clinical practice.

Micromechanical Models for FDM 3D-Printed Polymers: A Review.

Due to its large number of advantages compared to traditional subtractive manufacturing techniques, additive manufacturing (AM) has gained increasing attention and popularity. Among the most common AM techniques is fused filament fabrication (FFF), usually referred to by its trademarked name: fused deposition modeling (FDM). This is the most efficient technique for manufacturing physical three-dimensional thermoplastics, such that FDM machines are nowadays the most common. Regardless of the 3D-printing methodology, AM techniques involve layer-by-layer deposition. Generally, this layer-wise process introduces anisotropy into the produced parts. The manufacturing procedure creates parts possessing heterogeneities at the micro (usually up to 1 mm) and meso (mm to cm) length scales, such as voids and pores, whose size, shape, and spatial distribution are mainly influenced by the so-called printing process parameters. Therefore, it is crucial to investigate their influence on the mechanical properties of FDM 3D-printed parts. This review starts with the identification of the printing process parameters that are considered to affect the micromechanical composition of FDM 3D-printed polymers. In what follows, their (negative) influence is attributed to characteristic mechanical properties. The remainder of this work reviews the state of the art in geometrical, numerical, and experimental analyses of FDM-printed parts. Finally, conclusions are drawn for each of the aforementioned analyses in view of microstructural modeling.

Prognostic biomarker SYK and its correlation with immune infiltrates in glioma.

The tumor microenvironment (TME) provides excellent conditions for the development of glioma. The present study sought to identify the prognostic factors of glioma that could be used to improve the prognosis of patients with this disease. In the present study, Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data calculations were employed to estimate the ratio of tumor-infiltrating immune cells and the quantity of immune and stromal components in 698 glioma cases from the Cancer Genome Atlas database. In addition, certain differentially expressed genes were studied by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses and single genes associated with prognosis were identified by protein-protein interaction (PPI) network and Cox combined analysis. The immune and stromal scores of the TME were significantly associated with glioma patient survival. By using the PPI network and Cox regression analyses, spleen tyrosine kinase (SYK) was eventually identified as the best prognostic factor for patients with glioma. In addition, Gene Set Enrichment Analysis and CIBERSORT analyses were employed. The former indicated that the high-expression SYK group genes were mainly enriched in immune-related activities. The latter revealed that SYK expression was positively associated with T cell cluster of differentiation 4 memory resting and monocytes. The aforementioned experimental analyses provided the theoretical basis for the biological prediction of SYK. The data indicated that SYK contributed to immune predictors in patients with glioma by facilitating the shift of the TME from immune dominance to metabolic activity. Finally, reverse transcription-quantitative PCR and western blotting were used to verify the single gene expression in glioma cells. This may provide prognostic value for the treatment of glioma.

Procjena šumskih resursa na temelju Sentinel-2 snimki – studija slučaja Derventa, BiH (Katastarska općina Brezici)

The management of forest resources is complicated due to the complete lack of maintenance and disorganization of the land administration and survey that are decades old. Modern, unconventional monitoring systems are used with the aim of improving the existing records systems and creating a clearer insight into the state of forest resources. This study provides an example of the use of one such system, Sentinel-2. Using the R programming language, the multispectral Sentinel-2 images were classified by the Random Forest classification algorithm. Following the completion of the classifications, the accuracy of the classification was evaluated using the error matrix and the Kappa value. An analysis of forest resources for one cadastral municipality was accomplished using classified rasters and data from the Real Estate Cadastre Database. Based on the data analysis, major changes are visible in terms of the abandonment of agricultural land and its conversion into a certain form of forest vegetation. Furthermore, based on these data, the study demonstrates changes that can be monitored in shorter time intervals. Sentinel-2 images can be used to determine forest expansion, based on the aforementioned analyses, resulting in a clearer and better representation of existing forest resources that are unknown due to outdated and unreliable land administration systems.

Performance assessment of a novel solar heliostat and digester-based multigeneration system with hydrogen production

Renewable energy sources have the potential to mitigate environmental risks by replacing fossil fuels. The aim of this study is firstly to consider the use of renewable energy sources to develop a new multigeneration system for meeting the energy demands of residential communities; secondly to analyze the presently developed system using mass, energy, entropy and exergy balance equations; and finally to assess the system's performance in terms of energy and exergy efficiencies. The system uniquely employs an ammonia-water-based triple-effect absorption system with other systems, such as an organic Rankine cycle, a Brayton cycle, and a steam turbine Rankine cycle. The Engineering Equation Solver (EES) software package is used to determine the thermodynamic properties and perform the aforementioned thermodynamic analyses through the constructed codes. The results of this study show that the steam Rankine, Brayton and organic Rankine cycles have energy efficiencies of 35.90%, 19.50%, and 11.49%, respectively, while the exergy analysis reveals that the steam Rankine, Brayton and organic Rankine cycles achieve the corresponding exergy efficiencies of 74.30%, 31.68%, and 23.23%, respectively. Additionally, the energetic and exergetic coefficients of performance (COPs) of the absorption cooling system are determined to be 1.60 and 0.32, respectively.

Survival After Cryotherapy Versus Radiotherapy in Low and Intermediate Risk Localized Prostate Cancer

BackgroundFocal therapy, including cryotherapy, reduces overtreatment in low- and intermediate-risk prostate cancer (PCa) patients with multiple comorbidities, which seems to increase in popularity compared with whole gland treatment. However, there is currently no consensus regarding the medium-term outcomes of cryosurgery as a prospective alternative to radiotherapy (RT) for such patients. Our study aims to find the available evidence that directly compares the medium-term overall survival (OS) and cancer-specific mortality (CSM) outcomes between cryotherapy and RT in patients with low- and intermediated-risk PCa. Materials and MethodUsing the Surveillance, Epidemiology, and End Results (SEER) database, we identified 47,787 patients with low- and intermediate-risk PCa diagnosed between 2004 and 2015, of which 46,853 (98%) received treatment with RT, while only 934 (2.0%) received treatment with cryotherapy. Kaplan–Meier methods were used to estimateOS and cancer-specific survival (CSS) between the 2 groups. We performed multivariable Cox regression analysis to assess overall mortality (OM), while the cumulative incidence function (CIF) was used to illustrate cancer-specific mortality (CSM) and noncancer-specific mortality (non-CSM) for all patients. Additionally, competing risks regression (Fine-Gray) was implemented to evaluate any differences. After propensity score matching (PSM), all the aforementioned analyses were repeated. After the inverse probability of treatment weighting (IPTW), we repeated Kaplan–Meier methods on OS and CSS, and performed multivariable Cox regression analysis to assess OM in cryotherapy versus RT. Sensitivity analyses were conducted by excluding patients who died of cardiovascular disease. ResultsAfter applying 1:4 PSM to the cryotherapy group with the RT group, the resulting RT cohort consisted of 3,736 patients who were matched with 934 patients in the cryotherapy cohort. The 5‐year OS and cumulative CSM rates for PS-matched groups (N = 4670) receiving cryotherapy (N = 934) or RT (N = 3736) were 89% versus 91.8%, 0.65% versus 0.57, respectively. Multivariable Cox regression analysis demonstrated that cryotherapy was associated with a poorer OS outcome compared to RT (hazard ratio [HR] 1.29, 95% confidence interval [CI]: 1.07-1.55, p < .01). Multivariate competing risk regression analysis revealed that both treatments were not associated with CSS, with HR = 1.07 (95% CI: 0.55-2.08, p = .85). IPTW-adjusted analyses showed that the 5-years OS rates were 89.6% versus 91.8% for cryotherapy versus RT, respectively. Multivariate regression analysis for OS demonstrated that cryotherapy was more likely to have inferior OS in comparison to RT (HR = 1.30; 95%CI: 1.09-1.54; p < .01). The outcome of sensitivity analyses indicates that there was no significant difference in OS and CSS between the 2 groups. ConclusionFor low- and intermediate-risk PCa patients treated by cryotherapy or RT, we could not demonstrate a survival difference. Cryotherapy may be a feasible option as a viable alternative to traditional radiation therapy.

Evaluation of a strengthening approach for existing RC buildings in terms of resilience and cost efficiency

The aim of the present study is to assess the performance of a reinforced concrete (RC) building been retrofitted with a new precast insulated steel and concrete composite sandwich wall. The applied retrofitting method, as well as the selection of the performance level for design have been studied with reference to their impact on the retrofit cost and the estimated cost due to possible earthquake losses. As a case study an existing 8-story RC building has been selected that is strengthened with inverted-V steel bracings as well as with the application of the proposed insulated composite walls, respectively. By applying inelastic dynamic analyses with artificial accelerograms scaled to represent earthquake scenarios with specific probabilities of recurrence, the seismic response of the strengthened structure is defined for each scenario. The results of the aforementioned analyses are being correlated with the earthquake losses based on an established methodology. Eventually, the performance of each retrofit method is determined and then compared with the respective performance of the existing building. The most efficient method is defined by considering both the construction cost and the earthquake losses cost of each earthquake scenario.

Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods

Landslides are events that cause great impact in different parts of the world. Their destructive capacity generates loss of life and considerable economic damage. In this research, several Machine Learning (ML) methods were explored to select the most important conditioning factors, in order to evaluate the susceptibility to rotational landslides in a sector surrounding the city of Cuenca (Ecuador) and with them to elaborate landslide susceptibility maps (LSM) by means of ML. The methods implemented to analyze the importance of the conditioning factors checked for multicollinearity (correlation analysis and VIF), and, with an ML-based approach called feature selection, the most important factors were determined based on Classification and Regression Trees (CART), Feature Selection with Random Forests (FS RF), and Boruta and Recursive Feature Elimination (RFE) algorithms. LSMs were implemented with Random Forests (RF) and eXtreme Gradient Boosting (XGBoost) methods considering a landslide inventory updated to 2019 and 15 available conditioning factors (topographic (10), land cover (3), hydrological (1), and geological (1)), from which, based on the results of the aforementioned analyses, the six most important were chosen. The LSM were elaborated considering all available factors and the six most important ones, with the previously mentioned ML methods, and were compared with the result generated by an Artificial Neural Network with resilient backpropagation (ANN rprop-) with six conditioning factors. The results obtained were validated by means of AUC-ROC value and showed a good predictive capacity for all cases, highlighting those obtained with XGBoost, which, in addition to a high AUC value (&gt;0.84), obtained a good degree of coincidence of landslides at high and very high susceptibility levels (&gt;72%). Despite the findings of this research, it is necessary to study in depth the methods applied for the development of future research that will contribute to developing a preventive approach in the study area.