Modern portfolio theory claims that diversification into non-correlated or negatively correlated activities reduces the overall risk of a portfolio. Considering the total income of a bank as a portfolio of interest income and non-interest income, this paper investigates how the variability of interest income and non- interest income, and covariance between interest income and non-interest income influence the various risk factors of banks. We set out a study in the Indian context. We have extracted data for the period 2005-2017 and employed an extended version of Ridge, Lasso and Elastics Net regression to take care of multi-collinearly in our data. We have considered 10-fold cross-validation techniques to get optimal values of tuning parameters for Ridge, Lasso, and Elastics Net regression (which is a convex combination of ridge and the LASSO). We have compared different regression techniques by comparing RMSE and R2. We observe that non-interest income is positively correlated with interest income in the Indian context, but it does stabilize variance, idiosyncratic risk & market risk (Beta) of Indian Banks.

Stage of cancer is a discrete ordinal response that indicates aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with stage of cervical cancer, to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients has been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which is capable of fitting cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P>N, such as for TCGA data. We demonstrate use of this package through application to TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional dataset and effectively performs variable selection.

In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the Variational Quantum Classifier (VQC) and the Quantum Kernel Estimator (QKE). We evaluate the performance of these classifiers on six widely known and publicly available benchmark datasets and analyze how their performance varies with the number of samples on two artificially generated test classification datasets. As quantum machine learning is based on unitary transformations, this paper explores data structures and application fields that could be particularly suitable for quantum advantages. Hereby, we developed a data set based on concepts from quantum mechanics using the exponential map of a Lie algebra. This dataset will be made publicly available and contributes a novel contribution to the empirical evaluation of quantum supremacy. We further compared the performance of VQC and QKE on six widely applicable datasets to contextualize our results.\\ Our results demonstrate that the VQC and QKE perform better than basic machine learning algorithms such as advanced linear regression models (Ridge and Lasso). They do not match the accuracy and runtime performance of sophisticated modern boosting classifiers like XGBoost, LightGBM, or CatBoost. Therefore, we conclude that while quantum machine learning algorithms have the potential to surpass classical machine learning methods in the future, especially when physical quantum infrastructure becomes widely available, they currently lag behind classical approaches. Our investigations also show that classical machine learning approaches have superior performance classifying datasets based on group structures, compared to quantum approaches that particularly use unitary processes.\\ Furthermore, our findings highlight the significant impact of different quantum simulators, feature maps, and quantum circuits on the performance of the employed quantum estimators. This observation emphasizes the need for researchers to provide detailed explanations of their hyperparameter choices for quantum machine learning algorithms, as this aspect is currently overlooked in many studies within the field.\\ To facilitate further research in this area and ensure the transparency of our study, we have made the complete code available in a linked GitHub repository.

This article discusses the results of fluid inclusions study in barite collected at the Jan Mayen vent field area (Troll Wall and Perle & Bruce) and Loki’s Castle vent field of the Mohns Ridge segment of the Arctic Mid-Ocean Ridge. Three mafic-hosted volcanogenic massive sulfide deposits were examined within the active vent fields that adequately correspond to geological settings of ultraslow-spreading ridges and PT-conditions. Hydrothermal sediments were investigated to determine the temperature and salinity of the fluids responsible for barite precipitation. The hydrothermal origin of the barite was confirmed by its morphology. Fluid inclusions are two-phase and homogenized into the liquid phase on heating at temperatures below 287ºC. The salt concentration in fluids trapped in inclusions is 2.6–4.4 wt.% NaCl eq. The crystallization temperatures varied from 276ºС to 119ºС and from 307ºС to 223ºС for the Jan Mayen and Loki’s Castle vent fields, respectively. The data obtained allowed us to confirm evidence of fluid phase separation in the hydrothermal systems and to expand our knowledge of the temperature and salinity of mineral fluids previously known from recent direct measurements during the cruises within the G.O. Sars research vessel. The fluid inclusions data obtained from barites emphasize the fluid features characteristic of volcanogenic massive sulfide deposits, the similarities and differences for the studied hydrothermal sites, and allow comparisons with similar products from other active hydrothermal systems.

The purpose of this study was to evaluate the outcomes of implants placed in horizontally augmented alveolar ridges using porcine bone grafts and to investigate the long-term stability of the porcine bone grafts. A retrospective analysis was conducted on 55 sites that underwent horizontal ridge augmentation using porcine bone grafts and implant placement with a follow-up period longer than 5 years. Furthermore, additional analysis was conducted on 24 sites where porcine bone grafts were used exclusively and implants were placed simultaneously with horizontal ridge augmentation. The mean follow-up period after prosthesis loading was 67.3 months, with a mean marginal bone loss of 0.18 mm at 1 year and a cumulative mean marginal bone loss of 0.37 mm over the entire follow-up period. Of the 55 implants, 2 were lost and 4 did not meet the success criteria, resulting in a survival rate of 96.4% and a success rate of 89.1%. In 24 sites, the mean marginal bone loss was 0.22 mm at 1 year and 0.40 mm at 65.8 months, with 100% survival and success rates. Porcine bone grafts can be successfully used in horizontal ridge augmentation for implant placement in cases of ridges with insufficient horizontal width.

A variable annuity is a popular life insurance product that comes with financial guarantees. Using Monte Carlo simulation to value a large variable annuity portfolio is extremely time-consuming. Metamodeling approaches have been proposed in the literature to speed up the valuation process. In metamodeling, a metamodel is first fitted to a small number of variable annuity contracts and then used to predict the values of all other contracts. However, metamodels that have been investigated in the literature are sophisticated predictive models. In this paper, we investigate the use of linear regression models with interaction effects for the valuation of large variable annuity portfolios. Our numerical results show that linear regression models with interactions are able to produce accurate predictions and can be useful additions to the toolbox of metamodels that insurance companies can use to speed up the valuation of large VA portfolios.

Mediterranean Ridge accretionary complex (MAC) is one of the most critical subduction zones in the world. It is known that the region exhibits a continuous mass change (horizontal/vertical movements). This process is associated with the devastating and tragic earthquakes shaking the MAC for centuries. Here, we investigate the ocean bottom pressure (OBP) anomalies in the MAC derived from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) satellite missions. The OBP time series for the MAC comprises a decreasing trend in addition to 1-, 1.53-, 2.36-, 3.67-, and 9.17-year periodic components partially explained by the atmosphere, oceans, and hydrosphere (AOH) processes, and Earth's pole movement. We noticed that the OBP anomalies appear to link to a rising trend and periods in earthquakes' power time series. This finding sheds new light on the mechanisms controlling the most destructive natural hazard.

Whether resurfacing or not resurfacing the patella during total knee arthroplasty (TKA) still is a challenge to orthopedic surgeons. A significant reason for this controversy is the far from perfect outcomes of both techniques, resulting from inadequate knowledge of normal patellar dimensions in a diseased one. The primary purpose of the current study is to find the pre-diseased patellar dimensions and the ethnic differences in patellar dimensions. We measured the patella's dimensions on 927 normal young adult knee MRIs from seven different ethnicities. Besides comparing the dimensions between sexes, ages, and sides, we analyzed the differences among ethnic groups. The average thickness was 25.12±2.33 mm; the average width was 44.57±4.32 mm, the average articular surface length was 32.69±3.75 mm, with significant gender, age, and ethnic differences. There were also significant differences in dimensions among ethnic groups, except for between the Indians and Far Eastern Asians and between the Arabs and North Africans. We could also find a robust mathematical relationship between the patella's width, length, and thickness. The ethnic differences in patellar dimensions found in this study can help optimize surgical technique and implant designs for patellar resurfacing. The mathematical equation will help the surgeons find the normal, pre-diseased patella thickness to prevent over-or under-stuffing during the patellar resurfacing.

Viruses are the most abundant biological entities in aquatic ecosystems and harbor an enormous genetic diversity. While their great influence on the marine ecosystems is widely acknowledged, current information about their diversity remains scarce. A viral metagenomic analysis of three water samples was conducted from sites on the South Scotia Ridge (SSR) near the Antarctic Peninsula, during the austral summer 2016. The taxonomic composition and diversity of the viral communities were investigated and a functional assessment of the sequences was determined. Phylotypic analysis showed that most viruses belonging to the order Caudovirales, especially the family Podoviridae (41.92-48.7%), similar to the viromes from the Pacific Ocean. Functional analysis revealed a relatively high frequency of phage-associated and metabolism genes. Phylogenetic analyses of phage TerL and Capsid_NCLDV (nucleocytoplasmic large DNA viruses) marker genes indicated that many sequences associated with Caudovirales and NCLDV were novel and distinct from known phage genomes. High Phaeocystis globosa virus virophage (Pgvv) signatures were found in SSR area and complete and partial Pgvv-like were obtained which may have an influence on host-virus interactions. Our study expands the existing knowledge of viral communities and their diversities from the Antarctic region and provides basic data for further exploring polar microbiomes.

Spatial regression models have garnered significant attention across several disciplines, including functional magnetic resonance imaging analysis, econometrics, home price analysis, and many other domains. The phenomenon of sparsity is often found in nature, when a limited number of factors contribute significantly to the overall variation. Spatial regression models frequently use sparsity to indicate less complex computational and more superficial covariance structures. The spatial error model is a significant spatial regression model that focuses on the geographical dependence present in the error terms rather than the response variable. This study proposes an effective approach for estimating the vector of regression coefficients in the spatial error model, taking into consideration of the prior knowledge that some coefficients are insignificant and there is multicollinearity among the regressors. It also introduces pretest and shrinkage ridge estimators for spatial error regression models, evaluating their performance compared to traditional maximum likelihood estimators. It also assesses their efficacy using real-world data and bootstrapping techniques for comparison purposes.

As the largest energy consumer and carbon emitter, China has made substantial efforts to improve energy efficiency for decrease energy consumption, while the energy rebound effect determines its effectiveness. The embodied energy consumption of construction projects accounted for nearly one-sixth of the total economy's energy consumption in China. This paper is based on the logical relationship among capital input, technological progress, economic growth, and energy consumption, adapting an alternative estimation model to estimate the energy rebound effect for the construction industry in China for the first time. Empirical results in our paper reveal that the energy rebound effect for the construction industry in China is about 59.5% for the period of 1990–2014. The results indicate that the energy rebound effect does exist in China’s construction industry and it presented a fluctuating declining trend. This implies that half of the energy savings by technological progress is achieved. In addition, China’s government should implement proper energy pricing reforms and energy taxes to promote the sustainable development of China’s construction industry.

The electrocardiogram (ECG) was the first biomedical signal where digital signal processing techniques were extensively applied. By its own nature, the ECG is typically a sparse signal, composed of regular activations (the QRS complexes and other waveforms, like the P and T waves) and periods of inactivity (corresponding to isoelectric intervals, like the PQ or ST segments), plus noise and interferences. In this work, we describe an efficient method to construct an overcomplete and multi-scale dictionary for sparse ECG representation using waveforms recorded from real-world patients. Unlike most existing methods (which require multiple alternative iterations of the dictionary learning and sparse representation stages), the proposed approach learns the dictionary first, and then applies an efficient sparse inference algorithm to model the signal using the learnt dictionary. As a result, our method is much more efficient from a computational point of view than other existing methods, thus becoming amenable to deal with long recordings from multiple patients. Regarding the dictionary construction, we locate first all the QRS complexes in the training database, then we compute a single average waveform per patient, and finally we select the most representative waveforms (using a correlation-based approach) as the basic atoms that will be resampled to construct the multi-scale dictionary. Simulations on real-world records from Physionet's PTB database show the good performance of the proposed approach.

The compositions of metalliferous sediments which is associated with hydrothermal vents can provide reliable geochemical indicators for the exploration of seafloor sulfides. In this study, surface sediments from the Xunmei hydrothermal field (HF) on the South Mid-Atlantic Ridge (SMAR) were selected, and their major and trace elements were analysed. The results showed that the sediments are composed of pelagic material (calcium biogenic components), basaltic debris, iron-manganese oxides, and a mixture of hydrothermal components. Additionally, the sediments were found to be significantly enriched in Cu, Zn, Fe and Co derived from hydrothermal sources, as well as Mn, V, Mo, U and P, which are primarily scavenged from seawater. The Cu and Zn concentrations are highest in the northeastern part of Xunmei, while the northeastern, northern, and southern parts are characterized by great inputs of hydrothermal Fe. Manganese and Mo are mainly enriched in the western and southern parts and show a strong positive correlation, indicating that Mo is mainly scavenged by Mn oxides. There are high positive correlations among U, P, and Fe, indicating their coprecipitation with Fe from hydrothermal plumes. Vanadium and Co are introduced into sediments in two ways, i.e., V is scavenged and coprecipitates from hydrothermal plumes, and Co is derived from sulfide debris. Based on the variations in Cu and Zn contents and in the Cu/Fe (0.159), Zn/Fe (0.158), and Fe/Mn (1440) ratios, it can be inferred that a high-temperature hydrothermal vent existed in northeastern Xunmei. In combination with the distribution patterns of the above elements, the hydrothermal vents in the southern part ceased erupting after a short period of activity. In addition, the high Mn anomaly and the high U/Fe ratios at the boundaries of the investigated area indicate the presence of a relatively oxidized environment in southwestern Xunmei.

Fe- and Al-rich metapelite from the Transangarian segment of the Yenisey Ridge (East Siberia, Russia) is a potential new source of high-alumina refractories. The rocks have relatively high average contents of Al2O3 (20 wt.%) and Fe2O3 (7.91 wt%), moderate K2O (3.44 wt%), and low CaO (0.74 wt%). Their dominant mineral assemblages are andalusite + muscovite + margarite + chlorite + biotite + quartz or staurolite + kyanite or/and andalusite + chlorite + muscovite + biotite + quartz with ±garnet and ±plagioclase. Al2SiO5 polymorphs occur as up to 1.5 cm andalusite porphyroblasts and partial or complete pseudomorphs after andalusite (kyanite and staurolite). Accessories include abundant Fe-Ti oxides and sporadic REE-, Y-, Ca-phosphates; sulfides are negligible. The composition of Al2SiO5 concentrates obtained in laboratory by heavy-media and magnetic separation from ≥0.06 mm fractions meet all requirements for raw material of this type: >56 wt% Al2O3, <42 wt% SiO2, <1 wt% Fe2O3, <1.2 wt% TiO2, and <0.2 wt% (CaO+MgO). The andalusite, kyanite, and mixed ores yield 0.7-4.1 wt%, 0.7-2.2 wt%, and 1.9-6.0 wt% of concentrate, respectively. The best-quality ores rich in Al2SiO5 polymorphs reside in zones of contact and/or dynamic metamorphism superimposed over regional metamorphism of Al-rich rocks.

Detecting cracks in teeth is a clinical challenge. Patients may complain of diffuse pain on chewing, pain, at times, on temperature change and pain that occurs episodically. Common diagnostic tools such as radiographs and visual examination may not detect cracks. This case report shows how PTR-LUM in The Canary System can detect cracks in teeth not seen with other devices. In this clinical situation, the crack involved a portion of the mesial and distal surfaces of a mandibular second molar.

Semi-continuous data are very common in the social science and economics. In this paper, a Bayesian variable selection procedure is developed to assess the influence of exogenous factors including observed and unobserved on the semi-continuous data. Our formulation is based on the two-part latent variable model with polytomous response. We consider two schemes for the penalties of regression coefficients and factor loadings: the Bayesian spike and slab bimodal prior and the Bayesian lasso prior. Within the Bayesian framework, we implement Markov Chains Monte Carlo sampling method to conduct posterior inference. To facilitate posterior sampling, we recast the logistic model in part one as the norm-like mixture model. Gibbs sampler is designed to draw observations from the posterior. Our empirical results show that with suitable hyper-parameters, the spike and slab bimodal method slightly outperforms the Bayesian lasso in the current analysis. Finally, a real example related to the China household finance survey is analyzed to illustrate the application of the methodology.. .

Background: Atypical teratoid/rhabdoid tumor (AT/RT) is an uncommon and aggressive pediatric central nervous system neoplasm. However, a universal clinical consensus or reliable prognostic evaluation system for this malignancy is lacking. Our study aimed to develop a risk model based on comprehensive clinical data to assist in clinical decision-making. Methods: We conducted a retrospective study by examining data from the Surveillance, Epidemiology, and End Results (SEER) repository, spanning 2000 to 2019. The external validation cohort was sourced from the Children's Hospital Affiliated to Chongqing Medical University, China. To discern independent factors affecting overall survival (OS) and cancer-specific survival (CSS), we applied Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression analyses. Based on these factors, we structured nomogram survival predictions and initiated a dynamic online risk-evaluation system. To contrast survival outcomes among diverse treatments, we used propensity score matching (PSM) methodology. Molecular data with the most common mutations in AT/RT were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. Results: The annual incidence of AT/RT showed an increasing trend (APC, 2.86%; 95% CI:0.75-5.01). Our prognostic study included 316 SEER database participants and 27 external validation patients. The entire group had a median OS of 18 months (range 11.5 to 24 months) and median CSS of 21 months (range 11.7 to 29.2). Evaluations involving C-statistics, DCA, and ROC analysis underscored the distinctive capabilities of our prediction model. An analysis via PSM highlighted that individuals undergoing triple therapy (integrating surgery, radiotherapy, and chemotherapy) had discernibly enhanced OS and CSS. The most common mutations of AT/RT identified in the COSMIC database were SMARCB1, BRAF, SMARCA4, NF2, and NRAS. Conclusions: In this study, we devised a predictive model that effectively gauges the prognosis of AT/RT and briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges.

In a recent study, the PD-1 inhibitor has been widely used in clinical trials and shown to improve various cancers. However, PD-1/PD-L1 inhibitors showed a low response rate and showed to be effective for a small number of cancer patients. Thus, it is important to identify key genes, which can enhance the PD-1/PD-L1 response for promoting immunotherapy. Here, we used ssGSEA and unsupervised clustering analysis to identify three clusters to show different immune cell infiltration status, prognosis, and biological action. The cluster C showed a better survival rate, high immune cells infiltration, and immunotherapy effect enriched in a variety of immune active pathways, including T and B cell signal receptors. Besides, it showed more immune subtypes C2 and C3. Further, we used WGCNA analysis to confirm the cluster C correlated genes. The red module highly correlated with cluster C for 111 genes which were enriched in a variety of immune-related pathways. To pick candidate genes in SD/PD and CR/PR patients, we used the Least Absolute Shrinkage and SVM-RFE algorithms. In conclusion, our LASSO analysis and SVM-RFE based research identified targets with better prognosis, activated immune-related pathways, and better immunotherapy. The KLRC3 was identified as the key gene which can efficiently respond to immunotherapy with greater efficacy and better prognosis.

The ultramafic-hosted Kairei vent field, located at 25°19′S, 70°02′E towards the northern end of the segment 1 of the Central Indian Ridge (CIR-S1) in a water depth of ~2450 m. This study aims to investigate the distribution of trace elements among sulfides of differing textures, and discuss the possible factors controlling the trace element distribution in those minerals by using LA-ICP-MS spot analyses as well as line scans. Our results show that there are distinct systematic trace element distributions throughout the different minerals：(1) Pyrite is divided into three types at the Kairei, including early-stage euhedral pyrites (py-I), sub-euhedral pyrites (py-II), and colloform pyrites (py-III). Pyrite is generally enriched in Mo, Au, As, Tl, Mn, and U. py-I have higher contents of Se, Te, Bi, and Ni, py-II are enriched in Au relative to py-I and py-III, but poor in Ni, py-III are enriched in Mo, Pb, and U but are poor in Se, Te, Bi, and Au. Variations in the concentrations of Se, Te, and Bi in pyrite are likely governed by the strong temperature gradient. Ni is generally lower than Co in pyrites, indicates that our samples precipitated at a high-temperature condition, whereas the extreme Co enrichment is likely from a magmatic heat source combined serpentinization reactions underlie the deposits. (2) Chalcopyrite is characterized by high concentrations of Co, Se, Te. The abundant of Se and Te in chalcopyrite cause by the high solubilities of Se and Te incorporated into chalcopyrite lattice at high temperature fluids. The concentration of Sb, As and Au is relatively low in chalcopyrite from the Kairei vent field. (3) Sphalerite from both the Zn-rich chimney is characterized by high concentrations of Sn, Co, Ga, Ge, Ag, Pb, Sb, As, and Cd, but depleted in Se, Te, Bi, Mo, Au, Ni, Tl, Mn, Ba, V, and U in comparison with the other minerals. The high concentration of Cd and Co is likely caused by the substitution of Cd2+ and Co2+ for Zn2+ in sphalerite. A high concentration of Pb accompanied by high Ag concentration in sphalerite indicating the Ag occurs in the microinclusions of Pb-bearing minerals such as galena. Au is generally low in sphalerite and strong correlate with Pb suggesting its presence in the microinclusions of galena. The strong correlation of As with Ge in sphalerite from Kairei suggests that they might precipitate under medium- to low-temperature with moderately reduced conditions. (4) Bornite-digenite is very low in most trace elements, except for Co, Se, and Bi. The high concentration of Se and Bi in all the sulfide minerals was observed in bornite-digenite can be explained by abundant Bi-selenide inclusions. Serpentinization in ultramafic-hosted hydrothermal systems might play an important role on Au enrichment in pyrite with low As contents. Compared with felsic-hosted seafloor massive sulfide (SMS) deposits, sulfide minerals from the ultramafic-hosted deposits show higher concentrations of Se and Te, but lower As, Sb, and Au concentrations attributed to the contribution of magmatic volatile input. Significant Se enrichment in chalcopyrite has been found from mafic-hosted SMSs indicate that the primary factor that controls the Se enrichment is its temperature-controlled mobility in fluids.

Cropland area is closely related to food production. More previous focuses were paid on impacts of extreme events on food production, but less on cropland dynamics. This study took the Farming-Pastoral Ecotone of Northern China (FPEN) as a case area, to investigate its cropland area dynamics and driving factors in view of perspectives of extreme events, environmental conditions, socioeconomic development, urban expansion, and ecological construction. We used ridge regression approach to quantify contributions of these drivers to cropland area dynamics. Results showed that cropland area increased significantly at a rate of 333.5km2/a during 1992−2020 and were spatially clustered in east of the FPEN. Impact extent and size each driving factor on cropland trend presented large spatiotemporal differences, but ecological construction (EC) had overall the greatest impact on cropland area changes, followed by urban expansion (UE). In comparison, TL10p has the smallest. UE-dominated areas increased 41.9% since 2010s, but still less than EC. Furthermore, we found that extreme events effects on cropland area trend evidently increased. Particularly, TH90p displayed the most increase (~99.4%). Cropland area changes dominated by extreme temperature events in 2010−2020 increased nearly six times than that in 1992−2010. These findings suggest that increasing impacts of extreme weather events on cropland area changes should be cautioned.

The ridge-furrow plastic mulching (RFPM) system and nitrogen application have been effective strategies for improving crop productivity in the Loess Plateau of China. However, it is not clear how the ridge-furrow ratio and nitrogen fertilizer type (NT) affect the utilization of water, nitrogen, heat and radiation resources for increased rain-fed wheat production. Two nitrogen fertilizer types [Traditional urea (TU) and controlled-release urea (CRU)] and four planting patterns [conventional flat planting (F) and the RFPM system of 20 cm ridge with 40 cm furrow (R2F4), 40 cm ridge with 40 cm furrow (R4F4), and 60 cm ridge with 40 cm furrow (R6F4)] were tested from September, 2018 to June, 2021 during winter wheat growing season. The results showed that the RFPM system can increase the soil thermal time (TTsoil) by 41.0–152.1°C d compared to F. The thermal effect of the RFPM system shortens the vegetative growth period and extends the reproductive growth period by 2–7 days, promoting the increase of the leaf area index (LAI) and final dry matter (DM) accumulation. These significantly increased the grain yield (GY) in the RFPM system by 51.6–115.2% and enhanced the thermal time use efficiency (TUE) by 48–99.5%, water productivity (WP) by 37.4–76.3%, radiation use efficiency (RUE) by 16.3–34.4%, and partial factor productivity of nitrogen (PFPN) by 51.6–115.2% compared to F. Although a high ridge-furrow ratio combined with CRU increased GY and resources use efficiency, it also exacerbated the soil water depletion, especially in the 40–140 cm soil layer. Overall, applying CRU in combination with the RFPM system of 40 cm ridge with 40 cm furrow maximized resources use efficiency and increased wheat production in the Loess Plateau of China.

The redox properties of quinones underlie their unique characteristics as organic battery components that outperform the conventional inorganic ones. Furthermore, the redox properties could be precisely shaped by using different substituent groups. Machine learning and statistics, onthe other hand, have proven to be very powerful approaches for efficient in silico design of novel materials. Herein, we demonstrated the machine learning approach for the prediction of the redox activity of quinones that potentially can serve as organic battery components. For the needs of the present study, a database of small quinone-derived molecules was created. A large number of quantum chemical and chemometrics descriptors was generated for each molecule and subsequently different statistical approaches were applied to select the descriptors that most prominently characterize the relationship between structure and redox-potential. Various machine-learning methods for screening of prospective organic battery electrode materials were deployed to select the most trustworthy strategy for machine learning aided design of organic redox materials. It was found that ridge regression models perform better than regression decision tree and decision tree based ensemble algorithms.

This paper discusses the tectonics of the St. Peter and St. Paul Archipelago (SPSPA) in the Equato-rial Atlantic Ocean, based on the joint-system geometry which show a North-South shorten-ing/transpressional uplift tectonism, is active leading to exhumation of the sub-oceanic mantle. These islets are the summits of a sigmoidal submarine ridge formed by mantle ultramafic rocks. The ridge is crossed by the principal transform deformation zone of the northern transform fault of the St. Paul Multifault System. The South flank ridge exposes serpentinized mantle perido-tites, while the North flank exposes strongly deformed/fractured ultramylonites, recording duc-tile and brittle deformation at lithospheric conditions. The SPSPA show multiple joint systems cutting mylonitic foliation of the exposed rocks, forming three main families: high-angle paral-lel joints of tectonic origin, serpentinization-related joints with random direction and load-release low-angle parallel joints. The tectonic joints show an average direction of N31°E and N28°W, forming a conjugate system with a N1ºW compression axes, coherent with a trans-pressive stress field. Accordingly, the earthquakes focal mechanism close to the islets also shows N-S compression. The previously reported active uplift with an average rate of 1.5 mm/year and the directions of the joint system here reported agreeing with a present-day active N-S compres-sive field at a high angle with the direction of the transform fault.

The dependence of the width of annual rings of woody plants on the characteristics of the snow cover is analyzed in various natural zones of the West Siberian Plain and adjacent mountainous areas: the maximum thickness and water reserve for the entire winter period and for individual months, the dates of disappearing, establishment, and duration of the occurrence of a stable snow cover. It has been shown that the role of the thickness and water content of snow cover for the radial growth of trees is differentiated by geographical location. On the plain, it intensifies in the forest-tundra and dry steppe. The response of radial growth to snow cover in the upper and lower parts of the forest belt is often opposite. Dates of establishment of stable snow cover are more important for tree growth compared to dates of disappearance. Dates of disappearance of stable snow cover are more significant in the southern regions than in the northern ones. The value of the duration of the period with stable snow cover for tree growth is higher in the southern regions.

Alveolar bone atrophy after tooth extraction presents significant difficulties when planning delayed-immediate implant treatment to replace a lost tooth. Socket augmentation is an effective therapy that limits the post extraction volumetric changes and facilitates implant placement whilst reducing the need for staged grafting. However, socket augmentation and osteoconductive graft conversion is a slow process that could take 4-6 months. PRF and its variants have significant bio-enhancement effect on wound healing and regeneration through release of platelet drived growth factors, bioactive substances, cytokines. Leukocytes in PRF also play a significant role in enhancing tissue regeneration as part of osteo-immune response. Use of PRF during socket augmentation is highly effective in enhancing extraction site healing and graft integration by angiogenesis, cell proliferation, recruitment, and differentiation through release of growth factors and bioactive substances mediated by platelets and leukocytes. PRF with SA is recommended as an adjunct to SA as a sole biomaterial or in combination with xenograft or allograft bone substitutes to enhance tissue regeneration and to reduce risk of infection, osteitis and postoperative pain in extraction sockets