Examining the similarity of event environments or surroundings, more precisely settings, provides additional insight in analyzing event sequences as it provides information about the context and potential common factors that may have influenced them. This article proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been studied. While modeling event setting sequences we consider spatial and temporal scales to define the bounds of the setting and incorporates dynamic variables alongside static variables. Using a matrix-based representation and an extended Jaccard index we developed new similarity measures that allow for the use of all variable data types. We successfully used these similarity measures coupled with other multivariate statistical analysis approaches in a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, which validate the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. In conclusion, these similarity measures have many potential real-world applications, and offer researchers a powerful tool for understanding different factors and their dynamics corresponding to occurrences of spatiotemporal event sequences.

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method to classify spatiotemporal processes based on entropy measurements. To test and validate the method, we selected four classes of similar processes related to the evolution of random patterns: dynamic colored noises ((i)~white and (ii)~red); (iii)~weak turbulence from reaction-diffusion; (iv)~hydrodynamic fully developed turbulence, and (v)~plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon Permutation Entropy ($S^p_H$) and a new approach based on Tsallis Spectral Permutation Entropy ($S^s_q$). Notably, our observations reveal the segregation of reaction terms in this $S^p_H \times S^s_q$ space, a result that identifies specific sectors for each class of dynamic process, and can be used to train machine learning models for automatic classification of complex spatiotemporal patterns.

The identification of spatiotemporal heterogeneity, its key determinants, and the interaction effects between the driving factors of urban resilience (UR) within and between subregions is fundamental for understanding its underlying mechanisms. A resilience evaluation model was applied to analyze the temporal and spatial differences in UR in Hunan Province, China. The standard deviation ellipse was used to explore the spatial and dynamic evolution and direction of UR. A hot spot analysis identified clusters of cold and hot spots. The contribution of spatiotemporal differences in UR within and between subregions was assessed using the Theil index. A geodetector analysis determined the factors influencing UR and their interactions. There was an increasing trend in UR from 0.2692 in 2014 to 0.3422 in 2022. The number of cities with high resilience gradually increased from 2014 to 2022, while there was a decreasing gradient in UR from northeast to southwest across the province. High-resilience cities had positive spillover effects on the surrounding area. Hot spots were predominantly located in the northeast, while cold spots were concentrated in the southwest. The barycenter of UR shifted from northeast to southwest by 2018, before moving southeast by 2022. The Theil index values declined over time both within and between subregions. Per capita GDP, average wages of on-the-job employees, per capita social consumption, and doctor density enhanced UR. Two-factor interactions had a greater influence on the spatiotemporal heterogeneity of UR than single factors. Two-factor and nonlinear enhancements were identified as the primary mode of influence.

The surface velocity of the Amery Ice Shelf (AIS) is vital to assessing its stability and mass balance. Previous studies have shown that the AIS basin has a stable multi-year average surface velocity. However, spatiotemporal variations in the surface velocity of the AIS and the underlying physical mechanism remain poorly understood. This study combined offset tracking and DInSAR methods to extract the monthly average surface velocity of the AIS and obtained the inter-annual surface velocity from the ITS_LIVE product. An uneven spatial distribution in inter-annual variation of the surface velocity was observed between 2000–2022, although the magnitude of variation was small at less than 20.5 m/yr. The increase and decrease in surface velocity on the eastern and western-central sides of the AIS, respectively, could be attributed to the change in the thickness of the AIS. There was clear seasonal variation in monthly average surface velocity at the eastern side of the AIS between 2017–2021, which could be attributed to variations in the area and thickness of fast-ice and also to variations in ocean temperature. This study suggested that changes in fast-ice and ocean temperature are the main factors driving spatiotemporal variation in the surface velocity of the AIS.

It has been shown that Human papillomavirus (HPV) infection is associated with several forms of cancer. Additionally, although there are more than 100 types of HPV, more than ten types of it, including types 16 and 18, are considered high-risk. The two proteins that make up the viral capsid of HPV 16 are L1, the major capsid protein, and L2, the minor capsid protein. FDA (The Food and Drug Administration) approved HPV vaccines mostly target L1 and L2 capsid proteins, which facilitate intracellular entry during infection. In this study, it was set out to analyze the creation of phylogenetic relations of L1 nucleic acid sequences of viral isolates. This study also analyzed sequences of three key elements within the amino acid sequences of HPV 16 L1: loops critical for structural stability, nuclear localization signal sequences, and residues implicated in viral attachment. As highlighted by the literature, these elements represent pivotal aspects of viral function. Furthermore, the report highlighted the particular inter-sequence variability within the HPV 16 L1 protein sequences. Overall analysis revealed clustering of sequences primarily due to geographical and characterized by more phylogenetic relatedness within location-specific clusters.

Mobile health technologies enable continuous, quantitative assessment of mobility and gait in real-world environments, facilitating early diagnosis of gait disorders, disease progression monitoring, and prediction of adverse events like falls. Traditionally, mobile gait assessment predominantly relied on body-fixed sensors positioned at the feet or lower trunk. Here, we investigate the potential of an algorithm utilizing an ear-worn motion sensor for spatiotemporal segmentation of gait patterns. We collected 3D acceleration profiles from the ear-worn sensor during varied walking speeds in 53 healthy adults. Temporal convolutional networks were trained to detect stepping sequences and predict spatial relations between steps. The resulting algorithm, mEar, accurately detects initial and final ground contacts (F1 score of 99% and 91%, respectively). It enables determination of temporal and spatial gait cycle characteristics (among others stride time and stride length) with good to excellent validity at a precision sufficient to monitor clinically relevant changes in walking speed, stride-to-stride variability, and side asymmetry. This study highlights the ear as a viable site for monitoring gait and proposes its potential integration with in-ear vital sign monitoring. Such integration offers a practical approach to comprehensive health monitoring and telemedical applications, by integrating multiple sensors in a single anatomical location.

China's cotton production has shown a concentration to the advantageous regions in recent years. Based on the relevant data of cotton production in China, the study measures the cotton production agglomeration levels of 16 major cotton-producing provinces from 1978 to 2020, summarizes the spatiotemporal evolution characteristics of cotton production agglomeration, and analyzes the factors influencing cotton production agglomeration using spatial econometric models. The results indicate that: Firstly, since the opening-up and reform, China's cotton production agglomeration levels have shown an overall upward trend, exhibiting periodic dynamic changes over time, and the spatial pattern reveals a dynamic regional gradient. Secondly, there is spatial correlation among the production scales of cotton in different provinces, with a trend resembling a "U" shape, and the agglomeration form shows a low-level convergence. Thirdly, technological level, policies, substitute industrial products and foreign trade are identified as important factors. The cotton production in each province is spatially correlated through these factors, and there is also a presence of spillover effects. In conclusion, the government should prioritize considerations of various influencing factors to formulate tailored support policies, promote large-scale production to enhance technological levels, and focus on policies aimed at improving the quality of cotton.

Historic expeditions are events that are flavored by exploratory, scientific, military or geographic characteristics. Such events are often documented in literature, journey notes or personal diaries. A typical historic expedition involves multiple site visits and their descriptions contain spatiotemporal and attributive contexts. Expeditions involve movements in space that can be represented by triplet features (location, time and description). However, such features are implicit and innate parts of textual documents. Extracting the geospatial information from these documents requires understanding the contextualized entities in the text. To this end, we developed a semi-automated framework that has multiple Information Retrieval and Natural Language Processing components to extract the spatiotemporal information from a two-volumes historic expedition gazetteer. Our framework has three basic components, namely, the Text Preprocessor, the Gazetteer Processing Machine and the JAPE (Java Annotation Pattern Engine) Transducer. We used the Brazilian Ornithological Gazetteer as an experimental dataset and extracted the spatial and temporal entities from entries that refer to three expeditioners’ site visits and mapped the trajectory of each expedition using the extracted information. Finally, one of the mapped trajectories was manually compared with a historical reference map of that expedition to assess the reliability of our framework. The reference map was manually prepared in previous research work by others.

Climate change is challenging the agricultural sector worldwide. The pressure exerted by the altered thermal conditions drives the zonal shift for various cultivations. This study presents the multiyear analysis of the Growing Degree Days (GDD) index over the Northern Mediterranean Basin (NMB), focused on the high-value vine cultivation derived from the E-OBS dataset. To handle this extensive environmental dataset, R-language scripts and packages were applied, along with the capabilities of the GIS software. The investigated time period spans from 1969 to 2018, and the performed analysis indicates a broad shift/expansion in areas of GDD exceeding 2000 heat units. The extracted results specify the viticultural areas where the GDD index surpasses the suitable values for vine cultivation, owing to the increase of the thermal stress risk. In addition to mapping areas with GDD ≥ 2000, spatial calculations of the surface of areas meeting this criterion per country in the study area were made. In the northern parts of the NMB, the rise of the GDD index values indicates the establishment of vine plantations in the future.

The study investigated the application of Wastewater-Based Epidemiology (WBE) as a tool for monitoring SARS-CoV-2 prevalence in a city in Northern Italy from October 2021 to May 2023. Starting from a widely applied deterministic model, this study proposed a variation to take into account population characteristics and virus biodegradation in the sewer network. The model calculated virus loads and corresponding COVID-19 cases over time in different areas of the city and was validated with healthcare data while considering viral mutations, vaccinations, and testing variability. The correlation between the predicted and reported cases was high across the three waves that occurred in the considered period, demonstrating the model’s capacity to predict relevant fluctuations in the number of cases. Population characteristics did not substantially influence the predicted and reported infection rates. Conversely, biodegradation significantly reduced the virus load reaching the wastewater treatment plant, resulting in a 30% reduction in the total virus load produced in the study area. This approach can be applied for comparing virus load values across cities with varying population demographics and sewer network structures, enhancing the comparability of WBE data for effective surveillance and intervention strategies.

Relationships between the Farey sequence and the Riemann hypothesis other than the Franel-Landau theorem are discussed. Whether a function similar to Chebyshev’s second function is square-root close to a line having a slope different from 1 is discussed. The nontrivial zeros of the Riemann zeta function can be used to approximate many functions in analytic number theory. For example, it could be said that the nontrival zeta function zeros and the Möbius function generate in essence the same function - the Mertens function. A different approach is to start with a sequence that is analogous to the nontrivial zeros of the zeta function and follow the same procedure with both this sequence and the nontrivial zeros of the zeta function to generate in essence the same function. A procedure for generating such a function is given.

In Southeast Asia, the incidence of Leukemia, a malignant blood cancer originating from hema-topoietic progenitor cells, is on the rise, marked by a concerning 54% mortality rate. This study focuses on enhancing early-stage prediction to improve patient recovery prospects significantly. Leveraging Machine Learning and Data Science, we employ protein sequential data from frequently mutated genes such as BCL2, HSP90, PARP, and RB to predict Chronic Myeloid Leukemia (CML). Our approach relies on robust feature extraction techniques, namely Di-peptide Composition (DPC), Amino Acid Composition (AAC), and Pseudo amino acid composition (Pse-AAC), with prior attention to addressing outliers and validating feature selection through the Pearson Corre-lation Coefficient. Data augmentation ensures a well-rounded dataset for analysis. Employing a range of Machine Learning models, including Support Vector Machine (SVM), XGBoost, Random Forest (RF), K Nearest Neighbor (KNN), Decision Tree (DT), and Logistic Regression (LR), we achieve accuracy rates spanning from 66% to 94%. These classifiers undergo comprehensive as-sessment using performance metrics such as accuracy, sensitivity, specificity, F1-score, and the confusion matrix. Our proposed solution, encompassing a user-friendly web application dashboard, presents an invaluable tool for early CML diagnosis with profound implications for practitioners, offering a deploy-able asset within healthcare institutions and hospitals.

Direct Energy Deposition (DED) is a versatile and efficient method in metal additive manufacturing. However, humping, caused by abnormal dynamics in the melt pool (MP), poses a significant threat to the geometric integrity of manufactured products. Current state-of-the-art (SOTA) methods primarily detect humping by analyzing late-stage spatial abnormalities, such as MP detachment. This approach is fundamentally reactive, leading to a tendency to miss early humping spatiotemporal dynamics, like cyclic elongation of the MP. This study introduces a novel, proactive indicator named VIMPS (Variability of Instantaneous MP Solidification-Front Speed), a physics-based tool designed to quantify early abnormal fluctuations in MP solidification speed. The experiments demonstrate VIMPS correlation with humping-induced geometric inaccuracies. By capturing early spatiotemporal dynamics of the MP, VIMPS reduces detection latency by 30 seconds compared to existing SOTAs that focus solely on spatial abnormalities. This significant improvement transforms detection from reactive to proactive, providing the time needed for corrective actions to enhance the overall productivity and quality of the DED process.

The generation of arithmetic sequences involves arithmetic operations. However, is it possible to generate such sequences without being subject to any arithmetic or related operations, all by just hand? The answer may be negative, as the very rule defining these sequences itself relies on arithmetic operations. Nonetheless, this paper presents a few results demonstrating that the exact sequences of $an$, where, $a \in \{ 2,3,\cdots,9,10\}$ and $n \in \mathbb N \cup \{0\}$, can indeed be generated up to any desired number of terms without relying on any arithmetic operations and others derived from such. This is done by the formation of a specialized table that leverages numerical patterns and the notion of concatenation.

Spectral characteristics of CDOM in water column are a key parameter for bio-optical modeling. Knowledge of CDOM optical properties and spatial discrepancy based on the relationship between water quality and spectral parameters in Yinma River watershed with in situ data collected highly-polluted waters are exhibited in this study. Seasonal field data sets collected over a period of 2 months in 2015 in Yinma River Watershed. Based on the comprehensive index method, the riverine waters showed serious contamination, especially the COD, Fe, Mn, Hg and DO were out of range contamination warning. Dissolved organic carbon (DOC) and total suspended matter (TSM) with prominent non-homogenizing were significantly high in the riverine waters, but chlorophyll-a (Chl-a) was opposite. Ternary phase diagram showed that non-algal paritcles absorption played an important role in total non-water light absorption (>50%) in most sampling locations, and mean contribution of CDOM were 13% and 22% in summer and autumn respectively. Analysis of ratio of absorption at 250-365 nm (E_250:365) and spectral slope (S_275-295) indicated that CDOM had higher aromaticity and molecular weight in autumn than in summer, is consistent with the results of water quality and relative contribution. Redundancy analysis (RDA) indicated that the environmental variables OSM had a strong correlation with CDOM absorption, followed by heavy metal, e.g., Mn, Hg and Cr⁶⁺. However, for the specific UV absorbance (SUVA₂₅₄), the seasonal values showed opposite results compared with the reported literature. The potential reasons were the more UDOM (uncolored Dissolved Organic Matter) from human source (wastewater effluent) existed in waters. Terrigenous inputs simultaneously are in relation to the a_CDOM(440)-DOC relationship with the correlation coefficient was 0.90 in summer (2-tailed, p<0.01), and 0.58 in autumn (2-tailed, p<0.05). Spatial distribution of CDOM parameters exhibited that the downstream regions focused on dry land have high CDOM molecular weight and aromatic hydrocarbon. Partial sampling locations around the cities or countries generally showed abnormal values due to terrigenous inputs. As a bio-optical model parameter, spectral characteristic of CDOM is helpful in adjusting the derived algorithms in highly-polluted environments. The study on organic carbon and pollutants in highly-polluted waters had an important contribution to global carbon balance estimation and water environment protection.

Quantifying the landscape pattern change can effectively demonstrate the ecological progresses and the consequences of urbanization. Based on remotely sensed land cover data in 1994, 2000, 2006 and a gradient analysis with landscape metrics at landscape- and class- level, we attempted to characterize the individual and entire landscape patterns of Shanghai metropolitan during the rapid urbanization. We highlighted that a roadscape transect approach that combined the buffer zone method and the transect-based approach was introduced to describe the urban-rural patterns of agricultural, residential, green, industrial, and public facilities land along the railway route. Our results of landscape metrics showed significant spatiotemporal patterns and gradient variations along the transect. The urban growth pattern in two time spans conform to the hypothesis for diffusion–coalescence processes, implying that the railway is adaptive as a gradient element to analyze the landscape patterns with urbanization. As the natural landscape was replaced by urban landscape gradually, the urban fringe expanded radically. The results also showed that the desakota region expanded its extent widely. Satellite towns witnessed the continual transformation from the predominantly rural landscape to peri-urban landscape. Furthermore, the gap between urban and rural areas remained large especially in public service. More reasonable urban plans and land use policies should push to make more of an effort to transition from the urban-rural separation to coordinated urban-rural development. This study is a meaningful trial in demonstrating a new form of urban–rural transects to study the landscape change of large cities from a strategic viewpoint. By combining gradient analysis with landscape metrics, we addressed the process of urbanization both spatially and temporally, and provided a more quantitative approach to urban studies.

In this paper, we define Tetranacci and Tetranacci-Lucas matrix sequences and investigate their properties.

Digital gait monitoring is increasingly used to assess locomotion and fall risk. The aim of this work is analyzing the changes in the foot-floor contact sequences of Parkinson’s Disease (PD) patients in the year following the implant for deep brain stimulation (DBS). During their best-ON condition, 30 PD patients underwent gait analysis at baseline (T0), at 3 months (T1), and 12 months (T2) after the DBS neurosurgery for bilateral high-frequency subthalamic nucleus stimulation. Thirty age-matched controls underwent gait analysis once. Each subject was equipped with bilateral foot-switches and a 5-minute walk was recorded, including both straight-line and turnings. The walking speed, turning time, stride time variability, percentage of atypical gait cycles, stance, swing, and double support duration were estimated. Overall, the gait performance of PD patients improved after DBS, as also confirmed by the decrease in the UPDRS-III score from 19.4±1.8 to 10.2±1.0 points (T0 vs. T2) (p < 0.001). In particular, the percentage change of atypical gait cycles of PD more affected side decreased at T1 (Straigh-line: -72%; Turnings: -43%) and at T2 (Straigh-line: -54%; Turnings: -20%). The percentage of atypical gait cycles proved an informative digital biomarker for quantifying PD gait changes after DBS, both in straight-line paths and turnings.

Unquestionably the rapidly changing climate and, therefore, alterations in the associated bioclimate, constitute an alarming reality with implications in daily practice and natural capital management. In this research the present and projected bioclimate evolution over the phytogeographical regions of Greece is displayed. For this purpose, ultrahigh-resolution computation results on the spatial distribution of the Emberger index’s Q2 classes of bioclimatic characterisation are analysed and illustrated for the first time. The assessments are performed over the reference period (1970–2000) and two future time frames (2021–2040; 2041–2060) under the RCP4.5 and RCP8.5 emission scenarios. By 2060 and under the extreme RCP8.5, intense xerothermic trends are demonstrated owing to the resulting significant spatial evolution mainly of the Arid-Hot, Semi-Arid-Very Hot, Semi-Arid-Hot and Semi-Arid-Temperate Q2 classes, respectively, over the phytogeographical regions of Kiklades (up to 29% occupation), Kriti and Karpathos (up to 30%), West Aegean Islands (up to 26%), North East (up to 56%)–North Central (up to 31%). The RCP8.5 long-term period exhibits the strongest impacts over approximately the right half of the Greek territory the bioclimate of which appears as more dry-thermal in the future.

Early diagnosis of paroxysmal atrial fibrillation (PAF) could suggest patients to receive timely interventions in clinical practice. Various PAF onset prediction algorithms might benefit from accurate heart rate variability (HRV) analysis and machine learning classification but to be challenged in real-time monitoring scenarios. The aim of this study is to present an end-to-end deep learning based PAFNet model that integrates a sliding window technique on raw R-R interval of electrocardiogram (ECG) segments to achieve a real-time prediction of PAF onsets. This integration enable the deep convolutional neural network (CNN) to be customized as a light-weight architecture that accommodates the size of sliding windows simply by altering the input layer, specifically its effectiveness in making a new prediction with each new heartbeat. Catering to potential influence of input sizes, three CNN models were trained using 50, 100, and 200 R-R intervals, respectively. For each model, the performance of automated algorithms was evaluated for PAF prediction using a ten-fold cross-validation. As a results, a total of 56,381 PAFN-type and 56,900 N-type R-R interval segments was collected from publicly accessible ECG databases, and a promising prediction performance of the automated algorithm with a 100 R-R intervals was achieved in terms of the sensitivity of 97.12%, the specificity of 97.77%, and the accuracy of 97.45%, respectively. Importantly, the automated algorithm with a sliding windows step of 1 could process one sample in only 23.1 milliseconds and identify the onset of PAF at least 45 minutes in advance. The present results suggest that the sliding window technique on raw R-R interval sequences along by deep learning based algorithms may offers the possibility of providing an accurate, real-time, and end-to-end clinical tool for mass monitoring of PAF.

Let an odd integer \(\mathcal{X}\) be expressed as $\left\{\sum\limits_{M > m} b_M 2^M\right\} + 2^m - 1,$ where $b_M \in \{0, 1\}$ and $2^m - 1$ is referred to as the Governor. In Collatz-type functions, a high index Governor is eventually reduced to $2^1 - 1$. For the $3\mathcal{Z} + 1$ sequence, the Governor occurring in the Trivial cycle is $2^1 - 1$, while for the $5\mathcal{Z} + 1$ sequence, the Trivial Governors are $2^2 - 1$ and $2^1 - 1$. Therefore, in these specific sequences, the Collatz function reduces the Governor $2^m - 1$ to the Trivial Governor $2^{\mathcal{T}} - 1$. Once this Trivial Governor is reached, it can evolve to a higher index Governor through interactions with other terms. This feature allows $\mathcal{X}$ to reappear in a Collatz-type sequence, since $2^m - 1 = 2^{m - 1} + \cdots + 2^{\mathcal{T} + 1} + 2^{\mathcal{T}} + (2^{\mathcal{T}} - 1).$ Thus, if $\mathcal{X}$ reappears, at least one odd ancestor of $\left\{\sum\limits_{M > m} b_M 2^M\right\} + 2^{m - 1} + \cdots + 2^{\mathcal{T} + 1} + 2^{\mathcal{T}} + (2^{\mathcal{T}} - 1)$ must have the Governor $2^m - 1$. Ancestor mapping shows that all odd ancestors of $\mathcal{X}$ have the Trivial Governor for the respective Collatz sequence. This implies that odd integers that repeat in the $3\mathcal{Z} + 1$ sequence have the Governor $2^1 - 1$, while those forming a repeating cycle in the $5\mathcal{Z} + 1$ sequence have either $2^2 - 1$ or $2^1 - 1$ as the Governor. Successor mapping for the $3\mathcal{Z} + 1$ sequence further indicates that there are no auxiliary cycles, as the Trivial Governor is always transformed into a different index Governor. Similarly, successor mapping for the $5\mathcal{Z} + 1$ sequence reveals that the smallest odd integers forming an auxiliary cycle are smaller than $2^5$. Finally, attempts to identify integers that diverge for the $3\mathcal{Z} + 1$ sequence suggest that no such integers exist.

Let $S=\left(s_1, \ldots, s_n\right)$ be a finite sequence of integers. Then $S$ is a Gilbreath sequence of length $n$, $S\in\mathbb{G}_n$, iff $s_1$ is even or odd and $s_2, \ldots, s_n$ are respectively odd or even and $\min\mathbb{K}_{\left(s_1, \ldots, s_m\right)}\leq s_{m+1}\leq\max\mathbb{K}_{\left(s_1, \ldots, s_m\right)}\forall m\in\left[\left.1, n\right)\right.$. This, applied to the order sequence of prime number $P$, defines Gilbreath polynomials and two integer sequences A347924 \cite{oeisA347924} and A347925 \cite{oeisA347925} which are used to prove that Gilbreath conjecture $GC$ is implied by $p_n-2^{n-1}\leqslant\mathcal{P}_{n-1}\left(1\right)$ where $\mathcal{P}_{n-1}\left(1\right)$ is the $n-1$-th Gilbreath polynomial at 1.

Recombinant laboratory plasmids (RLPs) are common in biological research and freely shared among academic research laboratories (ARLs), a practice required by many research funding agencies. However, the generation of accurate, reproducible results in experiments utilizing RLPs can be hampered by a lack of accompanying sequence information and metadata. This culture of RLP sharing without knowledge of sequence or etiology is accepted by publishers, not regulated by governments, and outside the realm of bio-industry. In addition, no centralized infrastructure currently exists to collate such data, which at the moment is fragmented across companies, non-profits, and governments and thus is not easily accessed or enacted toward threat assessment. The ubiquity, free exchange, and dual-use risk of RLPs exemplifies a biosecurity threat and elevates the need to characterize their composition to facilitate improved biorisk management by the academic community. A number of common sense solutions are available to create a culture that addresses the biosecurity gap posed by RLP sharing. Culture shift in RLP management will require new norms, effective data management for collation of RLP sequences and metadata, and an incentive structure that encourages sequencing by stakeholders. The next generation of researchers must initiate and champion this shift with support from funding agencies and endorsement from governments and international organizations. Coordination of efforts and stakeholders will require international public/private collaboration, a structure that will be critical to ensure widespread utility as well as the ability of lower-resourced partners to participate, contribute, and benefit.

In this letter, we advocate that it is possible to mitigate Pilot Contamination in Massive MIMO systems by scrambling the pilot sequences with a Base Station (BS) scrambling sequence. It is possible if a set of sequences is carefully designed to meet the orthogonality property defined in this letter. Each BS possesses its own scrambling sequence that can be reused the same way frequency reuse is applied to cell deployment. The main advantage of the prosed pilot generation scheme is that the frequency reuse factor can be set to 1, the most aggressive one, while the scrambling sequences can be reused with much less aggressive reuse factors (e.g. 4, 7, 9, 12, etc.), which in consequence results in pilot contamination mitigation and increased system's performance.

We obtain two generalizations of a known theorem of A. Alam and M. Imdad (Fixed Point Theory Appl. 17 (2015) 693–702) showing that some standard proofs can be obtained involving only Cauchy sequences of the successive approximations instead of the usual successive approximations sequences. Suitable examples prove the effective generalization of our results in metric spaces not necessarily complete.

We developed a novel approach in the field of spatiotemporal modelling, based on the spatialisation of time: the Timescape algorithm. It is especially aimed at sparsely distributed datasets in ecological research, whose spatial and temporal variability is strongly entangled. The algorithm is based on the definition of a spatiotemporal distance that incorporates a causality constraint and that is capable of accommodating the seasonal behaviour of the modelled variable as well. The actual modelling is conducted exploiting any established spatial interpolation technique, substituting the ordinary spatial distance with our Timescape distance, thus sorting, from the same input set of observations, those causally related to each estimated value at a given site and time. The notion of causality is expressed topologically and it has to be tuned for each particular case. The Timescape algorithm originates from the field of stable isotopes spatial modelling (isoscapes), but in principle it can be used to model any real scalar random field distribution.

Education setting evolved from historical open learning system to traditional classroom set-up to distance learning modality. Teaching-Learning practice is transformed with an evolution of teaching-learning materials. With technological advancement in progressive manner and it’s increasing use in academic setting, distance learning has been the on-demand and on-debate topic in current educational discourse. Comparatively fresh topic in Nepali academic setting, this paper intended to analyze the perception of Nepali students towards online modality in Nepali academic setting. This paper further analyzed the student’s preference towards distance learning in current Nepali academic setting. Research findings were analyzed based on data collected through literature review, interview with students and professor and quantitative data collection through use of google form. Study identified opportunities as revenue generation; continuation of academic career from any part of country; increase learning outcome among jobholders. Study identified challenges as unequal access and quality of internet facilities; affordability of laptops/computers; limited interaction; and frequent disturbances. Seeing the better prospects, study strongly supported the need of shift in academic shift from traditional setting to non-traditional setting in Nepali context to meet the global needs of competitive and quality education.

Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven a promising method for fast SOM content estimation. However, soil physical properties significantly affect the sensitivity of satellite hyperspectral imaging to SOM, leading to poor generalization ability of the estimation model. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on the spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root mean squared height, RMSH), and soil bulk weight (SBW), a soil spectral correction strategy was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPD) exceeding 1.4 in almost all bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, root mean square error (RMSE) of 5.74 g/kg, and RPD of 1.68. The prediction accuracy, R2, RMSE, and RPD of the model after migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction strategy based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. The performance comparison highlighted the advantages of considering soil physical properties in regional-scale SOM prediction.

Employees’ organizational commitment is considered to be a critical issue in higher educational setting for the success of its visions and goals, as well as to keep its employees motivation granted for achieving better work performance. This subject has therefore, been studied so as to draw attention to enhance effectiveness of higher educational institution in Ethiopia, particularly in reference to Haramaya University. The major objective of study was to find out the level of academic staffs’ commitment. The study assessed whether significant difference exist in academic staffs’ commitment level in terms of their gender and level of education. Researchers used cross-sectional research design and both primary and secondary data sources were used for the study; 242 participants were selected using stratified sampling technique. Questionnaire, focus group discussion and document review were used to collect data. The quantitative data were analyzed using descriptive and inferential statistics; and the qualitative data were also analyzed using narration methods of analysis. Study result showed there was moderate level of in overall commitment and in specific commitment dimensions among academic staffs. The study further indicated that there is no significance difference in commitment of academic staffs with reference to gender; however, a significant difference in commitment was existed in reference to level of education.

This study aims at providing a climatological analysis of annual and long-term variability of dry periods in the Polish Carpathians in the period of 35 years between 1986 and 2020. Aside days with precipitation, dry periods, namely sequences of days with no precipitation, constitute an inherent characteristic of the climate. The input data for the research was formed by daily precipitation totals from 17 meteorological stations acquired from the Institute of Meteorology and Water Management - National Research Institute, and T. Niedźwiedź catalogue of circulation types over southern Poland. Frequency of dry periods of different duration has been presented. The incidence of long-term periods has been related to the atmospheric circulation. An analysis was performed regarding the change of the average quantity and average length of dry sequences in the 35-year period covered. At most stations, there were no statistically significant trends with respect to the analysed dry day characteristics. This is an obvious result of high variability year-to-year, which means that exclusively large changes may achieve statistical significance. Dry days have been evidenced to related to circulation types. A vast majority of dry days is related to anti-cyclonal systems, particularly the anti-cyclonal wedge (Ka) and western anti-cyclonal (Wa) situations.

The early elongation events in transcription or translational control in the N-terminal of genes are assuming critical roles in their heterologous expression. The secretory expression of the alkaline pectin lyase APL in B. subtilis were studied to explore the effects of the N-terminal 5-7 amino acids sequences of different signal peptides on gene transcription and protein secretion in this study. The first 5 amino acid sequences of the N-terminal of the signal peptide LipA were identified to play an important role in promoting the expression of APL for the first time. Meanwhile, it also revealed that the signal peptide LipA could lead to higher secretory expression than other signal peptides not because of its sequence structure associated with secretion is stronger but because the N-terminal sequences showed greater advantages in gene transcription. At this basis, the recombinant strain structed in this work showed a new record for the highest extracellular yields (12 295 U/ml) of APL in B. subtilis which was 1.9 times higher than that expressed in the recombinant Escherichia coli strain we had reported before. The novel theories revealed in this study would assuming significance roles for improving expression of foreign proteins in or out of the cells.

In this study, it was analyzed that the characteristics of ammonia and their effects on secondary particulate matter (PM) formation by region in Korea during 2020. The NH3 concentration was high in GJ (11.4 ppb) neighboring agricultural area followed by DJ (9.0 ppb) and SE (8.6 ppb) locating in the urban area. On the other hand, BI (2.6 ppb) and JI (4.5 ppb), which are background regions, were lower than other area. Seasonally, ammonia was high in spring and summer, and it generally increased when human activities are active. Therefore, it is believed that ammonia in the atmosphere changes depending on not only local emissions but also temperature-dependent phase distribution characteristics. For SE and GJ, regions with relatively high ammonia concentrations, the effect of ammonia on secondary PM formation was conducted. In both regions, the ammonium-to-sulfate mole ratio tended to increase with an ammonia or PM2.5 concentration increasing. It can be assumed that PM2.5 concentration increases as nitrates are formed under the ammonia sufficient condition. The adjusted gas ratio is generally greater than 4, indicating that there is a lot of free ammonia. Thus, it is estimated that reduction of ammonia would not effective to restrain the nitrate formation.

In this paper, it is proved that s-iteration with error and Picard-Mann iteration with error iterative processes converge strongly to the unique fixed point of Lipschitzian strongly pseudo-contractive map. This convergence is almost -stable and -stable. Applications of these results to the operator equations, where is a strongly accretive and accretive mappings of into itself.

B chromosomes are supernumerary chromosomes which are found in addition to the normal standard chromosomes (A chromosomes). B chromosomes are well known to accumulate several types of repeats. Although the evolution of B chromosomes has been subject of numerous studies, the mechanisms of accumulation and evolution of repetitive sequences is not fully understood. Recently, new genomic approaches have shed light on the origin and accumulation of different classes of repetitive sequences in the process of B chromosome formation and evolution. Here we discuss the impact of repetitive sequences accumulation on the evolution of plant B chromosomes.

Eye movements support memory encoding by binding distinct elements of the visual world into coherent representations. However, the role of eye movements in memory retrieval is less clear. We propose that eye movements play a functional role in retrieval by reinstating the encoding context. By overtly shifting attention in a manner that broadly recapitulates the spatial locations and temporal order of encoded content, eye movements facilitate access to, and reactivation of, associated details. Such mnemonic gaze reinstatement may be obligatorily recruited when task demands exceed cognitive resources, as is often observed in older adults. We review research linking gaze reinstatement to retrieval, describe the neural integration between the oculomotor and memory systems, and discuss implications for models of oculomotor control, memory, and aging.

On the analyzed section of 2.4 km of the national road No. 50 of the Młodzieszyn bypass, it was carried out by a consortium of OAT and SAT companies in the period April-September 2019, under constant traffic in the field of repairing the concrete pavement by replacing slabs, levelling them and sealing expansion joints, roughening and roadside reinforcements.

Background and Objectives To compare navigator-gated three-dimensional T1-weighted gradient-echo sequences (NG-3D T1W-GRE) with standard breath-hold T1W-GRE (BH T1W-GRE) for the detection of HCC nodules in patients with chronic viral hepatitis which undergo at magnetic resonance imaging (MRI) using gadoxetic acid. Methods and materials We evaluated 58 patients with hepatic nodules at least 1 cm in diameter detected by ultrasound who underwent liver MRI including NG-3D T1W-GRE sequences and breath hold T1-weighted gradient-echo sequences with different flip angles(FA) at 10-25-40° after contrast administration of gadoxetic acid at magnetic scanner operating at1.5 Tesla. Image quality and relative hepatic and nodular enhancement were evaluated by three blinded reviewers. Results In these patients, image quality of NG-3D T1W-GRE was assessed significantly better than quality of BHT1W- GRE (P < 0.005) and relative enhancement was significantly higher than that of BH- T1W-GRE ( P

Total hip arthroplasty (THA) is a preferred treatment for primary osteoarthritis (OA) or secondary due to developmental hip dysplasia (DDH). Gait analysis is considered a golden standard for evaluating post-arthroplasty walking patterns. This study compared post-THA spatiotemporal gait parameters (SGPs) between OA and DDH patients and explored correlations with demographic and clinical variables. Thirty patients (15 per group) were recorded during gait, and their SGPs were analyzed. Functionality was evaluated with the Oxford Hip Score (OHS). The OA patients were significantly older than DDH (pr< 0.51, all p< 0.05). The preoperative gait alterations associated with OA or DDH were improved to similar levels in both groups after THA. Post-arthroplasty SGPs and OHS correlations indicate limitations in certain activities. These find-ings must be correlated with kinetic gait analysis data to fully evaluate gait and functionality dif-ferences among patient groups.

Optical motion capture systems are prone to the errors connected with markers recognition – occlusion, leaving the scene or mislabelling – all these errors are then corrected in the software, but still, the process is not perfect, resulting in artifact distortions. In the article, we examine four existing types of artifacts, then propose the method for detection and classification of the distortions. The algorithm is based on the derivative analysis, low-pass filtering, mathematical morphology and loose predictor. The tests involved multiple simulations using synthetically distorted sequences, comparison of performance to the human operators on real life data and applicability analysis for the distortion removal.

The commercialization of historic streets constitutes a pivotal aspect of urban cultural heritage, and the comprehension of their visual perception serves as a valuable asset to urban planning and cultural conservation efforts. However, current research concerning the disparities in visual perception among diverse demographics within historic streets, as well as their spatiotemporal dynamics, remains insufficient. This study aims to utilize mobile eye-tracking technology to delve deeply into the visual perception characteristics of diverse demographics within commercialized historic streets, while also analyzing their spatiotemporal dynamics. We explore the disparities in visual perception of historic streets among groups with varying degrees of architectural knowledge and professional literacy. Through meticulous observation using mobile eye-tracking technology, we elucidate the visual perception patterns of different demographic characteristics through statistical analysis and spatiotemporal modeling. The findings reveal significant discrepancies in visual attention and perception among various groups. Participants with a higher understanding of architecture tend to focus more on architectural details and structures within the streetscape compared to those with a lower understanding, who exhibit greater interest in elements such as shops and signage. Moreover, during the process of perceiving the streetscape, participants’ eye movements transition from surrounding landscapes and historical edifices upon initial entry to the district, gradually shifting towards the commercial activities and distinctive architecture within the district. This study delves deeply into the visual perception characteristics and spatiotemporal dynamics of commercialized historic streets, providing invaluable insights for urban planning and cultural preservation endeavors. By revealing the impact of different demographic characteristics on the visual perception of historic streets, it offers new insights for urban planning and cultural heritage conservation.

China's economic development has shifted from high speed to high quality. This study constructs an evaluation index for China’s high-quality development utilizing a new development concept. The entropy method and cluster analysis were used to assess the level of high-quality development in China between 2015 and 2021. Kernel density estimation method was employed to explore spatiotemporal dynamics. The results reveal significant differences in the level of economic development among provinces in China; the eastern region is significantly better than the central, northeastern, and western regions. From the perspective of the sub-item evaluation, challenges with uneven, uncoordinated, and insufficient economic development exist in various Chinese provinces.

Protein molecules show varying degrees of flexibility throughout their three-dimensional structures. This flexibility is associated with the fluctuation of the torsion angle in proteins. The protein backbone can be defined mainly by two torsion angles, phi (φ) and psi (ψ). The fluctuation of torsion angles is derived from different NMR models’ variations in backbone torsion angles. The angle fluctuations in the cartesian coordinate space are used to characterize the protein’s structural flexibility. Fluctuation of torsion angle is useful in predicting protein function and structure when the torsion angles are used as restraints. This study aims to develop a machine learning method to predict torsion angle fluctuations directly from protein sequences. It explores various useful features such as disorder probability, position-specific scoring matrix profiles, secondary structure probabilities, monograms, bigram, position-specific estimated energy, half-sphere exposures, etc. Likewise, it explores well-known machine learning methods and proposes an optimized Light Gradient Boosting Machine Regressor (LightGBM) method, named TAFPred, to predict torsion angle fluctuations with the selected features. The proposed method achieves ten-fold cross-validated correlation coefficients of 0.746 and 0.737 and mean absolute errors of 0.114 and 0.123 for the angle fluctuation of φ and ψ, respectively, and attains an improvement of 10.08% in MAE, 24.83% in PCC in the phi angle, and 9.93% in MAE, 22.37% in PCC in psi angle compared to the state-of-the-art method proposed by Zhang et al.

The purpose of this study is to provide an accurate formula for calculating entropy for short DNA sequences and to demonstrate how to use it to examine leukemia patient surviving. We used IDIBAPS leukemia patient’s data base with 117 anonymized records. The generalized form of the Robust Entropy Estimator (EnRE) for short DNA sequences was proposed and key EnRE futures was showed. The Survival Analysis has been done using statistical package IBM SPSS. Entropy EnRE were calculated for leukemia patients for two samples: A. 2 groups divided by median EnRE and B. 2 groups of patients were formed according to their belonging to 1st and 4th quartiles of EnRE. The result of survival analysis are statistically significant: A. p &lt; 0.05; B. p &lt; 0.005. The death hazard for a patient with EnRE below median is 1.556 times that of a patient with EnRE over median and that the death hazard for a patient of 1st quartile (lowest EnRE) is 2.143 times that of a patient of 4th quartile (highest EnRE). The transition from median to quartile patients’ groups with more EnRE differentiation confirmed the unique significance of the entropy of DNA sequences for leukemia patients surviving.

In this paper, we introduce and study the notion of rough I₂-lacunary statistical convergence of double sequences in normed linear spaces. We also introduce the notion of rough I₂-lacunary statistical limit set of a double sequence and discuss about some properties of this set.

The relationship between tissue effects, intensity (I), and voltage (V) of a high-frequency alternative current seems well known. However, the translation of the settings of the electrosurgical unit and their use during surgery seems not to be unanimously understood by all surgeons. This results in many variations of surgical technique and plenty of specific questions asked during live surgery. Above 200 V, the heat of the sparks results in the explosion of superficial cells and a cutting effect. With higher voltages, the associated coagulation is increasing. Voltage and amount of energy (Watt=I*V) are independent since a similar amount of energy can be obtained with a continuous current or an intermittent current half the time and a double voltage. Below 200 V, tissue is heated until the steam from boiling tissue stops the current. Therefore, slower heating with less energy or a larger contact area permits deeper coagulation. The maximal output (W) of the electrosurgical unit results in versatility of use besides being a safety measure. The current intensity increases with the area of contact. Therefore, when I*V exceeds the preset output, V has to drop. A limited maximal output thus permits using the same instrument for non-contact cutting with >200V and sparks and coagulation when the contact area is much larger. In conclusion, the tissue effects of electrosurgery vary with the device’s voltage setting (often indicated as force) and the maximal output. The sur-geon’s choice to cut or coagulate by changing the contact area adds versatility.

An information-theoretic data mining method is employed to analyze categorical spatiotemporal Geographic Information System land use data. Reconstructability Analysis (RA) is a maximum-entropy-based data modeling methodology that works exclusively with discrete data such as those in the National Land Cover Database (NLCD). The NLCD are organized into a spatial (raster) grid, and are available in consistent format for every five years from 2001 to 2021. An NLCD tool reports how much change occurred for each category of land use; for the study area examined the most dynamic class is Evergreen Forest (EFO), so the presence or absence of EFO in 2021 was chosen as the dependent variable that our data modeling attempts to predict. RA predicts the outcome with approximately 80% accuracy using a sparse set of cells from a spacetime data cube consisting of neighboring lagged-time cells. When the predicting cells are all Shrubs &amp; Grasses there is a high probability for a 2021 state of EFO, while when the predicting cells are all EFO, there is a high probability that the 2021 state will be not-EFO. These findings are interpreted as detecting forest clear-cut cycles that show up in the data, and explain why this class is so dynamic. This study introduces a new approach to analyzing GIS categorical data and expands the range of applications that this entropy-based methodology can successfully model.

Global positioning systems (GPS) have recently been shown to reliably quantify the spatiotemporal characteristics of Polo, with the physiological demands of Polo play at low and high goal levels also investigated. This study aimed to describe the spatiotemporal demands of Polo across 0 – 24 goal levels. A player worn GPS unit was used to quantify distance, speed and high intensity activities performed. Data was divided into chukkas and five equine-based speed zones, grouped per cumulative player handicap and assessed using standardised mean differences. Average distance and speed per chukka increased in accordance with cumulative player handicap, with the magnitude of differences being Trivial – Large and Trivial – Very Large, respectively. Differences between time spent in speed zones 4 and 5 show a linear increase in magnitude, when comparing 0 goal Polo to all other levels of play (Small – Very Large; 6 – 24 goals, respectively). High intensity activities predominantly shared this trend, displaying Trivial – Large differences between levels. These findings highlight the increasingly demanding cardiovascular, anaerobic and speed-based needs of Polo ponies as playing level increases. Strategies such as high intensity interval training, maximal speed work and aerobic conditioning may be warranted to facilitate this development and improve pony welfare and performance.

As the world’s second-largest economy, China has undergone rapid industrialization and urbanization, leading to high energy consumption and significant carbon emissions. This development has exacerbated conflicts between human-land relations and environmental conservation, contributing to global warming and urban air pollution, both of which pose serious health risks. This study utilizes nighttime light (NTL) data from 2005 to 2019, combined with scaling techniques and statistical analysis, to estimate city-scale energy carbon emissions over a 15-year period. Our focus is on Northeast China, a traditional industrial region comprising 36 cities across three provinces, rich in natural resources but now facing severe aging challenges. We analyzed the spatial patterns of energy carbon emissions, while spatiotemporal evolution was assessed through spatial autocorrelation and dynamic changes. These dynamic changes were further evaluated using standard deviation ellipse (SDE) parameters and SLOPE values. Additionally, we examined the conflict between city-scale emissions and economic growth using the Tapio decoupling index. Our findings for the 36 cities over the 15-year period include: (1) Heilongjiang shows low emissions with a downward trend; Jilin shows overall improvement; Liaoning has high and steadily increasing emissions. (2) The global spatial autocorrelation of energy carbon emissions is significant, with a positive Moran’s I, while significant local Moran’s I clusters are concentrated in Heilongjiang and Liaoning. (3) The SDE shows the greatest changes in city-scale emissions occurred in 2015, followed by 2019, 2005, and 2010. (4) Emission growth across the 36 cities is fastest in Heilongjiang, followed by Liaoning and Jilin. (5) Tapio decoupling analysis shows overall positive decoupling in Heilongjiang, a decline in decoupling quality in Jilin, and no significant change in Liaoning. Finally, we recommend strategies to reduce emissions from the perspectives of government, industry, and residents. This research offers a quantitative foundation for achieving dual carbon goals and supports the implementation of policies focused on energy transition and sustainable urban planning.

We are in the era where various processes need to be online. However, data from digital learning platforms are still underutilised in higher education, yet, they contain student learning patterns, whose awareness would contribute to educational development. This limits development of adaptive teaching and learning mechanisms. In this paper, a model for data exploitation to dynamically study students progress is proposed. Variables to determine current students progress are defined and are used to group students into different clusters. K-means clustering is performed on real data consisting of students from a South African tertiary institution. Cluster migration is analysed and the corresponding learning patterns are revealed.

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Un-derstanding vegetation dynamics and its response to climate change and human activities provides essential information for ecological and environmental resource management. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine plat-form and Landsat satellite images, employing trend analysis and other methods to examine spati-otemporal changes in vegetation in the SRB. Additionally, partial correlation and residual analysis were utilized to explore the response of FVC to climate change and human activities. The main results were as follows: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10−3 y–1. The area within 1 km of roads experienced a higher increase of 3 × 10−3 y–1, while the roadless areas experienced a lower increase of 1.1 × 10−3 y–1. Road construction from 2004 to 2006 significantly reduced the FVC within 200 meters of the roads. FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that FVC has a strong positive correlation with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. There is a weak negative correlation between FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) Compared to climate factors, human activities have a greater impact on vegetation dynamics in the SRB. The contributions of climate change to vegetation recovery are increasing. (4) The area changes of dif-ferent land types are closely related to climate factors and human activities, reflecting their impact on vegetation. These results provide valuable information for ecosystem restoration and environ-mental protection in the SRB.

Abstract: Antimicrobial peptides (AMPs) are short peptides, usually made up of less than 100 amino acid residues. They are found in many living organisms and are an important factor in their innate immune systems. AMPs can be extracted from various living sources including bacteria, plants, animals and even humans. They are usually cationic peptides with an amphiphilic structure, which allows them to easily bind and interact with the cellular membranes of viruses, bacteria, fungi and other pathogens. They can act against both Gram-negative and Gram-positive pathogens and have various modes of action against them. Some attack the pathogens membrane, while others target their intracellular organelles as well as their nucleic acids, proteins and metabolic pathways. A crucial area of use of AMPs is in their ability to help with emerging antibiotic resistance: some AMPs are active against resistant strains and are susceptible to peptide engineering. This review considers AMPs from three key sources: plants, animals and humans, as well as their modes of action and some AMPs sequences.

One of creators of quantum mechanics P. Jordan in his work on quantum biology claimed that life's missing laws were the rules of chance and probability of the quantum world. The article presents author’s results of studying frequencies (or probabilities) of nucleotides on so-called epi-chains of long DNA sequences of various eukaryotic and prokaryotic genomes. DNA epi-chains are algorithmically constructed subsequencies of DNA nucleotide sequences. According to the algorithm of construction of any epi-chain of the order n, the epi-chain is such nucleotide subsequence, in which the numerations of adjacent nucleotides differ by natural number n (n = 1, 2, 3, 4,…). Correspondingly each epi-chain of order n ≥ 2 contains n times less nucleotides than the original DNA sequence. The presented results unexpectedly discover that in long single-stranded and double-stranded DNA of any tested genome its DNA epi-chains of different orders n (values n are not too large) have practically identical frequencies (or probabilities) of each kind of nucleotides. These data allow considering DNA as a regular rich set of epi-chains, which can play a certain role in genetic and epigenetic phenomena as the author belives. Appropriate rules of nucleotide frequencies on epi-chains of long DNA sequences are formulated for further their tests on a wider set of genomes. These results testify on existence of long-range coherence in long DNA and remind the Fröhlich's theory of long-range coherence in biological systems. The phenomenological data are discussed from different standpoints: the DNA double helices and helical antennas with circular polarizations of electromagnetic waves; relations with the Fröhlich's theory; numerical analysis of DNA epi-chains under binary representations of nucleotides. Results are useful for developing quantum and algebraic biology.

The study combines two critical aspects of climate research in Nigeria. The first segment focuses on improving predictive performance of global circulation models (GCMs) employing ensemble techniques. ANN ensemble displayed significant improvement in the accuracy of climate projections, particularly in high-altitude geographic areas where traditional models exhibit limitations. Performance evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Nash–Sutcliffe Efficiency (NSE) demonstrate the effectiveness of the ANN multimodal ensemble. Building on the improved climate modelling, the second segment delves into a spatial and temporal analysis of rainfall and mean temperature patterns under climate change in Nigeria. By considering two Representative Concentration Pathways (RCPs), namely RCP4.5 and RCP8.5, the study utilizes dynamically downscaled GCMs obtained from the Coupled Model Intercomparison Project phase 5 (CMIP5). The Mann-Kendall test reveals significant spatiotemporal variations in climate trends across the country. The warming trend is particularly pronounced in the northern regions, with RCP8.5 showing more severe impacts compared to RCP4.5. This integrated approach not only highlights the importance of multimodal ensembles in climate modelling but also provides valuable insights into the spatial and temporal patterns of climate change to inform effective adaptation and mitigation strategies for Nigeria.

Increasing groundwater levels (GWL) may become one of the most serious issues for Odessa City, Ukraine. This study investigated the spatial distribution characteristics and multifractal scaling behaviour of groundwater level/depth fluctuation for a Quaternary aquifer in Odessa City using a geostatistical approach and a Multifractal Detrended Fluctuation Analysis (MF-DFA). These two methods were applied to monthly GWL fluctuation time series from 1970 to 2020 to monitor 72 hydrogeological wells situated in different parts of Odessa City. The spatial distribution of GWL revealed an overall trend of decline and recovery from 1970 to 2020 in the study area, except for most of the southern region, where a persistent recovery of groundwater depth was observed. The MF-DFA results suggest that the dynamics of GWL fluctuations have multifractal characteristics in the Odessa City area. In addition, both long-range correlation and fat-tail probability distribution contribute to multifractality. However, long-range correlations among fluctuations made a major contribution to the observed multifractality of the GWL fluctuations time series. The generalized Hurst exponent shows a wide range of change (0.20 &lt; h(q) &lt; 2.85), indicating the sensitivity of GWL fluctuations to changes in small scale factors and large-scale factors. Regarding the long range correlations of GWL depth, the Hurst exponents (q = 2) demonstrated the positive persistence of groundwater depth recovery in the southern region and the persistence of groundwater depth variation in the other regions of the study area. The dynamic changes in GWL depth in the Odessa City area may be affected by both natural structural and anthropogenic factors.

Water is essential to all life-forms including various ecological, geological, hydrological, and climatic processes/activities. With changing climate, associated El Nino/Southern Oscillation (ENSO) events appear to stimulate highly uncertain patterns of precipitation (P) and evapotranspiration (EV) processes across the globe. Changes in P and EV patterns are highly sensitive to temperature variation and thus also affecting natural streamflow processes. This paper presents a novel suite of stochastic modelling approaches for associating streamflow sequences with climatic trends. The present work is built upon a stochastic modelling framework HMM_GP that integrates a Hidden Markov Model with a Generalised Pareto distribution for simulating synthetic flow sequences. The GP distribution within HMM_GP model is aimed to improve the model's efficiency in effectively simulating extreme events. This paper further investigated the potentials of Generalised Extreme Value Distribution (EVD) coupled with an HMM model within a regression-based scheme for associating impacts of precipitation and evapotranspiration processes on streamflow. The statistical characteristic of the pioneering modelling schematic has been thoroughly assessed for their suitability to generate/predict synthetic river flows sequences for a set of future climatic projections. The new modelling schematic can be adapted for a range of applications in the area of hydrology, agriculture and climate change.

Iron supplementation is associated with side effects and thus reduces the intake compliance. Because it has fewer side effects, a recent trial recommended low-iron micronutrient powder (MNP) for the prevention of anemia in Bangladeshi children exposed to a high concentration of iron from drinking groundwater. In the present study, we hypothesize that the low-iron MNP is a potential intervention to prevent childhood anemia in the low groundwater-iron settings in Bangladesh. A cross-sectional study was conducted in Bangladesh with children aged 2-5 years who drank groundwater containing a low level of iron (0-&lt;2 mg/L).The combined intake of iron was calculated from the key sources--diet, groundwater and MNPs. The intakes of iron were compared against the standard reference intake. The children’s hemoglobin was measured using a photometer. Combined intake of iron from dietary, groundwater and low-iron MNP in children was 5.8±2.0 and 6.9±2.5 mg/day comprising 193% and 169% of the Estimated Average Requirement in the 2-3 year-old and 4-5 year-old subgroups, respectively. Combined intake of bioavailable iron from dietary and groundwater sources was 0.42±0.023 and 0.22±0.019 mg/day in children exposed to groundwater concentration 0.8-&lt;2.0 mg/L and 0.0-&lt;0.8 mg/L respectively (p&lt;0.001). The mean concentration of hemoglobin in the respective groups was 12.17±0.94 mg/dl and 11.91±0.91 mg/dl (p=0.30). The combined intake of iron from dietary and groundwater sources was associated with maintenance of hemoglobin concentration at the non-anemic level in most of the children. The finding highlights the potential of low-iron MNP in controlling childhood anemia in this setting.

: The global impact of the COVID-19 pandemic has resulted in substantial morbidity and mortality rates. Consequently, there is a pressing need for clinical research focused on the prevention, diagnosis, and treatment of this disease. To comprehensively study the course of COVID-19 in the Philippine population, it is essential to have a comprehensive dataset that includes relevant treatment and diagnostic information. While individual patient reports related to the disease are available at various institutions in the Philippines, there is a need for a more extensive and representative database to facilitate robust analysis. The purpose of this review is to elucidate various challenges and opportunities as well as propose solutions for the development and implementation of a prospective COVID-19 patient registry within a regional context in the Philippines. The primary clinical objective of establishing this registry was to enhance the accuracy of disease diagnosis. In this review, we present a comprehensive explanation of the systematic rationale, design, and execution of a COVID-19 patient registry in the Western Visayas region of the Philippines. Based on this review, several factors exist that hinder the implementation of a COVID-19 patient registry in the regional setting in the Philippines including lack of funding, facilities, infrastructure, manpower, expertise, and unwillingness of some hospitals to pursue research. We also discussed herewith the proposed program implementation for the establishment of such registry in a regional setting in the Philippines.

The novel COVID-19 pandemic prompted an unprecedented Institutional reaction to aggregate existing capacity from silos of research laboratories to establish a multidisciplinary research laboratory for COVID19 testing. In less than two weeks, resources were mobilized from the community to strengthen public health response and epidemic control. Such strengthening of institutional research capacity to support public health response contributes to a natural knowledge transfer, facilitates collaboration, and generates new frontiers for knowledge production that should ultimately lead to professional development and retention of skilled human resources. This report describes the pre-established mechanisms and involvement of the authors that made it possible to set-up a multidisciplinary laboratory in a remarkably short period of time. We also discuss the opportunities and sustainability of multidisciplinary laboratory research post-COVID19. Existing institutional capacity can be repurposed to establish multidisciplinary research laboratories to support the strengthening of basic and clinical translational research capacity in resource limited settings and impact on public health and scientific knowledge for socioeconomic development.

Micro-expression (ME) is one of the key psychological stress reactions. It is a modest, spontaneous facial mechanism. ME has significant applicability in a variety of psychologically-related sectors because to its precision and unpredictability with regard to psychological manifestations. Nevertheless, the current Micro-expression recognition (MER) algorithms have poor accuracy and a limited quantity of ME data, and this study issue has not been thoroughly investigated. Therefore, we present an approach for deep learning based on a Spatio-temporal capsule network (STCP-Net). STCP-Net has four components: a jitter reduction module, a differential feature extraction module, an STCP module, and a fully linked layer. The first two modules are aimed to extract diversifying differential features more precisely and to limit the influence of head jitter. The STCP module is used to extract Spatio-temporal features layer by layer, taking the temporal and geographical connection between features into account. This research runs sufficient trials using the Leave One Subject Out (LOSO) methodology for cross-validation using the CASMEII dataset. The conclusion and analysis demonstrate that the algorithm is innovative and efficient.

Lyme disease is a growing public health problem in Québec. Its emergence over the last decade is caused by environmental and anthropological factors that favour the survival of Ixodes scapularis, the vector of Lyme disease transmission. The objective of this study was to estimate the speed and direction of Lyme disease emergence in Québec and to identify spatiotemporal risk patterns. A surface trend analysis was conducted to estimate the speed and direction of its emergence based upon the first detected case of Lyme disease in each municipality in Québec since 2004. A cluster analysis was also conducted to identify at-risk regions across space and time. These analyses were reproduced for the date of disease onset and date of notification for each case of Lyme disease. It was estimated that Lyme disease is spreading northward in Québec at a speed varying between 18 and 32 km/year according to the date of notification and the date of disease onset, respectively. A high rate of disease risk was found in seven clusters identified in the south-west of Québec in the sociosanitary regions of Montérégie and Estrie. The results obtained in this study improve our understanding of the spatiotemporal patterns of Lyme disease in Québec, which can be used for proactive, targeted interventions by public and clinical health authorities.

Development of effective treatments for high-grade glioma (HGG) is hampered by 1) the blood-brain barrier (BBB), 2) an infiltrative growth pattern, 3) rapid development of therapeutic resistance, and, in many cases, 4) dose-limiting toxicity due to systemic exposure. Convec-tion-enhanced delivery (CED) has the potential to significantly limit systemic toxicity and in-crease therapeutic index by directly delivering homogenous drug concentrations to the site of disease. In this review, we present clinical experiences and preclinical developments of CED in the setting of high-grade gliomas.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

This article presents an analysis of monthly precipitation totals based on the Global Precipitation Climatology Centre database and monthly mean temperatures based on National Oceanic and Atmospheric Administration data for 377 catchments located around the world. Data sequences covering 110 years from 1901 to 2010 were analysed. The long-term sequences of precipitation and temperature were used to assess the variability of climate extremes, referred to here as polarisation. Measures of polarisation used in the natural sciences are discussed. A simple measure of polarisation was presented and applied to long-term sequences of monthly precipitation totals and monthly mean temperatures. Due to the nature of the proposed polarisation measure, other characteristics of precipitation and temperature sequences are also presented as a background for the discussion of the polarisation index. The study showed the existence of trends related to the polarisation of temperature and precipitation phenomena. As a result of the analysis, trends of polarisation factors in the area of precipitation and temperature were identified in 11 catchments out of the 377 analysed catchments. The trend analysis used Mann-Kendall tests at a level of significance of 5%. The Pettitt test was used to determine the point of trend change for precipitation and temperature data. The whole investigation is supported by rich graphical analyses, and the results are presented in tabular form.

The article is devoted to biological models using recurrence sequences, which are connected with the harmonic progression 1, 1/2, …, 1/n, and some cooperative properties of genomes. The harmonic progression is itself one of the recurrence sequences based on the harmonic mean. This progression appears in the hyperbolic rules of oligomer cooperative organization in eukaryotic and prokaryotic genomes. This allows thinking that the harmonic progression is also related to inherited physiological systems, which must be structurally consistent with the genetic coding system for their transmission to descendants and survival in evolution. The harmonic progression is one of historically known mathematical series, whose features were studied by Pythagoras, Leibniz, Newton, Euler, Fourier, Dirichlet, Riemann. It is widely used in many known algorithms and is closely related to some other important mathematical objects, for example, the function of the natural logarithm and harmonic numbers. Accordingly, the article describes the possibilities of using these interrelated mathematical objects to model biological structures, including logarithmic spirals and some other. Modeling inherited spiral configurations seems to be a particularly urgent task, since they are extremely common at all levels of organization of living bodies and, according to Goethe, are lines of life. The principle of a recurrence similarity, that is a special similarity of parts and transformations presented in recurrence sequences of numbers and matrix operators (the scale similarity and scale transformations are only particular cases of such similarity), is considered as one of the key principles of structural organization of living bodies.

A high proportion of dogs and cats are fearful during veterinary visits, which in some cases may escalate into aggression. Here, we discuss factors that contribute to negative emotions in a veterinary setting and how these can be addressed. The set-up of the waiting area (e.g. spatial dividers; elevated places for cat carriers), tailoring the examination and the treatment to the individual, considerate handling (minimal restraint when possible, avoiding leaning over or cornering animals) and offering high-value food or toys throughout the visit can promote security and, ideally, positive associations. Desensitisation and counterconditioning are highly recommended both to prevent and address existing negative emotions. Some negative experiences such as short-term pain from injections can be minimised by using tactile and cognitive distractions. Preemptive analgesia is recommended for known painful procedures. Recommendations for handling fearful animals to minimise aggressive responses are discussed. However, anxiolytics or sedation should be used whenever there is a risk of traumatising an animal or for safety reasons. Stress-reducing measures can decrease stress and fear in patients and consequently their owners – thus strengthening the relationship with the clients as well as increasing the professional satisfaction of veterinary staff.

Domestic cats (Felis catus) are widespread worldwide as the most popular pets. This medium-sized carnivore have well-known negative effects on biodiversity, but there is still need to better understand the proximate causes of its predation. Based on a citizen science project, we assessed the role of spatiotemporal (i.e., latitude, longitude and seasons), climatic (i.e., rainfall), anthropogenic (i.e., human footprint, HFI) and individual (i.e., sex and age) variables on the number of preys returned home by cats in metropolitan France. Over the 5 048 cats monitored between 2015 and 2022, prey from 12 different classes (n= 36 568) were returned, being 68% mammals, 21% birds and 8% squamates. Shrews brought home by cats peaked during summer, while rodents were recorded during summer-autumn. Birds brought home by cats peaked in spring-summer and in autumn, and lizards in spring and in late summer. Lower HFI was associated with more voles and mice brought home and the opposite trend was observed for lizards and birds. Younger cats were more prone to bring home shrews, birds and reptiles. Although environmental factors play a minor role in prey brought home by cats, some geographical characteristics of prey species distribution partly explains the hunting behaviour of cats.

Fake news detection has become a significant topic based on the fast-spreading and detrimental effects of such news. Many methods based on deep neural networks learn clues from claim content and message propagation structure or temporal information, which have been widely recognized. However, such models (i) ignore the fact that information quality is uneven in propagation, which makes semantic representations unreliable. (ii) Most models do not fully leverage spatial and temporal structure in combination. (iii) Finally, internal decision-making processes and results are non-transparent and unexplained. In this study, we develop a trust-aware evidence reasoning and spatiotemporal feature aggregation model for more interpretable and accurate fake news detection. Specifically, we first design a trust-aware evidence reasoning module to calculate the credibility of posts based on a random walk model to discover high-quality evidence. Next, from the perspective of spatiotemporal structure, we design an evidence-representation module to capture the semantic interactions granularly and enhance the reliable representation of evidence. Finally, a two-layer capsule network is designed to aggregate the implicit bias in evidence while capturing the false portions of source information in a transparent and interpretable manner. Extensive experiments on two benchmark datasets indicate that the proposed model can provide explanations for fake news detection results, as well as can achieve better performance, boosting 3.5% in F1-score on average.

Polo is an equestrian team sport, consisting of Open and Women’s only handicapping systems. As cumulative player handicap increases in Open Polo, distance covered, average speeds and high intensity work performed per chukka also increase. These activities may differ in terms of distribution of, and their affect upon, match outcome in Women’s Polo, and thus have implications for equine preparation and management. This study aimed to quantify spatiotemporal differences between Open and Women’s Polo when matched for handicap and assess their affect upon chukka and match outcome using a prospective cohort design. Distance, speed and high intensity activity data were collected via player worn global positioning system (GPS) units during 16-goal Open and Women’s Polo tournaments. Notational analysis quantified chukka duration and chukka and game outcomes. Between group differences were assessed by independent samples t-tests, and two factor mixed effects ANOVA for within group analyses. Between group differences were analysed using an independent samples t-test with alpha defined a priori as p<0.05. Open and Women’s Polo differed by a small to large extent (ES: 0.54 – 1.81) for all spatiotemporal metrics. In Open Polo, players covered moderately more distance (429.0m; 238.9m to 619.0m), with small to large increases in high intensity activities performed in games won. Whereas in Women’s Polo, moderately higher maximum speeds were attained in games won (17.13 km/h; 11.86 km/h to 22.40 km/h) and a small increase in accelerations performed (5.1; 0.2 to 10.0). Open and Women’s Polo, when matched for handicap, present with small to large spatiotemporal differences that are likely of practical significance, and influence game outcome differently between codes. These differences do not necessarily mean that Polo ponies need to be trained differently for each code.

Postdocs who land faculty jobs at research-intensive institutions need to juggle several new large-scale tasks: identifying space and equipment needs for their lab, negotiating the hiring package, outfitting the lab with supplies, building a team, and learning to manage time in ways that can promote productivity and happiness. Here we share tips to help new hires think clearly about each of these tasks.

Prime period sequences can serve as the fundamental tool to construct arbitrary composite period sequences. This is a review study of prime period perfect Gaussian integer sequence (PGIS). When cyclic group {1,2,…,N−1} can be partitioned into k cosets, where N=kf+1 is an odd prime number, the construction of a degree-(k+1) PGIS can be derived from either matching the flat magnitude spectrum criterion or making the sequence with ideal periodic autocorrelation function (PACF). This is a systematic approach of prime period N=kf+1 PGIS construction, and is applied to construct PGISs with degrees 1, 2, 3 and 5. However, for degrees larger than 3, matching either the flat magnitude spectrum or achieving the ideal PACF encounters a great challenge of solving a system of nonlinear constraint equations. To deal with this problem, the correlation and convolution operations can be applied upon PGIS of lower degrees to generate new PGIS with degree-4 and other higher degrees, e.g., 6, 7, 10, 11, 12, 14, 20 and 21 in this paper. In this convolution based scheme, both degree and pattern of a PGIS vary and can be indeterminant, which is rather nonsystematic compared with the systematic approach. The combination of systematic and nonsystematic schemes contributes the great efficiency for constructing abundant PGISs with various degrees and patterns for the associated applications.

Optical motion capture is a mature contemporary technique for the acquisition of motion data, alas it is non-error-free. Due to technical limitations and occlusions of markers, gaps might occur in such recordings. The article reviews various neural network architectures applied for gap filling problem in motion capture sequences within FBM framework providing the representation for body kinematic structure. The results are compared with interpolation and matrix completion methods. We found out, that for longer sequences simple linear feedforward neural networks can outperform the other, sophisticated architectures. We were also able to identify, that acceleration and monotonicity of input sequence are the parameters that have a notable impact on the obtained results.

Multi-receiver encryption enables a sender to encrypt a message and transmit the ciphertext to a set of authorized users, while no one out this group of authorized users can decrypt the message. Multi-receiver encryption is of great importance in many sectors such as broadcast communication, cloud computing, wireless communications, networking applications, e-voting, lottery, and medical applications. This paper proposes an efficient multi-receiver public key encryption scheme based on Lucas sequences. The results of the computational analysis show that, projected scheme is better against renown attacks and prevailing anonymous multireceiver algorithms

Background and Objectives: The Ventilatory Workload Kinetic Index (VWKI) has been proposed as a clinical instrument to evaluate ventilatory balance-imbalance. On the other hand, the regulated application of scales that allow an integral evaluation of the object of study and their subsequent reliability evaluation should be a continuous. The objective was determined the reliability of the VWKI between two evaluators and its applicability in the clinical field of physiotherapy. Materials and Methods: Methodology was divided into three stages: i) induction period, ii) application of the VWKI, and iii) reliability process. Results: The VWKI total score obtained excellent inter-rater reliability (ICC=0.913, p&lt;0.05). The airway resistance (AR) showed moderate inter-rater reliability (ICC=0.528, p&lt;0.05), while the use of accessory musculature (UAM) showed poor inter-rater reliability (ICC=0.483, p&lt;0.05). The physiotherapy care prioritization system, for both evaluator A (EA) and evaluator B (EB), the total VWKI score was significantly higher in priority 1 (p=0.001; p&lt;0.05, respectively). The Total VWKI score by services, it was observed that both EA and EB rated the VWKI significantly higher in the intensive care unit (ICU) than in Medicine and the other services (p=0.001, p=0.0001 and p&lt;0.05, p&lt;0.05, respectively). Conclusions: VWKI have of excellent reliability your the total score. Also, have consistency with the system of prioritization of physiotherapy care, and the ability to determine the severity of respiratory clinical pictures.

The task of human action recognition (HAR) based on skeleton data is a challenging yet crucial technique owing to its wide-ranging applications in numerous domains, including patient monitoring, security surveillance, and observation of human-machine interactions. While numerous algorithms have been proposed in an attempt to distinguish between a myriad of activities, most practical applications necessitate highly accurate detection of specific activity types. This study proposes a novel and highly accurate spatiotemporal graph autoencoder network for HAR based on skeleton data. Furthermore, an extensive investigation was conducted employing diverse modalities. To this end, a spatiotemporal graph autoencoder was constructed to automatically learn both spatial and temporal patterns from human skeleton datasets. The powerful graph convolutional network, designated as GA-GCN, developed in this study, notably outperforms the majority of existing state-of-the-art methods when evaluated on two common datasets, namely NTU RGB+D and NTU RGB+D 120. On the first dataset, the proposed approach achieved accuracies of 92.3\% and 96.8\% for the cross-subject and cross-view evaluations, respectively. On the more challenging NTU RGB+D 120 dataset, GA-GCN attained accuracies of 88.8\% and 90.4\% for the cross-subject and cross-set evaluations, respectively.

Advances in technology have introduced remarkable capabilities, particularly in the realm of gathering and accessing vast amounts of data, which has led to the emergence of the "Big Data" era, presenting exciting opportunities to harness the wealth of the generated information. Traffic management centers comprise a stakeholder that can exploit such data towards improving road network operation. This research, explores the utilization of real traffic data, collected by distinct sources on an interurban motorway: static location based aggregate traffic data and probe vehicle data. The first is collected by traffic detectors, while the second involves patrol vehicles’ GNSS data. The traffic quantity analysed is vehicle speed, while specific emphasis has been given on the ap-plication of heatmaps to represent daily traffic patterns and the utilization of k-means clustering. The performed analysis demonstrates the importance of considering spatiotemporal variables as a unified entity when analysing speed in transportation networks. The limitations of using patrol vehicles as estimators of ambient speed, especially in low and heavy traffic conditions, are deline-ated, while the dual purpose of data gathered from these vehicles towards enhancing driving performance and enabling detector maintenance is emphasized. Valuable traffic insights are offered through comprehensive spatiotemporal speed behavior analysis and data combination. The inte-gration of these techniques enhances decision-making for a streamlined and secure transportation system

The processes of soliton generation and interference focus on the complex nonlinear soliton dynamics resembling matter particles. In order to further understand the dynamic process of solitons from a multidimensional perspective, here we report the vectorial solitons nature under two-sets pulse splitting in a single cavity induced by dual-core fiber assisted ultrafast fiber lasers. Owing to the weakly coupled cores in symmetrical dual core fiber (SDCF), two pulse groups interaction are formed in a cavity. By using the dispersive Fourier transformation technique (DFT), it was found that the four-component polarized rotation vector solitons (PRVS) generate. Moreover, gradually increasing the power can obtain the locked soliton bound state in two core space, and the corresponding evolution is similar to that of non-degenerate bright solitons in Bose Einstein condensates (BEC). In addition, by properly controlling the soliton phase offset in SDCF, the soliton rain state of multi pulse evolution can be obtained. The related experimental results would be fruitful to the communities interested in soliton dynamics and frequency comb lasers.

Abstract: Research Highlights: This study is among the first to investigate ozone levels in urban forests in China. It establishes that urban forest air quality in Yuanshan Forest Park, Shenzhen, is suitable for recreational activities and identifies spatial, seasonal, and diurnal O3 patterns and relationships with micrometeorological parameters, suggesting the possibility of manipulating relevant forest characteristics to reduce O3 levels. Background and Objectives: An understanding of O3 levels of urban forest environments is needed to assess potential effects on human health and recreational activities. Such studies in China are scarce. This study investigated urban forest O3 levels to improve understanding and support residents engaging in forest recreational activities. Materials and Methods: We monitored O3 levels in 2015–2016 for three urban forests representing common habitats (foothill, valley, and ridge) in Yuanshan Forest Park, Shenzhen, and for an adjacent square. Results: The overall mean daily and daily maximum 8-h mean (MDA8) O3 concentrations were highest for the ridge forest and lowest for the valley forest. Each forest’s O3 concentrations were highest in summer. Diurnally, forest O3 concentrations peaked between 13:00 and 17:00 and reached a minimum between 03:00 and 09:00. The correlation between forest O3 concentrations and air temperature (AT) was strongly positive in summer and autumn but negative in spring. In each season, O3 concentration was negatively correlated with relative humidity (RH). No MDA8 or hourly O3 concentrations in the forests exceeded National Ambient Air Quality Standard Grade I thresholds (100 and 160 μg m−3, respectively). Conclusions: O3 accumulation is present in ridge urban forest in all seasons. Foothill and valley urban forests have better air quality than ridge forestation. Urban forest air quality is better in spring and autumn than in summer and is better from night-time to early morning than from noon to afternoon.

Liver transplantation is currently the only curative therapy for patients with liver cirrhosis. Not all patients in the natural course of the disease will undergo transplantation, but the majority of them will experience portal hypertension and its complications. In addition to medical and endoscopic therapy, a key role in managing these complications is played by the placement of a transjugular intrahepatic portosystemic shunt (TIPS). Some indications for TIPS placement are well-established and they are expanding and broadening over time. This review aims to describe the role of TIPS in managing patients with liver cirrhosis, considering them as potential candidates for liver transplantation. As far as is known TIPS placement seems not to affect the surgical aspects of liver transplantation, in terms of intra operative bleeding rates, postoperative complications or length of stay in the Intensive Care Unit. However, the placement of a TIPS “towards transplant" can offer advantages in terms of enhancing the patient's clinical condition at the time of transplantation and improving patient survival. Additionally, the TIPS procedure can help preserve the technical feasibility of the transplant itself. In this context, indications for TIPS placement may be expanded and considered at an earlier stage. TIPS insertion can also lead to serious adverse events and there are no evidence-based recommendations on this topic to date. For these reasons further studies are needed to make reliable recommendations for TIPS in the pre-transplant setting.

The purpose of this systematic review is to examine the various articles published in the field of neuropsychology of anxiety research in terms of publication type and volume, methodology, and subject matter. The significance of this topic derives from the clinical and functional consequences of cognitive symptoms. Generalized anxiety disorder refers to the cluster of clinical symptoms characterized by predominant anxious anticipation and difficulty controlling worry. In order for the review to be systematic and reproducible, a search of the PsycINFO, Scopus, PubMed, Elsevier databases and an analysis using the PRISMA method are suggested. After analyzing the research data, empirical studies evaluating the clinical neuropsychology of generalized anxiety disorder are discussed.

In this paper, we consider a manufacturer who produces and sells a kind of innovative product in the monopoly market environment. Because the life cycle of innovative product is usually shorter than its procurement lead time, one unique demand quantity (scenario) will occur in the selling season, thus there is only one chance for the manufacturer to determine both optimal production quantity and optimal sale price. Considering this one-time feature of such a decision problem, a price-setting newsvendor model for innovative products is proposed. Different to the existing price-setting newsvendor models, the proposed models determine the optimal production quantity and sale price based on some specific state (scenario) which is most applicable for the manufacturer. The theoretical analysis provides managerial insights into the manufacturers’ behaviors in a monopoly market of an innovative product and several phenomena in the luxury goods market are well explained.

Cleomaceae is an important group that is medicinally and economically important. However, its systematic position has been controversial. In this study, we have obtained three chloroplast genomes sequencing results of Cleomaceae species. The genome sizes ranged from 157,073 bp (Sieruela rutidosperma) to 158,152 bp (Gynandropsis gynandra). These chloroplast genomes have a typical quadripartite formation with a large single-copy (LSC) region (86,422-87,242bp), a small single-copy (SSC) region (18,485-18,548bp), and a pair of inverted repeat (IR) regions (26,083-26,184bp). The chloroplast genomes encoded 131-132 genes, comprising 8 rRNA genes, 37 tRNA genes, and 79-84 protein-coding genes. The overall GC contents of the three chloroplast genomes are almost identical (35.81%-36.02%). The phylogenetic trees concluding 8 Cleomaceae plants were constructed based on ML and BI methods. The results showed that Gynandropsis gynandra and Sieruela rutidosperma were in the same branch, with Corynandra viscosa as their sister. Six hypervariable areas were identified, containing clpP, matK, ndhF, atpH-atpI, atpF-atpH and petN-psbM which could be used in species identification and population genetics studies. This study provides valuable information for further species identification, evolutionary and genetic studies of Cleomaceae in the future.

In this work, we present a new way to study the genetic code mathematical and chemical structure, based on the use of mathematical computations involving some few recently designed Fibonacci-like sequences, the “seeds” (“initial conditions”) of which are chosen according to the chemical and physical data of the three amino acids serine, arginine and leucine, playing a prominent role in a recent symmetry classification scheme of the genetic code. It appears that these mathematical sequences, of the same kind as the famous Fibonacci series, apart from their usual recurrence relations, are highly interwinned by many useful linear relationships. Using these sequences and also various sums or linear combinations of them, we derive several physical and chemical quantities of interest, as the number of total coding codons, 61, obeying various degeneracy patterns, the detailed number of hydrogen atoms, the detailed number of atoms (H/CNOS) and the integer molecular mass (or nucleon number), in the side chains of the 61 “amino acids”, also in various degeneracy patterns, in agreement with those described in the literature. Also, unexpectedly but interestingly, we find, as a by-product, an accurate description of the very chemical structure of the four ribonucleotides uridine monophosphate (UMP), cytidine monophosphate (CMP), adenosine monophosphate (AMP) and guanosine monophosphate (GMP), the building blocks of RNA whose groupings, in three units, constitutes the triplet codons. In summary, we find a full mathematical and chemical connection with the “ideal sextet’s classification scheme” mentioned above as well as with several others, notably, the Findley-Findley-McGlynn symmetrical classification. We organize the content of the text in such a way that, besides the presentation of several new research results, it has also an educational dimension. The paper could therefore be read and the computations easily worked out, also by non-experts with mathematical background.

The article presents an analysis of monthly precipitation totals based on the GPCC database, and monthly mean temperatures (NOAA data) for 377 catchments distributed across the globe. The analysis included 110-year data sequences from 1901 to 2010 calculated from grid data with a spatial resolution of 0.5°x 0.5° longitude and latitude. Long-term sequences of precipitation and temperature were used to assess the polarization of climatic phenomena. The noticeable impact of polarization in the area of extreme changes in temperature and precipitation is related to anthropogenic factors such as greenhouse gas emissions, deforestation and pollution, which affect ecosystem functioning, biodiversity, water resources and economies. The paper demonstrates the existence of trends related to the polarization of temperature and precipitation phenomena. The measures of polarization used in science are discussed. A simple measure of polarization was proposed and applied to both long-term sequences of monthly precipitation totals and monthly mean temperature. Due to the nature of the proposed polarization measure, other characteristics of the precipitation and temperature sequences are also presented as background for the discussion of the polarization index. The paper presents, for a selection of several hundred catchments from around the world, analyses of the assessment of precipitation and temperature trends using non-parametric tests. The trend analyses use Mann -Kendall tests at the 5% significance level. A Pettitt test was used to determine the trend change point for precipitation and temperature data. The whole is supported by rich graphical analyses and the results are presented tabularly.

Inherited mutations in BRCA1 and BRCA2 genes increase risks for breast, ovarian, and other cancers. Both genes encode proteins for accurately repairing chromosome breaks. If mutations inactivate this function, broken chromosomes may not be restored correctly, allowing breaks to persist or rearrange chromosomes. These abnormalities are potentially catastrophic events that can originate from viral infections. I used bioinformatic analyses of publicly available breast cancer patient data to show that the distribution of chromosome breaks in hereditary breast cancers differs markedly from sporadic breast cancers. Then I tested hereditary breast cancer sequence data around chromosome breaks for DNA similarity to all known viruses. Human DNA flanking breakpoints usually had decisive matches to Epstein-Barr virus (EBV / HHV4) tumor variants HKHD40 and HKNPC60. Many breakpoints were near EBV genome anchor sites, human EBV tumor-like sequences, EBV-associated epigenetic marks, and some fragile sites. On chromosomes 2 and 12, sequences near EBV genome anchor sites accounted for 90% and 88% of breakpoints (p<0.0001), respectively. On chromosome 4, 51/52 inter-chromosomal breakpoints were close to EBV-like sequences in 19 hereditary breast cancers. In contrast, 19 sporadic breast cancers only had 12 interchromosomal breakpoint regions on chromosome 4 near EBV-like sequences. On various other chromosomes, five EBV genome anchor sites were near hereditary breast cancer breakpoints at precisely defined, disparate gene or LINE locations. Independent evidence further implicating EBV in hereditary breast cancer breakpoints is that 25 breast cancer break positions are within 1.25% of breakpoints in model EBV cancers. In addition to BRCA1 or BRCA2 mutations, all the hereditary breast cancers had mutated genes essential for immune responses. This compromise facilitates reactivation of herpes viruses which produce nucleases capable of breaking chromosomes. EBV also causes other deleterious effects: anchored EBV episomes can interfere with normal replication and obstruct DNA break repairs; even very early infection causes massive transcription changes. The results, therefore, imply proactive treatment and prevention of herpes viral infections may prevent some chromosome breaks and benefit BRCA mutation carriers.

Inherited mutations in BRCA1 and BRCA2 genes increase risks for breast, ovarian, and other cancers. Both genes encode proteins for accurately repairing chromosome breaks. If mutations inactivate this function, chromosome fragments may not be restored correctly. Resulting chromosome rearrangements can become critical breast cancer drivers. Because I had data from thousands of cancer structural alterations that matched viral infections, I wondered whether infections contribute to chromosome breaks and rearrangements in hereditary breast cancers. There are currently no interventions to prevent chromosome breaks because they are thought to be unavoidable. However, if chromosome breaks come from infections, they can be treated or prevented. I used bioinformatic analyses to test publicly available breast cancer sequence data around chromosome breaks for DNA similarity to all known viruses. Human DNA flanking breakpoints usually had the strongest matches to Epstein-Barr virus (EBV) tumor variants HKHD40 and HKNPC60. Many breakpoints were near sites that anchor EBV genomes, human EBV tumor-like sequences, EBV-associated epigenetic marks, and fragile sites. On chromosome 2, sequences near EBV genome anchor sites accounted for 90% of breakpoints (p<0.0001). On chromosome 4, 51/52 inter-chromosomal breakpoints were close to EBV-like sequences. Five EBV genome anchor sites were near breast cancer breakpoints at precisely defined, disparate gene or LINE locations. Breakpoint flanking regions resembled known EBV-cancers. Twenty-five breakpoints in breast cancers were within 1.25% of EBV cancer breakpoints. In addition to BRCA1 or BRCA2 mutations, all the breast cancers had mutated genes essential for immune responses. Because of this immune compromise, herpes viruses can activate and produce nucleases that break chromosomes. Alternatively, anchored viral episomes can obstruct break repairs, whatever the cause. The results, therefore, imply proactive treatment and prevention of herpes viral infections may prevent some chromosome breaks and benefit BRCA mutation carriers.

In this article/review, the selective interactions of several berberine and palmatine derivatives with various DNA G-quadruplex structures are reported. These derivatives were constructed starting from two natural compounds, berberine and palamatine, through specific synthetic passages following two different schemes for each of them and using several substituents. The details of these synthesis are also described. Indeed, the study of the interactions of these derivative compounds with various G-quadruplex forming sequences was carried out by means of various structural and biochemical techniques. The results show that the presence of suitable side chains are very useful to improve the interaction of the ligands with G-quadruplex structures. Thus, since G-quadruplex formation is promoted by these compounds, which have never been reported before, these may be tested as potential anticancer drugs.

Genus Clematis is one of the largest within Ranunculaceae. Here we report the chloroplast genome of two Clematis species, C. brachyura and C. trichotoma endemic to Korea. The chloroplast genome lengths of C. brachyura and C. trichotoma are 159,532 bp and 159,170 bp, respectively. Gene contents in the complete chloroplast genomes of these two Clematis species are identical to that of most Ranunculaceae and other angiosperms. However, our data results demonstrated that genus Clematis has inversion and rearrangement events concerning gene rps4 gene, rps16 to trnH region, and trnL to ndhC region, and IR regions expansion. Comparison of IR regions among Ranunculaceae species revealed that Clematis species contained six protein coding genes (infA, rps8, rpl14, rpl16, rps3, and rpl22) usually found in the long single copy (LSC) region of other species. Phylogenetic analysis demonstrated that genus Clematis is closely related to genus Ranunculus. Differences in repeat structure, substitution rates, and IR expansion in genera Clematis and Ranunculus, explained their relationship. Clematis species showed slightly higher tandem repeats content than Ranunculus species. The six protein-coding genes showed lower synonymous substitution rates in the IR of Clematis species than in the LSC of Ranunculus species. Overall, the chloroplast genomes and results presented here provide important information on the evolution of Ranunculaceae.

[3:46 PM] Anahita Zahertar This research explores the factors influencing bikeshare usage durations in the Detroit Metropolitan Area over two years, focusing on spatial, temporal, and COVID-19-related variables. Using a fully parametric hazard-based duration model with random parameters, we address data heterogeneity and uncover how different conditions affect bikeshare trips. Our findings reveal that a) intense environmental factors such as high traffic stress, poor weather, and high COVID-19 risk levels are associated with shorter trip durations b) in contrast, supportive initiatives like memberships, an affordable $5 Access Pass, a free one-month pass during the pandemic, and the introduction of new stations have been more likely to encourage longer rides c) furthermore, the study examines how variables like gym closures due to the pandemic, evening hours, and the addition of new stations, which were set as random variables in our model, exhibit both positive and negative relationships with ride durations. A key finding is the 20-minute mark in ride durations, which helps understand user behaviors and trip purposes. This insight aids urban planning by suggesting strategic bike station placements to enhance bikeshare system efficiency and meet diverse community needs. This study not only deepens our understanding of urban mobility dynamics but also underscores the effectiveness of adaptive strategies in promoting sustainable urban transportation.

Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics and spatial transcriptomics now allow recording the patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multi-scale dynamics of its evolution. Here, we review latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed a burgeoning growth in recent past in terms of mapping heterogeneity within tumor cell types as well as stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor, and a more systematic investigation of implications of heterogeneity for the patient outcomes.

Diversification of cropping sequences has a positive impact on soil organic carbon, while improving nutrient cycling and crop yields. The objective of this research was to assess amylase and cellulase activity, C and N dynamics, and maize yield on a low fertility Oxisol in the Brazilian Cerrado. The experiment was conducted under field conditions during three maize crop succession cycles. Treatments consisted of cultivating maize during the summer, after sorghum and lablab cropped as green manure and fallow during the winter. Higher maize yields were achieved by sorghum-maize succession compared to monocropping, due to higher N fertilizer and biomass in-puts to topsoil. Sorghum-maize succession also provided a higher proportion of stable C and N compared to other successions. Maize yields declined as tropical soil fertility intrinsically de-creased along three crops succession cycles. Cellulase activity decreased over time, whereas amylase activity increased as the plant residues were already in advanced stages of decomposition. The sorghum-maize crop succession stood out compared to lablab and fallow as it provided the high-est maize yields, while, maintaining higher C and N levels, and amylase activity. This better performance was likely due to larger amounts of incorporated biomass and better mineral N fertilizer management.

Well-annotated gene and genomic sequences serve as a foundation for making inferences in molecular biology and evolution, and can directly impact public health. The first SARS-CoV-2 genome was submitted to GenBank and used to develop the two successful vaccines. Conserved protein domains are often chosen as targets for developing antiviral medicines or vaccines. Mutation and substitution patterns provide crucial information not only on functional motifs and genome/protein interactions but also for characterizing phylogenetic relationships among viral strains. These patterns, together with the collection time of viral samples, serve as the basis for addressing the question of when and where the host-switching event occurred. Unfortunately, viral genomic sequences submitted to GenBank undergo little quality control, and critical information in the annotation is frequently changed without being recorded. Researchers often have no choice but to hold blind faith in the accuracy of the sequences. There have been reports of incorrect genome annotation but no report that casts doubt on the genomic sequences themselves because it seems theoretically impossible to identify genomic sequences that may not be authentic. This paper takes an innovative approach to show that some SARS-CoV-2 genomes submitted to GenBank cannot be possibly authentic. Specifically, some SARS-CoV-2 genomic sequences deposited in GenBank with collection time in 2023 and 2024, isolated from saliva, nasopharyngeal, sewage, and stool are identical to the reference genome of SARS-CoV-2 (NC_045512). The probability for such occurrence is effectively 0. I also compile SARS-CoV-2 genomes with changed sample collection time. One may led astray in bioinformatic analysis without being aware of errors in sequences and sequence annotation.

In the context of all-digital radar systems, phase-modulated continuous wave (PMCW) based on pseudorandom binary sequences (PRBSs) appears a prominent candidate modulation scheme for applications such as autonomous driving. Among the reasons for that are its simplified transmitter architecture and lower linearity requirements, e.g., compared to orthogonal-frequency division multiplexing radars, as well as its high velocity unambiguity and multiple-input multiple-output operation capability that are characteristic of digital radars. For appropriate operation of a PMCW radar, choosing a PRBS whose periodic autocorrelation function (PACF) has low sidelobes and high robustness to Doppler shifts is paramount. In this sense, this article performs an analysis of Doppler shift tolerance of the PACFs of typically adopted PRBSs in PMCW radar systems supported by simulation and measurement results. To accurately measure the Doppler-shift-induced degradation of PACFs, peak power loss ratio (PPLR), peak sidelobe level ratio (PSLR), and integrated-sidelobe level ratio (ISLR) were used as metrics. Furthermore, to account for effects on targets whose ranges are not multiples of the range resolution, oversampled PACFs are analyzed.

The aim of this survey is to review some recent developements in devising efficient preconditioners for sequences of linear systems A x = b. Such a problem arise in many scientific applications, such as discretization of transient PDEs, solution of eigenvalue problems, (Inexact) Newton method applied to nonlinear systems, rational Krylov methods for computing a function of a matrix. Full purpose preconditioners such as the Incomplete Cholesky (IC) factorization or approximate inverses are aimed at clustering eigenvalues of the preconditioned matrices around one. In this paper we will analyze a number of techniques of updating a given IC preconditioner (which we denote as P0 in the sequel) by a low-rank matrix with the aim of further improving this clustering. The most popular low-rank strategies are aimed at removing the smallest eigenvalues (deflation) or at shifting them towards the middle of the spectrum. The low-rank correction is based on a (small) number of linearly independent vectors whose choice is crucial for the effectiveness of the approach. In many cases these vectors are approximations of eigenvectors corresponding to the smallest eigenvalues of the preconditioned matrix P0 A. We will also review some techniques to efficiently approximate these vectors when incorporated within a sequence of linear systems all possibly having constant (or slightly changing) coefficient matrices. Numerical results concerning sequences arising from discretization of linear/nonlinear PDEs and iterative solution of eigenvalue problems show that the performance of a given iterative solver can be very much enhanced by the use of low-rank updates.

Aim: Streptococcus pneumoniae and influenza H1N1 virus are common organisms associated with human infections. These infections could play a significant role in immune regulation. The study was performed to analyse the genome sequences of these organisms with human genome and study its functional significance. Methods: The study was performed to analyse the overlapping of genome sequences in Streptococcus pneumoniae and Influenza (H1N1) virus against human genome sequences by BLASTn sequence analysis. The alignments are studied against the corresponding genes for their functional significance with DAVID and NDEx software.Results: Several hits or overlapping nucleotide segments were identified. Between streptococcus and homo sapiens 287 overlaps were identified, and among influenza and homo sapiens 124 hits were identified. A wide range of functional significance of these genes were identified, and the results are presented in this study. The results show insights into functional pathways and biological activities associated with the respective vaccinations or infections by these microorganisms. Conclusion: The common organisms like Streptococcus pneumoniae and Influenza H1N1 virus actively interact with the immune system and result in a wide range of immune regulations.

Physical roots, exemplifications and consequences of periodic and aperiodic ordering (represented by Fibonacci series) in biological systems are discussed. The role and physical and biological roots of symmetry and asymmetry appearing in biological patterns is addressed. Generalization of the Curie-Neumann Principle as applied to biological objects is presented, briefly summarized as: “asymmetry is what creates a biological phenomenon”. The “up-bottom approach” and “bottom up” approaches to the explanation of symmetry in organisms are presented in detail. The “up-bottom approach”, implies that the symmetry of the biological structure follows the symmetry of media in which this structure is functioning; the “bottom-up” approach, in turn, adopt that the symmetry of biological structures emerges from the symmetry of molecules constituting the structure. A diversity of mathematical measures applicable for quantification of ordering in biological patterns is introduced. The continuous, Shannon and Voronoi measures of symmetry/ordering and their application to biology objects are addressed. The fine structure of the notion of “ordering” is discussed. Informational/algorithmic roots of ordering inherent for the biological systems are considered. Ordered/symmetrical patterns provide economy of biological information, necessary for algorithmic description of a biological entity. Application of the Landauer principle bridging physics and theory of information to the biological systems is discussed.

The geographical location of Yunnan province is at the upstream area of water vapor transportation from the Bay of Bengal and the South China Sea to inland China. Understanding the spatiotemporal variations of water vapor over this region holds significant importance. We utilized the GNSS data collected from 12 stations situated in Yunnan, which are part of Crustal Movement Observation Network of China, to retrieve hourly precipitable water vapor (PWV) from 2011 to 2022. The retrieved PWV at Station KMIN were evaluated by the nearby radiosonde data, and the result shows that the mean bias and RMS of the differences between the two datasets are 0.08 and 1.78 mm respectively. Average PWV values at these stations are in the range of 11.77 to 33.53 mm, which decrease from the southwest to the north of Yunnan and are negatively correlated with the station heights and latitudes. Differences between average PWV in wet season and dry season range from 12 to 27 mm. These differences tend to increase as the average PWV increases. The yearly rates of PWV variations, averaging 0.18 mm/year, are all positive for the stations, indicating a year-by-year increase in water vapor. Amplitudes of the PWV annual cycles are 9.75-20.94 mm. The spatial variation of these amplitudes is similar to that of average PWV over the region. Generally, monthly average PWV values increase from January to July and decrease from July to December, and the growth rate is less than the decline rate. Average diurnal PWV variations show unimodal PWV distributions over the course of the day at the stations except Station YNRL, where bimodal PWV distribution was observed.

Since the 1970s, land subsidence has been developing rapidly in the Beijing Plain, the systematic study of its evolution mechanism is of great significance to the sustainable development of the regional economy. First, based on ENVISAT ASAT and RADARSAT2 data, the land subsidence data in Beijing Plain were obtained using permanent interferometer technology. Second, based on the GIS platform and using fishing net tools, vector data of ground settlement with different resolutions were obtained. Through a series of tests, a scale of 960 metres was selected as the research unit, and the subsidence rate of the grid was obtained from 2004 to 2015. Finally, based on the Mann-Kendall mutation test method, a trend analysis of land subsidence changes in various grids was carried out. The results showed that single-year mutation mainly distributed in the middle and lower parts of the Yongding River alluvial fan and the Chaobai River alluvial fan, mainly occurring in 2015, 2005 and 2013, respectively. The upper and middle alluvial fan of the Chaobai River, the vicinity of the emergency water source and the edge velocity of the groundwater funnel have undergone several sudden changes. Combined with hydrogeology, basic geological conditions and the impact of the South-to-North Water transfer project, we analysed the causes of the mutations in the grid. The research results can provide a basis for the study and prevention of land subsidence in this area and help to further explore the trend characteristics of land subsidence in this area.

Signal preprocessing and feature extraction are two important and necessary steps of data analysis. In signal analysis, typically these two steps are implemented through multiple algorithms. Moreover, the significant parameters in most algorithms are set empirically. We have developed an algorithm that can preserve and highlight characteristic waveforms while attenuating non-feature waveforms. The parameters in the algorithm are set through difference analysis. We have conducted experiments on both clinical and open-source ECG data, obtaining effective test results.

The main purpose of this article is to identify key areas of research on climate change in the context of the SDGs, focusing on the potential development impacts on Bolivia and Paraguay. Application of the Delphi technique with the involvement of a panel of experts allowed the consolidation of different perspectives and knowledge on climate change, focusing on those that experts considered to have the greatest potential impact on the regions. The results of this study constitute a valuable guide for decision-makers and funding bodies, highlighting research areas that could have a significant impact at the national and regional levels, as well as for researchers, identifying specific research areas crucial for regional development and climate change mitigation.

1. Darwin’s naturalization hypothesis predicts that alien species closely related to native species are less likely to naturalize because of strong competition due to niche overlap. Closely related species are likely to attract similar herbivores and to release similar plant volatiles following herbivore attack, thus could attract the same predators. However, the importance of phylogenetic relatedness on the interaction between alien and native plants has never been tested in a multitrophic context. 2. In a mesocosm experiment we grew six alien target plant species alone and in competition with nine native plant species of varying phylogenetic relatedness. To test the effects of multitrophic interactions on the performance of alien target species, we used enclosure cages to expose plants to the presence and absence of herbivorous arthropods, predatory arthropods and nematodes. 3. Surprisingly, biomass and number of flowering structures increased with presence of competitors for some of the alien species, but overall there was no consistent competition effect. Similarly, we found that none of the multitrophic-interaction treatments affected survival, biomass or number of flowering structures of the alien species. 4. We conclude there was no significant relationship between performance measures of the alien species and their phylogenetic relatedness to the native competitors.