Recent changes in lung cancer care, including new approvals in first line and the introduction of high throughput molecular technologies in routine testing led us to question ourselves on how deeper molecular testing may be helpful for the optimal use of targeted drugs. In this article we review recent results in the scope of personalized medicine in lung cancer. We discuss biomarkers that have a therapeutic predictive value in lung cancer with a focus on recent changes and on the clinical value of large scale sequencing strategies. We review the use of second and third generation EGFR and ALK inhibitors with a focus on secondary resistance alterations. We discuss anti-BRAF and anti-MEK combo, emerging biomarkers as NRG1 and NTRKs fusions and immunotherapy. Finally we discuss the different technical issues of comprehensive molecular profiling and show how large screenings might refine the prediction value of individual markers. Based on a review of recent publications (2012–2018), we address promising approaches for the treatment of patients with lung cancers and the technical challenges associated to the identification of new predictive markers.

This study assesses attitudes of young adults' (18-30 years old) consumption on local and traditional products in7 European countries. A clustered sample (n=836) from natives of Greece, Bulgaria, Romania, Slovenia, Croatia, Denmark and France was collected, by distributing questionnaires through social media and university mail services. Sample was examined by implementing Principal Component Analysis (PCA) in three different samples; overall and two subgroups, Eastern and Western European countries. Six major factors revealed: consumer behavior, health issues, cost, influence from media and close environment and availability on store. As a result, young adults have a positive attitude to local and traditional food products but they express insecurity for health issues. Cost factor influences less people from Eastern European countries than those from the overall sample (3rd and 5th factor accordingly). Influence of close environment is a different factor in Eastern countries comparing to Western ones that it common with influence from media. Females and older people (25-30 years old) doubt less about TFPs, while media have high influence on consumers’ decisions. Aim of this survey is to create consumer profiles of young adults and create different promotion strategies of local and traditional products among the two groups of countries.

Baobab (Adansonia digitata) leaves are usually used in dry form in the preparation of a soup known as ‘miyan kuka’ in Northern Nigeria. The leaves are believed to have nutritional and medicinal benefits and have been used for those purposes in Africa and Asia. However there has been limited research on the detailed constituents of the dry leaves. In this study, phytochemical, elemental and proximate analyses of stored, sun and shade-dried baobab leaves were conducted. The results revealed a great variation in the nutritional contents of the leaves. The study reveals that the leaves are rich in phytochemicals such as glycosides, saponins, steroids and flavonoids while alkaloids, tannins and resins are absent. Also, they are important source of minerals such as zinc, copper, iron and manganese. In addition, they are rich in fibre, crude protein, nitrogen and ash. Contrasting responses were obtained in the samples studied. The implications of these responses are discussed in relation to crop yield.

An internationally acknowledged requirement is to analyze and provide technical solutions for prevention and safety during use and maintenance of manure spreader wagons. Injuries statistics data and specific studies show that particular constructive criticalities have been identified on these machines, which are the cause of serious and often fatal accidents. These accidents particularly occur during the washing and maintenance phases, especially when such practices are carried out inside the hopper when the working bodies of the machine are in action. The current technical standards and the various safety requirements under consideration have not always been effective for protecting workers. To this end, the use of SWOT analysis allowed to highlight critical and positive aspects of the different solutions studied for reducing the risk due to contact with the working bodies. The selected and tested solution consists in a decoupling system automatically activated when the wheels of the wagon are not moving. Such a solution prevents the contact with the moving working bodies of the machine when the worker is inside the hopper. This mechatronic solution allowed to obtain a prototype that has led to the resolution of the issues related to the use of the wagon itself: in fact the system guarantees the stopping of manure spreading organs in about 12 seconds from the moment of the wheels stopping.

Improved understanding of climate-growth relationships of multi-species is fundamental to understand and predict response of forest growth to future climate change. Forests are mainly composed of conifers in Northwestern Yunnan Plateau, but variations of growth response to climates among the species are not well understood. To detect growth response of multiple species to climate change, we developed residual chronologies of four major conifers, i.e. Abies georgei, Picea likiangensis, Pinus densata and Larix potaninii at upper distributional limits in Shika Snow Mountain. By using dendroclimatology method, we analyzed correlations between the residual chronologies and climate variables. The results showed that conifer radial growth was influenced by both temperature and precipitation in Shika Snow Mountain. Previous November temperature, previous July mean maximum temperature (T_max) and current June precipitation were the common climatic factors, which had consistent influences on radial growth of four species. Temperature in previous post growing season (September–October) and current growing season (June-August), and precipitation in previous August were the common climatic factors, which had divergent impacts on four species radial growth. Current May T_max and early growing season (April-May) precipitation showed positive and negative influences on growth of P. likiangensis, respectively. Temperature in current post growing season positively affected growth of A. georgei. According to the prediction of climate models and our understanding in growth response of four species to climate variables, we may understand growth response to climate change at species level. It is difficult to predict future forest growth in the study area, since future climate change might cause both increases or decreases for four species and indirect effects of climate change on forest should be considered.

Strategic researches on green mine construction are of great theoretical and practical significance to the sustainable development of China's mining industry as well as the great-leap-forward development strategies of China. Strategies of green mine construction in China are methods summarized to solve all potential problems from mine production to ecological restoration. At present, strategies of green mine construction in China are not fully evaluated and studied yet. Therefore, on the basis of green mine construction related literatures carried out by researchers in China and abroad, this study took the green mine of Yongcheng City in China as the research object to evaluate the current situation of green mine construction in Yongcheng City and put forward corresponding countermeasures. First of all, driving force-pressure-state-impact-response (DPSIR) model was introduced for the construction of evaluation index system; construction principles and selection methods of indexes and the index system based on driving force, pressure, state, impact and response were constructed. Secondly, principal component analysis (PCA) was adopted to calculate and evaluate data of green mine of Yongcheng City in recent years, and construction state of green mine in Yongcheng City was analyzed concretely according to the evaluation results. Empirical results showed that, the evaluation system constructed in this study was feasible, which could be applied to evaluate construction of green mine effectively.

By installing FBG sensors on the geogrids, smart geogrids can both reinforce and monitor the stability for geogrid-reinforced slopes. In this paper, a geogrid-reinforced sand slope model test was conducted in the laboratory and fiber Bragg grating (FBG) sensing technology is used to measure the strain distribution of the geogrid. Based on the model test, the performance of the reinforced soil slope is simulated by finite element software Midas-GTS, and the stability of the reinforced soil slope is analyzed by strength reduction method. The relationship between the geogrid strain and safety factor is set up. The results indicate that the measured strain and calculated results agree very well. The geogrid strain measured by FBG sensor can be applied to evaluate the stability of geogrid-reinforced sand slopes.

In his best-selling book An Introduction to Probability Theory and its Applications, W. Feller established a way of ending the St. Petersburg Paradox by the introduction of an entrance fee, and provided it for the case in which the game is played with a fair coin. A natural generalization of his method is to establish the entrance fee for the case in which the probability of head is θ (0 < θ < 1/2). The deduction of those fees is the main result of Section 2. We then propose a Bayesian approach to the problem. When the probability of head is θ (1/2 < θ < 1) the expected gain of the St. Petersburg Game is finite, therefore there is no paradox. However, if one takes θ as a random variable assuming values in (1/2,1) the paradox may hold, what is counter-intuitive. On Section 3 we determine a necessary and sufficient condition for the absence of paradox on the Bayesian approach and on Section 4 we establish the entrance fee for the case in which θ is uniformly distributed in (1/2,1), for in this case there is paradox.

Aim: To analysis the relationship between healthy adults right ventricular diameter (RVD) and geographical factors. Location: China’s 22 provinces, municipalities and autonomous regions. Methods: Using correlation analysis the relationship between RVD and nine geographic factors, build predictive models by factor analysis. Results: There is significant correlation between RVD and geographical factors, and the annual average temperature is most relevant geographical factors, a predictive model was built by factor analysis: Ŷ = 19.46 + 0.0007072X1 + 0.001034X2 -0.05412X3 -0.001405X4 -0.0006351X5 + 0.02903X6 + 0.01243X7 -0.01033X8 + 0.03816X9 ± 0.63. Then using Kriging interpolation interpolate the geographical distribution of Chinese healthy adult right ventricular diameter reference value in ArcGIS software. Main Conclusion: If geographic factors can be derived in anywhere of China, RVD can be calculated by the model, you can also get RVD from geographical distribution.

Open source software such as OpenDSS has given a lot of help to distribution network researchers and educators. With high penetration of distributed renewable energy resources into distribution network, tradition distribution steady state analysis software such as OpenDSS is faced with difficulty in handling distributed generators. Three-phase distributed generators are often modeled in sequence frame while unbalanced distribution network are usually modeled in phase frame. So a load flow in sequence-phase coupled frame is proposed to handle models described in both frames. Voltage controlled DGs which are difficult to cope with in OpenDSS are handled in proposed program. The steady state analysis platform is programmed with open source Modelica language and the main aim of this paper is to introduce an open source platform for active distribution network steady analysis include load flow and short circuit analysis which can be easily adopted and improved by other educators and researchers.

One consequence of the global technological advancement in conventional metallurgy is the fast depletion rate of valuable minerals, which are also becoming increasingly difficult to find in pure and economically viable forms. This has spurred more interest in technologies that investigate ability of different microorganisms to mobilize valuable metals from their ores via diverse metabolic processes. This study was carried out therefore to isolate, identify and characterize iron solubilizing bacteria from Iron stones of Agbaja iron ore mining site of Kogi State, Nigeria. Crushed samples in the range of 0.25µm and 0.75µm particle sizes were cultured in a modified 9k media to facilitate bacterial growth and pure cultures were then isolated and sub-cultured for further bioleaching studies. Morphological and biochemical analysis suggests that some of the bacteria identified are members of Acidithiobacillus spp, Pseudomonas spp, and Leptospirillum spp. Studies conducted on pure cultures and mixed consortium of the identified organisms shows that a mixture of the three organisms leached iron ore to about 96.16%. Also results of growth pattern due to bacteria countafter 24-72hours of incubation ranged between 0.1×10³ cfu/ml and 12.3 ×10³ cfu/ml for Acidithiobacillus spp. The need to explore the molecular characteristics of these organisms with a view to generating more information on the quality/quantity of their DNA for future cloning activities was also investigated in this work. DNA was extracted using zymo fungal/bacterial extraction mini prep kit ^TM (cat #6001) and subsequently subjected to 1% agarose gel electrophoresis. Visible bands were obtained with Alpha Innotech Gel Documentation Machine. DNA amplification was carried out using a pettier based thermo cycler PCR machine and electrophoresed on 1.5% agarose gel. Results of the PCR shows a visible band corresponding to 1.5kbp using this primer 27F (5¹-GAGTTTGATCCTGGCTCAG-3¹) and 1492R (5^1-GGTTACCTTGTTACGACT-3¹). DNA purity check shows two of the bacteria possess very good qualities for sequencing for further molecular analysis.

The land use land cover (LULC) map is extensively employed for different purposes. The LULC maps provided by the Portuguese National Geographic Information System (SNIG) are not ideal for continuous assessment and regular scrutiny. Machine learning (ML) algorithms applied in remote sensing (RS) data has been proven effective in image classification, object detection, and semantic segmentation. Previous studies have shown that Random Forest (RF) and Support Vector Machine (SVM) consistently achieved high accuracy for land classification. In this study, the application of RF and SVM classifiers, and object-based (OBIA) and pixel-based (PBIA) approaches with Sentinel-2A imagery was evaluated using Google Earth Engine (GEE) platform for land cover classification of a burned area in the Serra da Estrela Natural Park (PNSE), Portugal. This aimed to detect the land cover change and closely observe the burned area and vegetation recovery after the 2022 wildfire. The combination of RF and OBIA achieved the highest accuracy in all evaluation metrics. At the same time, a comparison with the Normalized Difference Vegetation Index (NDVI) map and Conjunctural Land Occupation Map (COSc) of 2023 year indicated that the SVM and PBIA map resembled the maps better.

Geochemical mapping of river sands in the Manufahi area of Timor-Leste revealed potential areas for future mineral exploration. River sand samples from the study area were collected and geochemically analyzed to identify anomalous concentration distributions of several valuable elements and locate potential target areas and geological formations that may host mineral deposits. The 26 major and minor elements were identified using wavelength-dispersive X-ray fluorescence. The river sands exhibited varying elemental concentrations, with Cr, Cu, Zn, and Ba showing deviations from the normal distribution patterns. Identification of geochemical anomalies is an important task in mineral exploration geochemistry. The mean + 2 standard deviations (mean+2STD), median + 2 median absolute deviations (median+2MAD), and Tukeyâs inner fence (TIF) methods were used to determine the geochemical thresholds. This study shows that TIF and principal component analysis (PCA) methods are highly effective in calculating appropriate threshold values and identifying relevant elemental associations. These approaches have proven useful for delineating target areas for mineral deposits, resulting in reliable outcomes. Four predicted target areas with high potential for deposits and mineralization anomalies of Cr, Cu, Ni, and Ba were delineated in the study area.

The purpose of this finite element analysis (FEA) was to evaluate the stress experienced by both the prosthetic components and the bone in relation to varying cement thicknesses ranging from 20 to 60 μm, utilized in the attachment of a zirconia crown on a conometric cap. The study focused on two distinct types of implants (Cyroth and TAC, AoN Implants, Grisignano di Zocco, Italy) featuring a Morse cone connection. Detailed three-dimensional (3D) models were created to repre-sent the bone structure (cortical and trabecular) and the prosthetic components, encompassing the crown, cement, cap, abutment, and the implant. The two implants were placed in a 1.5 mm sub-crestal position and subjected to an inclined load of 200 N at 45° on the crown. The results re-vealed that an increase in cement thickness resulted in a reduction of von Mises stress on the cor-tical bone for both Cyroth and TAC implants, while the decrease in stress on the trabecular bone was relatively less pronounced. However, the TAC implant exhibited a higher stress field and de-formation in the apical area compared to the Cyroth implant. In summary, this study investigated the impact of cement thickness on stress transmission across prosthetic components and peri-implant tissues by means of FEA analysis, indicating that the 60 μm cement layer manifested higher stress values nearing the material strength limit.

Cancer is a disease caused by the abnormal growth of cells in different parts of body is one of the top causes of death globally. Microarray gene expression data plays a critical role in the identification and classification of cancer tissues. Due to recent advancements in Machine Learning (ML) techniques, researchers are analyzing gene expression data using a variety of such techniques to model the progression rate &amp; treatment of cancer patients with great effect. But high dimensionality alongside the presence of highly correlated columns in gene expression datasets leads to computational difficulties. This paper aims to propose the use of ML classification techniques- Linear Discriminant Analysis (LDA) &amp; Random Forest (RF) for classifying five types of cancer (breast cancer, kidney cancer, colon cancer, lung cancer and prostate cancer) based on high dimensional microarray gene expression data. Principal component analysis (PCA) was used for dimensionality reduction, and principal component scores of the raw data for classification. Six distinct categorization performance measures were used to evaluate these approaches; RF method provided us with higher accuracy than LDA method. The method and results of this article should be helpful to researchers who are dealing with many genes in microarray data.

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley, one in the west/northwest, the other in the southeast. Wavelet analysis revealed the annual cycle to be most persistent temporal mode of PM10 variability in both subregions, with intermittent signals observed at time scales of around 120, 30~90 and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere.

Since the outbreak of COVID-19, social media platforms, such as Twitter, have experienced a tremendous increase in conversations related to Long COVID. The term “Long COVID” describes the persistence of symptoms of COVID-19 for several weeks or even months following the initial infection. Recent works in this field have focused on sentiment analysis of Tweets related to COVID-19 to unveil the multifaceted spectrum of emotions, viewpoints, and perspectives held by the Twitter community. However, most of these works did not focus on Long COVID, and the few works that focused on Long COVID have limitations. Furthermore, no prior work in this field has investigated Tweets where individuals self-reported experiencing Long COVID on Twitter. The work presented in this paper aims to address these research challenges by presenting multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between May 25, 2020, and January 31, 2023. First, the analysis shows that the average number of Tweets per month where individuals self-reported Long COVID on Twitter, has been considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings of sentiment analysis using VADER show that the percentage of Tweets with positive, negative, and neutral sentiment were 43.12%, 42.65%, and 14.22%, respectively. Third, the analysis of sentiments associated with these Tweets also shows that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.

Dynamic variables in the non-equilibrium systems of life are determined by catalytic activities. These relate to the expression of the genome. The extent to which such a variable depends on the catalytic activity defined by a gene has become more and more important in view of the possibilities to modulate gene expression or intervene with enzyme function through the use of medicinal drugs. With all the complexity of cellular systems biology, there are still some very simple principles that guide the control of variables such as fluxes, concentrations, and half-times. Using time-unit invariance we here derive a multitude of laws governing the sums of the control coefficients that quantify the control of multiple variables by all the catalytic activities. We show that the sum of the control coefficients of any dynamic variable over all catalytic activities is determined by the control of the same property by time. When the variable is at a maximum, minimum or steady, this limits the sums to simple integers like 0, -1, 1, and -2, depending on the variable under consideration. Some of the implications for biological control are discussed as is the dependence of these results on the precise definition of control.

Blockchain technology has been gaining great interest from a variety of sectors including healthcare, supply chain, and cryptocurrencies. However, Blockchain suffers from its limited ability to scale (i.e. low throughput and high latency). Several solutions have been proposed to tackle this. In particular, sharding proved that it is one of the most promising solutions to Blockchain’s scalability issue. Sharding can be divided into two major categories: (1) Sharding-based Proof-of-Work (PoW) Blockchain protocols, and (2) Sharding-based Proof-of-Stake (PoS) Blockchain protocols. The two categories achieve good performances (i.e. good throughput with a reasonable latency), but raise security issues. This article focuses on the second category. In this paper, we start by introducing the key components of sharding-based PoS Blockchain protocols. Then, we briefly introduce two consensus mechanisms, namely PoS and practical Byzantine Fault Tolerance (pBFT), and discuss their use and limitations in the context of sharding-based Blockchain protocols. Next, we provide a probabilistic model to analyze the security of these protocols. More specifically, we compute the probability of committing a faulty block and measure the security by computing the number of years to fail. Finally, we evaluate the effectiveness of the proposed model via numerical analysis.

Studies of dietary preferences of migratory species are of great importance as these species connect food webs of habitats across the migration route and thus represent trophic relationships between the spatially disjointed communities. Here we describe dietary preferences of threespine stickleback G. aculeatus in the White Sea during the spawning season using stable isotope and stomach content analyses. Both analyses indicated that during the spawning season, when sticklebacks spend most of the time in the inshore, their diet significantly consist of benthic species in contrast to the start of the spawning season when fishes migrating from the offshore are feeding on zooplankton. Also, we show that stickleback eggs contribute greatly to the diet of both male and female fishes. Using Bayesian mixing modelling we show that dietary preferences in females were broader than in males, and more variable during the spawning season. Males fed on eggs almost while guarding their nests. Both stomach contents and isotope signatures demonstrate that by the end of the spawning season sticklebacks again increase consumption of plankton, and isotope analysis proved to be more reliable tool to trace this change than stomach content analysis. Our results show that stable isotope and stomach content analyses well supplement each other in understanding of seasonal changes in dietary composition of stickleback.

Data integration has been proven to provide valuable information. The information extracted using data integration in the form of multiblock analysis can pinpoint both common and unique trends in the different blocks. When working with small multiblock datasets the number of possible integration methods is drastically reduced. To investigate the application of multiblock analysis in cases where one has few number of samples, we studied a small metabolomic multiblock dataset containing six blocks (i.e. tissue types), only including common metabolites. We used a single model multiblock analysis method called Joint and unique multiblock analysis (JUMBA) and compare it to a commonly used method, concatenated PCA. These methods were used to detect trends in the dataset and identify underlying factors responsible for metabolic variations. Using JUMBA, we were able to interpret the extracted components and link them to relevant biological properties. JUMBA shows how the observations are related to one another, the stability of these relationships and to what extent each of the blocks contribute to the components. These results indicate that multiblock methods can be useful even with a small number of samples.

Chronic diseases are still known as incurable diseases, and we suspect that the medical research model is unfit for characterizing chronic diseases. In this study, we examined accuracy and reliability required for characterizing chronic diseases, reviewed implied presumptions in clinical trials and assumptions used in statistical analysis, examined sources of variances normally encountered in clinical trials, and conducted numeric simulations by using hypothetical data for several theoretical and hypothetical models. We found that the sources of variances attributable to personal differences in clinical trials can distort hypothesis test outcomes, that clinical trials introduce too many errors and too much inaccuracies that tend to hide weak and slow effects of treatments, and that the means of treatments used in statistical analysis have little or no relevance to specific patients. We further found that a large number of uncontrolled co-causal or interfering factors normally seen in human subjects can greatly enlarge the means and the variances of the experimental errors, and the use of high rejection criteria (e.g., low p values) further raises the chances of failing to find treatment effects. As a whole, we concluded that the research model using clinical trials is wrong on multiple grounds, under any of our realistic theoretical and hypothetical models, and that misuse of statistical analysis is most probably responsible for failure to identify treatment effects for chronic diseases and to detect harmful effects of toxic substances in the environment. We proposed alternative experimental models involving the use of single-person or mini optimization trials for studying low-risk weak treatments.

Non-toroidal shape primary pass through protection current transformers (CTs) are used to measure high currents. Their design provides them with a big airgap that allow passing several cables per phase though them, which is the main advantage versus toroidal types, as the number of CTs required to measure the whole phase current is drastically reduced. The cables pass through the transformer window can be in several positions. As the isolines of the magnetic field generated by the primary currents are centered in the cables, if these cables are not centered in the transformer window, the magnetic field will be non-uniform along the transformer core. Consequently, local saturations can appear if the cables are not properly disposed causing the malfunction of the CT. In this paper, the performance of a non-toroidal shape protection CT is studied. The research is focused on the cable position influence in possible partial saturations of the CT when it is operating near to its accuracy limit. Depending on the cable position, the ratio of the primary and secondary currents can depart from assigned ratio. The validation of this phenomenon has been carried out via Finite Element Analysis (FEA) showing that partial transformer core saturations appears in areas of the magnetic core close to the cable. Applying FEA, also the admissible accuracy region for cable positioning inside the CT is delimited. Finally, the simulation results are ratified with experimental tests performed in non-toroidal protection CTs varying the primary cables’ positions, which are subjected to currents up to 5 kA, achieving satisfactory results. From this analysis, installation recommendations are given.

In this paper we report the possibility of using the X-ray powder diffraction (XRPD) technique to detect the gamma-hydroxybutyric acid (GHB) as its sodium salt in different beverages but because it is not possible to freely buy them, beta-hydroxybutyric acid (BHB) and its sodium salt (NaBHB) were used as a model to fine tune an X-ray diffraction method for the qualitative analysis of the sodium salt of GHB. The method requires a small quantity of beverage, and an easy sample preparation, that consists only in the NaOH addition to the drink and a subsequent drying step. The dry residue obtained can be easily analyzed with XRPD using a single crystal X-ray diffractometer to exploit its high sensitivity that allows very fast patterns collection. Several beverages with different NaBHB:NaOH molar ratios were tested and the results show that NaBHB is detected in all drinks analyzed when NaBHB:NaOH molar ratio is 1:50 using characteristic peak at very low 2θ values that permits to detect its presence also in complex beverage matrices. Moreover, depending on the NaOH amount added, shifting and/or splitting of the characteristic peak of the NaBHB salt was observed, and the origin of this behavior was investigated.

Understanding the dynamics of land use and land cover (LULC) change in today's context of rapid urbanization is critical for sustainable urban planning and environmental conservation. Therefore, the research aim is to understand the LULC changes in Chattogram Metropolitan Area and to assess the policy implications of these changes for sustainable urban development. Through a comparative analysis of LULC maps for two periods (1997–2007; 2007–2017), we investigated the transformation of a detailed planning zone before and after detailed area plan (DAP) approval. Using a mixed-method approach integrating quantitative data with policy analysis, we elucidated the impacts of urbanization trends on local ecosystems—specifically the conversion of forest cover and waterscape use. The findings reveal a significant conversion of forested and waterscape areas to urban and peri-urban landscapes, highlighting the urgent need for sustainable planning interventions that focus on these peri-urban areas. The study further critiques the DAP's effectiveness, revealing a disconnect between regional policy implementation and local outcomes. This research proposes a validated, scalable framework for urban master plans that adapts to the complex socio-ecological contexts of rapidly urbanizing regions.

Ambitious biodiversity goals to protect 30% or more of the Earth’s surface by 2030 (30x30) require strategic near-term targets. To define areas that must be protected to prevent the most likely and imminent extinctions, we propose Conservation Imperatives—16,825 unprotected sites harboring rare and threatened species and spanning ~164 Mha of the terrestrial realm. We estimate that protecting the Conservation Imperatives would cost approximately US$169 billion [90% probability: $146 — $228 billion]. Globally, 38% of the 16,825 sites are either adjacent to or within 2.5 km of an existing protected area, potentially reducing land acquisition and management costs. These sites should be prioritized for conservation action over the next five years as part of a broader strategy to expand the global protected area network. The expansion of global protected areas between 2018–2023 incorporated only 7% of sites harboring range-limited and threatened species, highlighting a renewed urgency to conserve these habitats. Permanently protecting only 0.74% of land found in the tropics, where Conservation Imperatives are concentrated, could prevent the majority of predicted near-term extinctions once adequately resourced. We estimate this cost to be US$29 billion to US$46 billion per year over the next five years. Multiple approaches will be required to meet long-term protection goals: providing rights and title to Indigenous Peoples and Local Communities (IPLCs) conserving traditional lands, government designation of new protected areas on federal and state lands, and land purchase or long-term leasing of privately held lands.

Digital twins are gaining in popularity for simulating complex natural and urban environments. In this context, accurate segmentation of objects within 3D urban environments is of crucial importance. The aim of this project is to develop a methodology for extracting buildings from textured 3D meshes. To this end, PicassoNet-II, a semantic segmentation architecture is employed. The methodology also incorporates Markov field-based contextual analysis to assess post-segmentation features. In addition, building instantiation is performed using cluster analysis algorithms. Training this model to fit various datasets requires a large amount of annotated data, both from Quebec City, Canada, and from simulated data. Experimental results show that the use of simulated data improves segmentation accuracy, and the DBScan algorithm proves effective in extracting isolated buildings. This project paves the way for improved applications in 3D urban modeling, offering opportunities in fields ranging from urban planning to resource management. The positive results with simulated data reinforce the impact of this research on improving digital models of our ever-changing urban environments.

Expression analysis of small noncoding RNA (sRNA), including microRNA, piwi-interacting RNA, small rRNA-derived RNA, and tRNA-derived small RNA, is a novel and quickly developing field. Despite a range of proposed approaches, selecting and adapting a particular pipeline for transcriptomic analysis of sRNA remains a challenge. This paper focuses on the identification of the optimal pipeline configurations for each step of human sRNA analysis, including reads trimming, filtering, mapping, transcript abundance quantification and differential expression analysis. Based on our study, we suggest the following parameters for analysis of human sRNA in relation to categorical analyses with two groups of biosamples: (1) trimming with the lower length bound = 15 and the upper length bound = \(Read\ length - 40\% Adapter\ length\); (2) mapping on a reference genome with bowtie aligner with one mismatch allowed (-v 1 parameter); (3) filtering by mean threshold > 5; and (4) analyzing differential expression with DESeq2 with adjusted p-value < 0.05 or limma with p-value < 0.05 if there is very little signal and few transcripts.

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had the ‘neutral’ emotion. The other emotions - ‘bad’, ‘good’, ‘terrible’, and ‘great’ were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domains embedded in the tweets were found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other URLs. Finally, to support similar research and development in this field centered around the analysis of tweets, we have developed an open-access Twitter dataset that comprises tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.

The fatigue life of welded joints under cyclic loading is a complex process that can be roughly divided into fatigue crack initiation, crack propagation and final fracture. Usually, these phases are evaluated separately. Several methods are available to estimate the extent of the different phases in welded components, such as stress-based fatigue concepts for crack initiation or linear elastic fracture mechanics for crack propagation; however, distinguishing these phases in welded components is a complex procedure and often relies on assumptions such as transition crack sizes. The objective of this study is to investigate the relationship between fatigue crack initiation and propagation in welded joints using artificially notched specimens with welded joints characteristics of different notch acuity (different radii and opening angle). The experiments show that the investigated relationship basically depends on the notch acuity, the load level and the stress ratio.

The aim of this work is to explain the behaviour of the multiresistance percentage of Pseudomona aeruginosa in some countries of Europe through a multivariate statistical analysis and machine learning validation, using data from the European Antimicrobial Resistance Surveillance System, the World Health Organization and the World Bank. First, we will use a descriptive analysis and a principal components analysis. Then, we use a k-means clustering to determine the countries and regions that are most affected by the antibiotic resistance. Second, we expand the database by adding some socioeconomic, governance and antibiotic-consumption variables. We then run a data panel regression analysis to determine some functions that relates the multiresistance percentage with those new variables. Finally, we use machine learning techniques to validate a pooling panel data case, using XGBoost and random forest algorithms. The results of the data panel analysis indicate that the most important variables for the multiresistance percentage are corruption control and the rule of law. Similar results are found with the machine learning validation analysis, where the human development index is an additional important variable for the multiresistance percentage.

Cancer cells are adept at reprogramming energy metabolism and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism and ROS detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

Hardened air void analysis provides essential information of concrete freeze-thaw durability based on the size and spacing of air voids in the material. As the physical freeze-thaw experiment is time-consuming and costly, the characteristics of concrete air voids are often deemed as a proxy of the freeze-thaw performance. This analysis is typically done by measuring the 2D air void intersections on polished samples, but the current interpretation of the 2D void characters does not accurately represent the actual void structure in 3D. To solve this problem, a 2D-to-3D unfolding technique has been proposed in the field of stereology. However, the unfolding analysis is known to be sensitive to several factors, such as void population and size along with a binning scheme, where improper unfolding can considerably bias the prediction of the actual concrete void system. This study investigates the optimal strategy of conducting the unfolding analysis for concrete. The investigation is carried out on both idealized void systems to interrogate the influence of the critical factors individually, and real concrete samples with varying levels of air entrainment to assess the concrete-specific impacts. The concrete void system is studied based on a stereological model emulating the intersected 3D air voids on the surface of polished concrete. The results highlight that, for unfolding concrete voids, logarithmic binning scheme is far more accurate to linear binning. The low unfolding error of the concrete samples indicates that the proposed methodology enables an accurate restoration of 3D void size distribution.

In order to assess the human exposure risks from the release of contaminants from water pipes made of polyvinyl chloride (PVC), experiments were carried out by subjecting the PVC pipe material to burning and leaching conditions followed by analysis of the emission and leachate samples. The emissions of burning pipes were analyzed by both infrared spectrometry and gas chromatography-mass spectrometry (GC-MS). The emission results indicate the presence of chlorinated components including chlorine dioxide, methyl chloride, methylene chloride, allyl chloride, vinyl chloride, ethyl chloride, 1-chlorobutane, tetrachloroethylene, chlorobenzene, and hydrogen chloride were detected in the emissions of burning PVC pipes. Furthermore, the concentrations of benzene, 1,3-butadiene, methyl methacrylate, carbon monoxide, acrolein, and formaldehyde were found at levels capable of affecting human health adversely. The analysis of PVC pipe leachates using GC-MS shows that there are 40-60 tentatively identified compounds, mostly long-chain hydrocarbons such as tetradecane, hexadecane, octadecane, and docosane, were released when the burned PVC materials were soaked in deionized water for one week. Quantitative analysis shows that 2-butoxyethanol, 2-ethyl-1-hexanol, and diethyl phthalate were found in the burned PVC polymer at the average levels of 2.7, 14.0, and 3.1 micrograms per gram (g/g) of pipe material. This study has significant implications for understanding the benzene contamination of drinking water in the aftermath of wildfires that burned polymer pipes in California.

The mutual information between the state of a neural network and the state of the external world represents the amount of information stored in the neural network that is associated with the external world. In contrast, the surprise of the sensory input indicates the unpredictability of the current input. In other words, this is a measure of inference ability, and an upper bound of the surprise is known as the variational free energy. According to the free-energy principle (FEP), a neural network continuously minimizes the free energy to perceive the external world. For the survival of animals, inference ability is considered to be more important than simply memorized information. In this study, the free energy is shown to represent the gap between the amount of information stored in the neural network and that available for inference. This concept involves both the FEP and the infomax principle, and will be a useful measure for quantifying the amount of information available for inference.

We examine how observations can be used to evaluate an air quality analysis by verifying against passive observations (i.e. cross-validation) that are not used to create the analysis and we compare these verifications to those made against the same set of (active) observations that were used to generate the analysis. The results show that both active and passive observations can be used to evaluate of first moment metrics (e.g. bias) but only passive observations are useful to evaluate second moment metrics such as variance of observed-minus-analysis and correlation between observations and analysis. We derive a set of diagnostics based on passive observation–minus-analysis residuals and we show that the true analysis error variance can be estimated, without relying on any statistical optimality assumption. This diagnostic is used to obtain near optimal analyses that are then used to evaluate the analysis error using several different methods. We compare the estimates according to the method of Hollingsworth Lonnberg, Desroziers, a diagnostic we introduce, and the perceived analysis error computed from the analysis scheme, to conclude that as long as the analysis is optimal, all estimates agrees within a certain error margin. The analysis error variance at passive observation sites is also obtained.

From some perspective, every natural phenomenon is just a combination of many elementary random events clustering and forming a pattern. Nonetheless, the nature of random events differs from one field of study to another. This research is an attempt to determine whether volatile stock price movement, considered random, accords to the randomness of real world phenomena. To accomplish the task, a deep learning model was built, trained on preprocessed data and evaluated to calculate it's accuracy. The acquired results were then compared, and it turned out that stock prices are very similar in randomness to natural phenomena as the model trained on large amounts of stock data was increasing the accuracy of predictions of all real world parameters.

The COVID-19 pandemic has drastically affected global mental health, with news media playing a central role in shaping public emotions. This study examines how anxiety and fear were represented in online news articles from Indonesia and Taiwan, focusing on their potential impact on public health perception during the pandemic. By comparing these two countries, the research aims to identify patterns of emotional tone in media coverage. A sentiment analysis was conducted using LIWC and Seance on a dataset of online news articles published in Indonesia and Taiwan. LIWC was utilized to quantify anxiety-related content, while Seance analyzed fear-related expressions. These tools provided a comprehensive assessment of the emotional tone across both regions. Indonesian news exhibited higher levels of anxiety-related language, especially during periods of escalating COVID-19 cases. In contrast, Taiwanese news maintained a more balanced tone, although fear-related language increased during key pandemic events such as new variants and vaccination rollouts. Both anxiety and fear peaked during times of heightened public health concern. The findings suggest significant emotional differences in media coverage between Indonesia and Taiwan, reflecting distinct public health responses and media strategies. Understanding these emotional patterns can inform more effective health communication during crises.

This article presents a kinematic analysis of a 4-degree-of-freedom (DOF) robotic arm equipped with a 3-finger gripper, specifically designed for harvesting fruits of varying sizes. The kinematic modeling is thoroughly detailed, encompassing the equations of motion, homogeneous transformation matrices, and kinematic solutions required for the efficient operation of the robotic arm. This analysis is crucial for optimizing both the design and programming of agricultural robots, aiming to enhance harvesting efficiency and reduce labor costs. Additionally, an in-depth examination of the 3-finger gripper’s movement is provided, illustrating how MATLAB facilitates the simulation and visualization of its dynamic behavior during the opening and closing processes. The benefits of using MATLAB for this purpose are emphasized, including its ease of implementation, advanced simulation capabilities, and seamless integration with other tools to optimize the performance of robotic systems in agricultural settings. This study makes a valuable contribution to the field of agricultural robotics, offering technical insights that can be applied in the development of sophisticated robotic solutions for the modern agricultural industry.

This paper presents a numerical study of topology-optimised pin-end aluminium alloy columns using finite element analysis (FEA). The FEA models integrate geometric imperfections and material nonlinearity, validated against experimental findings from existing literature. Furthermore, modern design methodologies including Eurocode 9, Direct Strength Method (DSM), and Continuous Strength Method (CSM) are employed to assess the maximum load capacity of such columns. Parametric investigations encompass diverse parameters such as varied cross-sections, column lengths, and global and local imperfections. By analysing a total of 288 FE models, incorporating 16 column cross-sections across two lengths with nine distinct imperfections, the study compares results with those derived from modern design methodologies. Thus, this research elucidates the behaviour of novel cross-sections and the application of contemporary design techniques in their analysis.

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.

Objective: Despite long simulation times, recently developed finite element analysis (FEA) models of knee joints have demonstrated their suitability for predicting individual risk of onset and progression of knee osteoarthritis. Therefore, the objective of this study was to assess the feasibility of machine learning (ML) to replicate outcomes obtained from FEA when simulating mechanical responses and predicting cartilage degeneration within knee joint. Design: Two ML models based on the Gaussian Process Regression (GPR) algorithms were developed. The first model (GPR1) utilized age, weight, and anatomical joint dimensions as predictor variables to predict tissue mechanical responses and cartilage degeneration based on FEA data. The second model (GPR2) utilized age, weight, height, and gender to predict anatomical joint dimensions, which were then used as inputs in the GPR1 model. Finally, the GPR1 and combined GPR1+GPR2 models were used to investigate the importance of clinical imaging when making personalized predictions for knees from healthy subjects with no history of knee injuries. Results: In the GPR1 model, R2 of 0.9 was exceeded for most of the predicted mechanical parameters. The GPR2 model was able to predict knee shape with R2 of 0.67 - 0.9. Both GPR1 and combined GPR1+GPR2 models offered equally good performance (AUC = 0.73 - 0.74) at classifying patients at high risk for the onset and development of knee osteoarthritis. Conclusions: In the future, real-time and easy-to-use GPR models may provide a rapid technology to evaluate mechanical responses within knee for researchers or clinicians who have no former knowledge from FEA.

With growing global emphasis on sustainable development and social responsibility, Environmental, Social, and Governance (ESG) ratings have emerged as critical metrics to evaluate commercial banks’ operating performance. The paper utilizes panel data spanning 2009 to 2022 from large state-owned and joint-stock commercial banks in China. It employs a two-way fixed effect model and robustness tests, alongside tests for heterogeneity analysis and moderating effect to conduct empirical analysis. The findings of the fixed effect model indicate that improvements in ESG performance have advantageous impacts on commercial banks’ operating performance, particularly with higher growth rate for operating income Robustness tests including mixed regressions, Tobit tests, variable substitutions, and tests of endogenous bias have validated that initial regression findings still hold. Tests of heterogeneity analysis reveal that among large state-owned commercial banks, banks with higher leverage, larger major shareholder ownership, and larger banks scales, ESG performance can significantly affect the banks’ operating performance, and the relationship exhibits positive. Furthermore, the paper finds that GDP growth rate, CPI and analysts’ coverage can strengthen the relationship between ESG performance and commercial banks’ operating performance through moderating effects. The research provides policy proposals and strategic recommendations tailored for policymakers, banks, and investors, building on these insights.

Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand, while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements and thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art approaches of ESs aimed at improving energy efficiency in industry, with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, the review reveals research gaps, pointing toward promising topics for future research.

Cross-lingual sentiment analysis is crucial for understanding and interpreting emotions expressed in text across diverse linguistic contexts. However, cross-lingual sentiment analysis faces challenges such as differences in emotional lexicons, data imbalance, and the need for multi-language sentiment normalization. In this study, we propose a novel approach to address these challenges by leveraging low-resource language training techniques to enhance cross-lingual sentiment analysis. Our method aims to bridge the gap in emotional lexicons by adapting sentiment analysis models to diverse linguistic contexts. Additionally, we tackle issues of data imbalance through innovative data augmentation strategies tailored to each language, and we introduce a multi-language sentiment normalization technique to ensure consistent sentiment interpretation across different languages. Our method achieved state-of-the-art results in twelve languages across four domains, demonstrating superior performance in both text-level and sentence-level sentiment analysis tasks. By conducting comprehensive evaluations across diverse linguistic contexts and domains, we showcase the versatility and effectiveness of our approach in achieving top-tier results in cross-lingual sentiment analysis.

Performance analysis of companies is critical for decision-makers in the financial sector, particularly when resources are limited. This study investigates the properties of various Multi-Criteria Decision-Making (MCDM) methods, critically comparing their results in evaluating the performance of consumer goods companies listed on the Nigerian Stock Exchange (NSE). The analysis utilizes financial ratios, with weights derived using Buckley's Column Geometric Mean Method, a fuzzy ranking approach. The study concludes by comparing TOPSIS, VIKOR, and ELECTRE methods, determining the final rankings of these companies, and interpreting these rankings based on financial ratios over a five-year period (2016-2020).

Background: The increasing prevalence of severe obesity among children and adolescents poses a significant challenge for pediatricians and general practitioners. This study aimed to investigate the relationships between biochemical results, anthropometry, blood pressure measurements and bioimpedance analysis (BIA)-derived parameters, to identify potential cardiometabolic complications associated with severe obesity. Methods: The study included 347 children (162 boys, 185 girls) aged 0-19 years, meeting criteria for severe obesity based on BMI thresholds for different age groups. Patients were recruited in four pediatric endocrinology centers in Poland (Zabrze, Cracow, Rzeszow, Szczecin). Each participant underwent anthropometric measurements, pubertal stage assessment, blood pressure measurement, biochemical and hormonal tests and BIA. Results: BMI showed significant associations with fat mass percentage (FM%) and waist-to-height ratio (WHtR), but not waist-to-hip ratio (WHR). The relationship between BMI and FM% was stronger in girls and prepubertal children. Metabolic syndrome (MetS) Z-score showed a strong positive correlation with BMI in pubertal children. Negative correlation between HDL and triglycerides was observed only in boys. Prepubertal children exhibited more significant correlations despite a smaller sample size and shorter duration of obesity. Conclusions: Considering multiple parameters beyond BMI alone provides a better understanding of cardiometabolic risks associated with severe obesity in children. MetS Z-score was not a reliable indicator of increased cardiometabolic risk in younger children. Early-onset severe obesity was associated with a higher risk of metabolic complications. Early intervention is crucial to mitigate metabolic complications in this population.

While in the past, especially numerical approaches such as regression models and p-values were utilized to investigate whether the functional form between age and happiness is linear or U-shaped in a given country, recent advances have shown that such approaches can be misleading, and graphical analyses should amend the analyses. However, these have the downside that they rely to some extent on subjective interpretations and are hardly quantifiable. If applied carelessly, they can be misleading as well. We suggest two easily computed statistics to combine graphical and numerical approaches. We demonstrate their usage with ESS data (N > 440,000) and show how they enable a more nuanced investigation of functional forms. Furthermore, we discuss how statistical uncertainty can be handled in the age-happiness debate and emphasize practical significance, which needs to be kept in mind.

Pneumococcal disease, caused by Streptococcus pneumoniae is the leading cause of high mortality in children worldwide. The tremendous direct cost of hospital admissions and significant indirect costs from productivity loss contributed considerably to the economic burden, with vaccination being the only efficient way against the illness. Our study aims to summarize the cost-effectiveness of the pneumococcal conjugate vaccine (PCV) implemented in the pediatric population. Employing online databases from Pubmed, Embase, and Medline, we looked for economic evaluation from 2018 until March 2024. The Incremental Cost-Effectiveness Ratios (ICER) and Quality-Adjusted-Life-Years (QALY) were the primary outcomes for measuring PCV's cost-effectiveness. A 28-item CHEERS 2022 checklist was applied to assess the quality of collected studies. Of the 16 papers found, 9/16 discussed the lower-valent vaccine (PCV13, PCV10) and 7/16 examined the higher-valent vaccine. (PCV20, PCV15). PCV13 and PCV10 required more cost and generated more QALY compared to no vaccination. Both PCV15 and PCV20 averted substantial healthcare costs and yielded greater quality of life than PCV13. Additionally, PCV20 was a dominant strategy compared to PCV15. Utilizing PCV13 is a very cost-effective option compared to not getting vaccinated. Transitioning from PCV13 to PCV20 would result in higher QALY gain and more cost-saving than switching to PCV15.

The Kampar Peninsula, a significant peatland region in Riau, Indonesia, is vital for its carbon re-serves and biodiversity, including the Sumatran tiger. Covering approximately 735,091 hectares, it comprises critical peat domes essential for hydrological balance. This research investigates land use changes from 1990-2020 using the Normalized Difference Fraction Index (NDFI) to map disturb-ances and land cover changes. Additionally, interviews and regulatory analysis were conducted to examine the historical drivers of land use change in the Kampar Peninsula. In 2020, natural forests covered 433,395.20 hectares (59.9% of the total area), industrial pulpwood plantations (primarily acacia) covered 27.3%, oil palm plantations accounted for 10.4%, infrastructure occupied 2.4%, and water bodies made up 0.9%. Key drivers of land use change include the construction of access roads by oil companies since 1975 and government policies during the New Order period (1980s-1998) promoting forest exploitation and transmigration programs. These activities have significantly transformed the Kampar Peninsula's land cover, highlighting the need for sustainable management and conservation efforts.

In recent decades, the cultivation of avocados has expanded throughout the Iberian Peninsula, with most of the production occurring on the southern Atlantic and Mediterranean coast, as well as in the Canary Islands and the north. This expansion is due to high demand and high prices, which have made the crop very attractive. However, climatic suitability criteria have not always been followed, putting sustainability at risk. Avocados originate from tropical and subtropical areas and have very specific climatic requirements that must be met to ensure good production. This study analyzed key climatic variables, including winter cold damage, pollen viability, and flowering and fruit set temperatures. Using daily climate data from 1975 to 2022, advanced spatial analysis techniques were applied to produce suitability maps. The results indicate that the ex-pansion of the crop is possible in certain areas of southern Andalusia, but not in the rest of the peninsula. Minimum winter and March temperatures are the main limiting factors. This work provides valuable insights into the potential for sustainable agricultural intensification, the sus-tainability of agricultural decision-making, and resilience to climate change.

The integration of advanced image analysis using artificial intelligence (AI) is pivotal for the evolution of autonomous vehicles (AVs). This article provides a thorough review of the most significant datasets and the latest state-of-the-art AI solutions employed in image analysis for AVs. Datasets such as Cityscapes, NuScenes, and CARLA form the benchmarks for training and evaluating different AI models, with unique characteristics catering to various aspects of autonomous driving. Key AI methodologies, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer models, and Generative Adversarial Networks (GANs), are discussed. The article also presents a comparative analysis of various AI techniques in real-world scenarios, focusing on semantic image segmentation, 3D object detection, and vehicle control in virtual environments. Simultaneously, the role of multisensor datasets and simulation platforms like AirSim, TORCS, and SUMMIT in enriching the training data and testing environments for AVs is highlighted. By synthesizing information on datasets, AI solutions, and comparative performance evaluations, the article serves as a crucial resource for researchers, developers, and industry stakeholders. Offering a clear view of the current landscape and future directions in autonomous vehicle image analysis technologies.

Financial market data analysts are increasingly turning towards machine learning techniques for data analysis and making decisions. This paper explores how knowledge graphs, essentially a network of real entities and their relationships, can be used to improve collaborative financial market data analysis and make it more efficient and user friendly. This paper reviews the current state of financial market data analysis, the challenges in deploying machine learning models in industrial environments, and the concepts such as ML-Ops and knowledge graphs. A software architecture comprising various roles, layers and services such as Data Engineers, Data Analysts, public users, APIs, workflows, analytics libraries, UX layer, Business Layer, etc. is introduced and described in detail. Then, it discusses how knowledge graphs can be used to enhance collaborative financial market data analysis. Finally, a case study is presented to demonstrate the usage of the whole system, within the context of financial market data analytics of equities.

Este estudio examina la implementación de redes bayesianas para el diagnóstico de afecciones respiratorias utilizando datos clínicos. Se analizaron 5725 registros de una clínica privada en Francia, obtenidos de Kaggle, centrándose en seis variables clave: glucosa en sangre, frecuencia cardíaca, saturación de oxígeno, presión arterial, temperatura y estado de salud del paciente. El objetivo era crear un modelo que mejorara la precisión y eficiencia en el diagnóstico de infecciones respiratorias. Se desarrolló una red bayesiana para modelar las interacciones fisiológicas en condiciones respiratorias, destacando la influencia de la glucosa en sangre en la oxigenación y la interacción entre la frecuencia cardíaca y la saturación de oxígeno. La mediana de la temperatura corporal fue de 38,18 °C y la mediana de la frecuencia cardíaca fue de 92,00 lpm. El modelo, basado en sólidos principios médicos, considera tanto los mecanismos patológicos directos como las respuestas compensatorias del cuerpo. Se utilizó Netica para la simulación y análisis del modelo, proporcionando una visualización clara de las probabilidades condicionales y las relaciones entre las variables. Los hallazgos demuestran que las redes bayesianas son herramientas efectivas para apoyar el diagnóstico de afecciones respiratorias, proporcionando una evaluación integral del estado de salud del paciente. La matriz de confusión reveló una precisión del 94%, una especificidad del 100% y una sensibilidad del 86,53%. Además, la calidad de la prueba para el estado "enfermo" mostró una sensibilidad de hasta el 98,49% y una especificidad de hasta el 98,85%, con un área bajo la curva ROC (AUC) de 0,9592. Estos resultados indican que el modelo tiene un alto rendimiento en la clasificación correcta de los casos de salud y enfermedad normales. Este enfoque tiene el potencial de mejorar la precisión y la eficiencia del diagnóstico, especialmente en entornos clínicos que requieren decisiones rápidas y precisas. Este estudio destaca la aplicación innovadora de las redes bayesianas en medicina respiratoria, ofreciendo una herramienta prometedora para mejorar el diagnóstico y el manejo de pacientes con afecciones respiratorias, y estableciendo una base para futuras investigaciones en el uso de métodos probabilísticos en el diagnóstico médico.

The proliferation of wearable technology enables the generation of vast amounts of sensor data, offering significant opportunities for advancements in health monitoring, activity recognition, and personalized medicine. However, the complexity and volume of this data present substantial challenges in data modeling and analysis, which have been tamed with approaches spanning time series modeling to deep learning techniques. The latest frontier in this domain is the adoption of Large Language Models (LLMs), such as GPT-4 and Llama, for data analysis, modeling, understanding, and generation of human behavior through the lens of wearable sensor data. This survey explores current trends and challenges in applying LLMs for sensor-based human activity recognition and behavior modeling. We discuss the nature of wearable sensors data, the capabilities and limitations of LLMs to model them and their integration with traditional machine learning techniques. We also identify key challenges, including data quality, computational requirements, interpretability, and privacy concerns. By examining case studies and successful applications, we highlight the potential of LLMs in enhancing the analysis and interpretation of wearable sensors data. Finally, we propose future directions for research, emphasizing the need for improved preprocessing techniques, more efficient and scalable models, and interdisciplinary collaboration. This survey aims to provide a comprehensive overview of the intersection between wearable sensors data and LLMs, offering insights into the current state and future prospects of this emerging field.

(1) Background: This study aimed to compare the effect of polyetheretherketone (PEEK) and titanium abutments on implant prosthetic systems and supporting bone using three-dimensional finite element analysis (FEA); (2) Methods: A three-dimensional finite element analysis was conducted using CATIA and Abaqus software to model mandibular first molar implant systems with titanium and PEEK abutments. Under external loading conditions, finite element analysis was conducted for the stresses in implant components and surrounding bones of each group; (3) Results: The implant fixture of the PEEK model exhibited the highest von Mises stress (VMS). The lowest VMS was observed in the abutment screw of the titanium model. Both implant systems demonstrated similar stress distribution and magnitudes in cortical and cancellous bones; (4) Conclusion: PEEK abutments show similar stress distribution in surrounding bone compared to titanium. However, PEEK absorbs the stresses within the implant system and exhibits the highest VMS values due to its low mechanical and physical properties. Therefore, PEEK abutments need improved mechanical properties for better clinical application.

A high-performance broadband High Power Amplifier (HPA) has been developed employing 250 nm gate length Gallium Nitride (GaN) technology from the \href{https://www.leonardo.com/en/innovation-technology/technological-areas/electronics}{LEONARDO} foundry GN25. This HPA employs the a corporate configuration for FET transistors, optimizing its performance. This article provides an in-depth study and analysis of the critical design parameters of such HPAs in Monolithic Microwave Integrated Circuit (MMIC) technology. The designed MMIC chip measures 7.5 x 7 mm^2 and includes three stages, each carefully optimized to maximize efficiency and performance. Both the design and the measurement results of amplifier are presented. he most remarkable feature of this chip is an operational bandwidth exceeding two octaves, covering a wide RF frequency range from 2 to 10 GHz (2.23:1 ratio. Additionally, the amplifier achieves an output power higher than 43 dBm, along with a power-added efficiency(PAE) surpassing 20% in the mentioned frequency range (2-10 GHz). This HPA is suitable to be integrated into future Transmit and Receive Modules (TRMs) for Active Electronically Scanned Array (AESA) systems. These systems include multiple capabilities in a single device, such as Radar, Electronic Warfare (EW), and Communications (COMMs) functions, demonstrating the versatility and high efficiency of this design in advanced multifunctional applications

Flap surgery is a common method used to cover defects following tumor ablation, trauma, or infection. However, insufficient vascularity in the transferred flap can lead to flap necrosis and failure. Proper postoperative monitoring is essential to prevent these complications. Recently, research has explored the use of infrared thermal imaging in plastic surgery, leading to its clinical application. This study investigates the use of a forward-looking infrared (FLIR) camera to monitor blood flow in flaps. In the study, 28 rats underwent reverse McFarlane flap surgery, and their flaps were analyzed using a FLIR thermal imaging camera seven days post-surgery. Additionally, thermal images of flaps were taken on postoperative days 0, 1, 2, 3, and 7 in 22 patients. The study focused on temperature differences between normal skin and the perforator compared to the average flap temperature. Results showed that the temperature difference was higher in the necrosis group and increased over time in cases of total necrosis. A lower perforator temperature compared to the flap's average indicated vascular compromise, potentially leading to flap failure. The FLIR camera, being contact-free and convenient, shows promise for understanding and inferring the clinical progression of flaps in postoperative monitoring.

Pumpo is a Simmental breed and an essential livestock resource in the nucleus genetic cattle of Peru. This study provides a draft genome sequence of a top bull using a de novo assembly approach on the Illumina Novaseq X platform, yielding 208 GB of raw sequencing data with 150 bp paired-end reads. The final genome assembly resulted in a size of 2.06 Gb with an N50 contig length of 108 Mb and a completeness of 95.7% according to BUSCO analysis. A total of 973,925 simple sequence repeats (SSRs) were identified, with a predominance of mononucleotide repeats. The genome showed low heterozygosity (0.568%) and moderate repeatability (11.5%), aligning with other Bos taurus genomes. Reference-guided scaffolding improved the assembly quality significantly, producing an N50 scaffold value of 108 Mb. The SSR analysis of the Pumpo genome identified 973,925 SSRs with a frequency of 2,808 SSRs per kilobase, predominantly mononucleotide repeats, and 85,453 found in compound formations. Obtaining knowledge of the genome of a breeding Simmental bull is essential to optimize breeding programs and improve productivity.

Collaboration across the agriculture supply chain is essential to address the high-yield demand and sustainable practices amid global overpopulation. Limited resources, such as soil, are compromised by excessive chemical agents and nutrient use. The Internet of Things (IoT) and smart farming offer solutions by optimizing agent application, data analysis, and farm monitoring. Evidence from numerous studies indicates that collaboration in the supply chain, including farmers, can improve efficiency and productivity, reduce costs, and enhance crop quality. This research focuses on implementing IoT technology to enhance decision-making for crop yield optimization and sustainable practices on a real farm. By collaborating with a farm in the southern region of Zagreb, Croatia, farmers were trained on sensor usage and yield monitoring. Small farms in that region face challenges in improving yields due to limited capacity and lack of entrepreneurial skills. The DMAIC methodology was applied to define the problem and measure relevant parameters. The analysis demonstrated consistent patterns between electrical conductivity (EC) measurements and potassium levels in soil. It suggests the potential for estimating potassium concentrations based on EC readings, or vice versa. Leveraging EC as a proxy for potassium levels could offer a cost-effective means of assessing soil fertility and nutrient dynamics. Additionally, the PCA biplot analysis highlighted that pH value behaved independently. Understanding these dynamics enhances knowledge of soil variability and informs sustainable soil management practices.

The following article analyses the relationship between the mental health index and the variables of the Environment, Social and Governance-ESG model in the Italian regions between 2004 and 2023. The econometric analysis is aimed at investigating in detail the relationships between the mental health index and the individual components of the ESG model. The results are critically discussed

Despite energy-related financial concerns and the growing demand for sustainability, many energy efficiency measures are not being implemented in industrial practice. There are a number of reasons for this, including a lack of knowledge about energy efficiency potentials and the assessment of energy savings as well as the high workloads of employees. This article describes the systematic development of an expert system, which offers a chance to overcome these obstacles and contribute significantly to increasing the energy efficiency of production machines. The system employs data-driven regression models to identify inefficient parameter settings, calculate achievable energy savings, and prioritize actions based on a fuzzy rule base. Proposed measures are first applied to an analytical real-time simulation model of a production machine to verify that constraints required for the specified product quality are met. This provides the machine operator with the expert means to apply proposed energy efficiency measures to the physical entity. We demonstrate the development and application of the system for a throughput parts cleaning machine in the metalworking industry.

Trade-offs in ecosystem services (ES) manifest when the enhancement of one service leads to the diminishing of another. These trade-offs pose a notable challenge, impacting the sustainability of particular socio-ecological systems peri-urban landscapes (PULs). This issue arises from the dynamic processes associated with peri-urbanization, posing threats to natural ecosystems and their services in peri-urban areas. Additionally, the escalating demand for ecosystem services in PULs contributes to these trade-offs. Policy making and planning concerning ecosystem ES trade-offs in PULs should prioritize the promotion of a balance between conflicting services and foster synergies among them. However, it is noteworthy that ES trade-offs in PULs are not given high priority on policy and planning agendas. Knowledge regarding policy development and planning for ES trade-offs in PULs often remains concealed within specific country and regional case studies. Consequently, this research seeks to characterize the ES trade-offs in the selected PUL case studies, with the objective of identifying potential commonalities among them. Furthermore, the study aims to identify: (i) the factors driving ES trade-offs, (ii) challenges pertaining to how policy-making and planning address ES trade-offs in PULs, and (iii) recommendations for enhancing governance practices to better manage peri-urban ES trade-offs. We designed a semi-quantitative survey and collected information about 23 different case studies, located across the world. Answers from this survey were analyzed with the help of Principal Component Analysis approach. The results showed that the most common trade-off occurred between “cultural and provisioning” and “regulating and provisioning” ES. It was found that urban development is the basic driver behind emerging of the examined trade-offs. To tackle this issue at the governance level, the study recommends establishing mechanisms to facilitate collaboration among stakeholders. This should be accompanied by robust dissemination efforts and the promotion of awareness among actors regarding the fundamental concepts of ES and PULs.

The objective of this research is to ascertain the elements that have an impact on and drive domestic violence in Saudi Arabia, a phenomenon that has a prevalence rate of around 35% among women globally. The researchers administered a survey to a sample of 550 individuals and using exploratory factor analysis (EFA) to analyze the collected data. The findings revealed three factors: lack of familial unity, encouragement of detrimental characteristics, and economic turmoil. The authors examined the consequences of these characteristics on preventive and intervention programs and proposed suggestions for policy makers and researchers. This research enhances the existing body of knowledge on domestic violence by doing a statistical analysis to examine the factors that lead to it and the resulting outcomes within a particular cultural setting.

Convolutional Neural Networks (CNNs) present drawbacks for modeling geometric transformations, such as scaling and rotation, caused by the convolution operation’s locality. Deformable convolution (DCON), a mechanism that substitutes standard convolution, increasing the receptive field to capture relevant features, is a promising approach to solve this drawback and improve the robustness of CNNs. However, the optimal way to replace the standard convolution with its deformable counterpart in a CNN model is unclear. In this study, we clarify this aseveration by conducting several experiments using deformable convolutions applied in the layers that conform a small four-layer CNN model. We also use deformable convolutions on the four-layers of several ResNet CNNs with depths 18, 34, 50, and 101. The models were tested in binary balanced classes with 2D data for image classification: Cats &amp; Dogs, EyesPACS, Spyders &amp; Chickens, and Shapes. After this testing, we evaluated DCON in 3D data for action recognition: UCF101 and Human2 (a dataset we compiled to control movement, clothing and background). The contribution of this research lies in a guideline to use DCON. It can be summarized as follows: if DCON is used on the first layers of the proposal of model (with simple features), the computational resources expressed as the quantity of Flops will tend to increase and produce bigger misclassification than the standard CNN. However, if the DCON is used at the end layers, the quantity of Flops used in the training and testing will decrease, and the classification accuracy will improve by up to 20% about the base model. Moreover, it gains robustness when using deformable convolutions because it can adapt to the region of interest. Also, the best kernel size of the DCON is three. It showed better results than size five. In the last case, the quantity of Flops increase quadratically, but their performance does not increase significantly. With these results, we propose a guideline to use the DCON and contribute to understanding the impact of DCON on the robustness of CNNs.

This work investigates the stability conditions for linear systems with time-varying delays via augmented Lyapunov-Krasovskii functional(LKF). Two types of augmented LKFs with cross terms in integral are suggested to improve the stability conditions for interval time-varying linear systems. Through this work, the compositions of the LKFs are considered to enhance the feasible region of stability criterion for linear systems. Mathematical tools such as Wirtinger-based integral inequality(WBII), zero equalities, reciprocally convex approach, and Finsler’s lemma are utilized to solve the problem of stability criteria. Two sufficient conditions are derived to guarantee the asymptotic stability of the systems using linear matrix inequality(LMI). First, asymptotic stability criteria are induced by constructing the new augmented LKFs in Theorem 1. And then simplified LKFs in Corollary 1 are proposed to show the effectiveness of Theorem 1. Second, asymmetric LKFs are shown to reduce the conservatism and the number of decision variables in Theorem 2. Finally, the advantages of the proposed criteria are verified by comparing maximum delay bounds in two examples. Two numerical examples show that the proposed Theorem 1 and 2 obtained less conservative results than existing outcomes. Also, Example 2 shows that the asymmetric LKF methods of Theorem 2 can provide larger delay bounds and fewer decision variables than Theorem 1’s in some specific systems.

Using a sample of 103 surviving and non-surviving global bond funds over a long sample period of 1998 to 2022, we perform a comprehensive analysis of the funds under our modified Sharpe’s (1992) style analysis model. We find that dynamic style analysis is useful in explaining time-series and cross-sectional and in predicting future returns of global bond funds. The dynamic style analysis also allows us to examine asset allocation over time. The results show that, during 2001 to 2014, funds shift their allocations from intermediate-term bonds to world corporate bonds and from world corporate bonds to emerging market bonds in search of higher returns at the price of credit quality.

Principal Component Analysis (PCA) is a method that identifies common directions within multivariate data and presents the data in as few dimensions as possible. One of the advantages of PCA is its objectivity, as the same results can be obtained regardless of who performs the analysis. However, PCA is not a robust method and is sensitive to noise. Consequently, the directions identified by PCA may deviate slightly. If we can teach PCA to account for this deviation and correct it, the results should become more comprehensible. The methods for doing this and an issue with this are presented.

The role of Physician Assistant emerged in 1960s America and their number has increased to become a significant component of the health care workforce in that country. On the eve of a new policy regulating Physician Associates in the United Kingdom, the role of key actors in shaping the development of regulatory policies in the two countries is critically compared against a backdrop of very different political contexts and health care systems, to reveal the factors that might help or hinder the deployment of Physician Associates/Assistants and ameliorate workforce shortages.

New dimers of selected dipyridothiazines with an m-xylene linker have been designed, synthesized and characterized. Their structure was identified using 1H and 13C NMR as well as 2D NMR techniques (COSY, ROESY, HSQC and HMBC experiments) and HR MS spectrometry. The compounds were subjected to preliminary in silico biological profile tests using the Way2Drug server and analysis of the probable cytotoxic effect on various cancer cell lines. Preliminary in vitro cytotoxicity tests were performed against normal cell lines (HaCaT) and five cancer cell lines (SW480, SW620, MDA-MB-231, A-549, LN-229) showing moderate activity. An analysis of the structure-activity relationship was performed.

The interest in lipid composition profiling is significantly increasing as research reveals the immense importance of lipids in medicine, plant science, food and agriculture. However, lipidomic analysis requires high-end specialty equipment. We used two-dimensional thin-layer chromatography (2D TLC) as a readily available, low-cost tool for basic lipidomic profiling of lipid classes in algal samples as model: Chlamydomonas reinhardtii, Auxenochlorella protothecoides, and Euglena gracilis. Algal lipid extracts were separated on a 2D-TLC plate, and TLC analysis was followed by scraping individual TLC spots off the plate, and a subsequent liquid chromatography separation and tandem mass spectrometry (LC-MS/MS) analysis. For comparison, crude lipid extracts were also injected directly to the LC-MS/MS system. Lipid class annotation was achieved by a combination of accurate mass, retention time information, neutral loss and fragment ion analysis by MS2Analyzer, and by matching spectra to LipidBlast MS/MS library. Overall, we were able to identify 15 lipid classes, and to adequately profile the lipid classes in all three organisms. This TLC method is thus suggested as an accessible tool for lipid class profiling of algal, plant, and food lipids, alike, when a rapid and simple analysis is required, e.g., for screening purposes.

A methodology has been developed to assess the presence and dissipation of herbicides of wide range of polarities in soil using in-tube solid phase microextraction (IT-SPME) coupled on-line to capillary liquid chromatography (capLC). The compounds investigated were tritosulfuron (TRT), triflusulfuron-methyl (TRF), aclonifen (ACL) and bifenox (BF), with log octanol-water partition coefficients (log Kow) ranging from 0.62 to 4.48. The linearity, recoveries, intra and interday precision, limits of detection (LODs) and quantification (LOQs), and accuracy of the method were established. The proposed approach was successfully applied assess the degradation of the tested herbicides in different types of soil (agricultural, urban and forest) after being exposed to different laboratory and outdoor conditions. The results obtained showed a greater persistence of the most apolar compounds ACL and BF, regardless of the soil characteristics, whereas a significant degradation of highly polar herbicides TRT and TRF was observed in soils with lowest organic matter even after a few days of exposure. Those results indicate that the proposed approach can be considered an effective tool for a better understanding of soil pollution.

Originating in the Mediterranean, B. oleracea is grown all over the world and is highly valued for its nutritional value and variety in cooking. Its susceptibility to pests, diseases, and environmental stressors, however, poses significant challenges to sustaining consistent crop quality and productivity. The BASIC PENTACYSTEINE gene significantly impacts plant growth and responses to abiotic stresses. Nevertheless, little is known about its precise role and the underlying biological processes in B. oleracea. Eleven BPC genes in B. oleracea were identified by a thorough genome-wide investigation and categorized into three categories according to their structural traits and gene motifs. There were eight different chromosomes on which these BoBPCs were located. Comparative synteny analysis was used to reveal the evolutionary traits of potatoes, Arabidopsis, and B. oleracea. The BoBPC family genes in B. oleracea were found to have specific cis-elements linked to growth, stress responses, and hormone regulation when their promoter regions were studied. Cuticular wax synthesis expression revealed that all BoBZR1 genes, from wild to mutant, expressed at varied amounts in distinct types. Moreover, distinctive BoBPC gene expression patterns were found in the seven distinct tissues that were studied. Our thorough analysis shows that the BoBPC family is widely involved in B. oleracea developmental processes. The present findings provide an established theoretical basis for further exploration into the composition and roles of BoBPC in B. oleracea.

The rapid expansion of China's A-share market, coupled with the burgeoning volume of financial research reports, presents a unique challenge to traditional stock price prediction methodologies. This study introduces FAST-SCAN, a novel framework designed to leverage natural language processing (NLP) and time series analysis for the efficient and accurate prediction of stock market trends. By integrating advanced sentiment analysis, primarily through the utilization of the RoBERTa model, with dynamic time series forecasting, FAST-SCAN aims to distill actionable insights from the vast corpus of financial research reports. This approach not only enhances the speed and efficiency of financial analysis but also addresses the time-sensitive nature of market-influencing factors, thereby providing a competitive edge in stock price prediction. The framework demonstrates a notable improvement in predictive performance, achieving a 20% annualized return and a 7.19% RankIC, marking a significant advancement over conventional statistical analysis methods. Through its innovative combination of NLP and predictive analytics, FAST-SCAN paves the way for a more informed and strategic approach to investment in the A-share market, emphasizing the importance of timely, data-driven decision-making in the financial industry.

The Ebola virus disease (EVD) is an extremely contagious and fatal illness caused by the Ebola virus. Recently, Uganda witnessed an outbreak of EVD, which generated a lot of attention on various social media platforms. To ensure effective communication and implementation of targeted health interventions, it is crucial for stakeholders to comprehend the sentiments expressed in the posts and discussions on these online platforms. In this study, we used deep learning techniques to analyze the sentiments expressed in Ebola-related tweets during the outbreak. We explored the application of three deep learning techniques to classify the sentiments in 8,395 tweets as positive, neutral, or negative. The techniques examined included a 6-layer convolutional neural network (CNN), a 6-layer long-short-term memory model (LSTM), and an 8-layer Bidirectional Encoder Representations from Transformers (BERT) model. The study found that the BERT model outperformed both the CNN and LSTM-based models across all the evaluation metrics, achieving a remarkable classification accuracy of 95\%. These findings confirm the reported effectiveness of transformer-based architectures in tasks related to natural language processing, such as sentiment analysis.

The digital era has significantly amplified the volume of online reviews, presenting both opportunities and challenges in harnessing insights from consumer feedback. Aspect-Based Sentiment Analysis (ABSA) has emerged as a crucial tool for distilling sentiments from these reviews, providing valuable data for enhancing product and service quality. This study introduces an advanced hybrid model, AdvSentiNet, which integrates adversarial training into the state-of-the-art framework to elevate the precision of sentiment detection at the aspect level. By employing an adversarial network, where a generative model competes against a classifier by crafting highly realistic synthetic samples, we aim to bolster the model's resilience against varied data samples. This innovative approach, unexplored in its entirety within the realm of ABSA, demonstrated remarkable performance improvements on benchmark datasets. For instance, accuracy on the SemEval 2015 dataset escalated from 81.7% to 82.5%, and for the SemEval 2016 dataset, it surged from 84.4% to 87.3%.

The incessant evolution of online platforms has ushered in a multitude of shopping modalities. Within the food industry, however, assessing the delectability of meals can only be tentatively determined based on consumer feedback encompassing aspects such as taste, pricing, packaging, service quality, delivery timeliness, hygiene standards, and environmental considerations. Traditional text data mining techniques primarily focus on consumers' emotional traits, disregarding pertinent information pertaining to the online products themselves. In light of these aforementioned issues in current research methodologies, this paper introduces the Bert BiGRU Softmax model combined with multimodal features to enhance the efficacy of sentiment classification in data analysis. Comparative experiments conducted using existing data demonstrate that the accuracy rate of the model employed in this study reaches 90.9%. In comparison to single models or combinations of three models with the highest accuracy rate of 7.7%, the proposed model exhibits superior accuracy and proves highly applicable to online reviews.

The Drinking Water Directive (EU) 2020/2184 includes the parameter microcystin-LR, a cyanotoxin, which drinking water producers need to analyze if the water source has potential for cyanobacterial blooms. In light of the increasing occurrences of cyanobacterial blooms worldwide and given that more than 50 percent of the drinking water in Sweden is produced from surface water, both fresh and brackish, the need for improved knowledge about cyanotoxin occurrence and cyanobacterial diversity has increased. In this study a total of 98 cyanobacterial blooms were sampled in 2016-2017 and identified regarding their toxin production and taxonomical compositions. The surface water samples from freshwater lakes throughout Sweden including brackish water from 8 east coast locations along the Baltic Sea were analyzed for their toxin content with LC-MS/MS and taxonomic composition with 16S rRNA amplicon sequencing. Both the extracellular and the total toxin content was analyzed. Microcystin´s prevalence was highest with presence in 82% of blooms, of which as free toxins in 39% of blooms. Saxitoxins were found in 36% of blooms in which the congener decarbamoylsaxitoxin (dcSTX) was detected for the first time in Swedish surface waters at four sampling sites. Anatoxins were most rarely detected, followed by cylindrospermopsin, which were found in 6% and 10% of samples, respectively. As expected, nodularin was detected in samples collected from the Baltic Sea only. The cyanobacterial operational taxonomic unit (OTU) with the highest abundance and prevalence could be annotated to Aphanizomenon NIES-81 and the second most profuse cyanobacterial taxon to Microcystis PCC-7914. In addition, two correlations were found, one between Aphanizomenon NIES-81 and saxitoxins and another between Microcystis PCC-7914 and microcystins. This study is of value to drinking water management and scientists involved in recognizing and controlling toxic cyanobacteria blooms.

A mathematical model that incorporates human attacks on dogs is presented to explore the dynamics of rabies transmission. The model divided the infection rate into two categories: dog-to-dog transmission rates during the prodromal phase (βdP) and the furious phase (βdF). It has been determined that the model is well-posed and that all of the solutions are positive, as well as the feasibility and positivity of the model's solutions. Both the basic reproduction number (R0) and the effective reproductive number (Re) are computed, and it is demonstrated that the model has a unique disease-free equilibrium that is globally asymptotically stable whenever Re < 1. To identify the model's most sensitive parameters and the ones that intervention efforts should focus on, sensitivity analysis of the effective reproduction number is carried out. We can construct an optimal control system using the state equations, the adjoint equations, and the optimality condition that determines the controls by applying Pontryagin's Maximum/Minimum principle. To help decision-makers in allocating funds for rabies interventions, cost-effectiveness analyses are conducted. To determine the degree to which the intervention strategies are advantageous and cost-effective, this study performs a cost-effective analysis of one or all possible combinations of the optimal rabies control strategies (pre-exposed and post-exposed vaccination for both dog and human populations) for the four different transmission settings. After arranging the techniques to increase effectiveness (total infections prevented), a cost-effective analysis using the Incremental Cost Effectiveness Ratio (ICER) was conducted. The result supports the function that the four intervention options are performing to either completely eradicate or significantly reduce rabies disease among the populations. The dynamical behavior of the system is studied through simulations using the ode45 numerical technique from MATLAB.

This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery.

Lower extremity joint kinetic factors are thought to modulate walk–to–run transition (WRT) and run–to–walk transition (RWT). This study aimed to investigate joint stiffness and energetic patterns during the WRT and RWT processes and identify whether gait transitions occurred within a single step or not. Ten healthy subjects participated in treadmill WRT (1.8 m/s – 2.4 m/s) and RWT (2.4 m/s – 1.8 m/s) tests. We investigated two steps before transition (S–2, S–1), two steps after transition (S1, S2) and the transition step (S0). We found significant differences between S2 and S–2 for ankle joint stiffness during WRT and RWT (p &lt; 0.001); and for hip joint stiffness, we found significant differences between S1 and S–1 during WRT and RWT (p ≤ 0.001). Additionally, stance phase mechanical energy generation was observed to transfer from proximal to distal joints at S0 during WRT, and from distal to proximal at S0 during RWT. Transition step ankle kinematic and kinetic patterns were similar to the target locomotion task gait format in both WRT and RWT. Moreover, RWT required longer adaptation time compared with WRT. These findings indicate that WRT and RWT were modulated before and after the actual transitions, not within a single step. Redistribution of joint mechanical work generation was related to gait transition triggers, which modulate the WRT and RWT process.

This study outlines the development of an innovative method utilizing UHPLC-PDA for the simultaneous identification of polyphenols and bitter acids (alpha-acids, beta-acids, and iso-alpha-acids) in beer samples. The resulting chemical profiles were leveraged to distinguish the characteristics of four bergamot-flavored beers, each representing distinct styles (IPA, Lager, Blanche, and ALE), produced on a pilot-scale plant. In a streamlined 29-minute analysis, twenty-nine polyphenols and fourteen bitter acids were successfully identified under optimized separation conditions. Rigorous validation, encompassing parameters such as Limit of Detection (LOD), Limit of Quantification (LOQ), R-squared (R2), repeatability, and reproducibility, was conducted for polyphenol quantification using constructed calibration curves with seven levels. Exploring polyphenolic components as potential discriminators among different beer styles, a total of thirty-two polyphenolic compounds were identified and quantified, including characteristic bergamot peel polyphenols like neoeriocitrin, naringin, and neohesperidin. The determination of bitter units and total phenols further aided in style differentiation. Multivariate analysis provided additional insights into variations among specific beer styles, revealing discrepancies in the presence or relative concentrations of specific compounds linked to brewing ingredients and processes. This research enhances to trace the fingerprinting of the chemistry governing beer quality through a straightforward and cost-effective analytical approach, readily applicable in common laboratories.

This paper draws upon the theory of turbulence in physics to explore the similarities and differences between volatility in financial markets and turbulent phenomena at a statistical physics level. By analogizing the dynamics of financial markets with fluid turbulence, we have developed an innovative analytical framework aimed at deepening the understanding of the complexity inherent in financial markets. Our research methodology includes a comparative analysis of simulated time series data for the Standard & Poor's 500 Index and fully developed turbulent velocity time series. We focus on the similarities in key statistical physical properties, including probability distributions, correlation structures, and power spectral densities. Furthermore, we have established a model of capital flow in financial markets and proposed corresponding solutions. Through computational simulations and data analysis, we find that while financial market volatility shares some statistical characteristics with turbulent phenomena, significant differences exist in the shapes of probability distributions and the temporal scales of correlations. This suggests that although financial markets exhibit patterns akin to turbulence, the behavior of this complex, multi-variable driven system does not completely correspond to natural turbulence, highlighting the limitations of directly applying turbulence theory to financial market analysis. Our study offers a new perspective for the theories of financial markets and the field of econophysics, providing fresh insights into the complexity of financial markets and contributing to the prevention and management of financial risk.

The manuscript presents the applicability of the Gumbel distribution in the frequency analysis of extreme events in hydrology. The advantages and disadvantages of using the distribution are highlighted, as well as recommendations regarding its proper use. A literature review was also carried out regarding the methods of estimating the parameters of the Gumbel distribution. Thus, for the verification of the methods, case studies are presented regarding the determination of the maximum annual flows and precipitations using nine methods for parameter estimation. The influence of the observed data lengths on the estimation of the statistical indicators, the parameters and the quantiles corresponding to the field of low annual exceedance probabilities (p <1%) is also highlighted. The results are compared to those obtained with the Generalized Extreme Value distribution, respectively the Burr and the Wakeby distributions with parameters estimated using the L-moments. The results highlight and reaffirm the statistical, mathematical and hydrological recommendation regarding the avoidance of using the Gumbel distribution in Flood Frequency Analysis and its use with reservations in the case of maximum precipitation, especially when the statistical indicators of the analyzed data are not close to the characteristic ones and unique to the distribution.

Nowadays cities, due to the increasing urbanization, are facing challenges spanning on multiple domains as mobility, energy, environment, etc. Big data gathered from legacy systems, from geographic information systems, operators, and Internet of Things technologies can be exploited to provide insights to assess the current status of a city, the possibility to perform what-if analysis to observe the impact of possible changes. The few available solutions for formal scenario definition and analysis are limited to single domain analysis and provide proprietary formats and tools. Therefore, in this paper, we present a novel scenario model and editor integrated into the open-source Snap4City.org platform to define general processing and what-if scenarios in order to carry out analysis on different domains. The proposed solution responds to a series of identified requirements and implements NGSIv2 compliant data models and formal description of the urban context. As a case of study, a traffic congestion analysis is provided, confirming the validity and usefulness of the proposed solution.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the corporate distribution image, and further reduce corporate operating costs and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identified the locations of primary and secondary logistics nodes but also provided a new perspective for constructing the urban last-mile logistics system, enriching the academic research related to logistics node construction. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

The underperforming state of agricultural sector especially in Sub Saharan Africa, Nigeria inclusive is largely due to gender inequities in opportunities which undermine the sustainable and inclusive agricultural development. Agricultural value chain development requires equity in opportunities among men and women along the value chain, and this can only be done through interventions recommended by gender equity studies. The study analyzed gender equity opportunities, and youth engagement in rice value chains in southeast Nigeria. The multistage sampling technique was used in selecting the sample. The sample size consists of 476 youth rice value chain actors randomly selected from 48 villages across three states in South-East Nigeria. The data were analyzed using Modified Gender Equity Index (MGEI), and value addition models. The results from MGEI show that the level of gender equity opportunities for youth rice producers, processors and marketers were -0.21, -0.21 and -0.04 respectively, indicating inequity against the female youth. The overall value-added per ton by the male youth actors was ₦52,281 and ₦52,229 for female youth actors. The share of the value-added by male youth producers, processors and marketers were 51.65%, 49.41% and 47.78% while the share for female youth were 48.38%, 50.59% and 52.22%, respectively. The major constraints to youth engagement in rice value chain include lack of capital to start up, lack of access to credit, drought, lack of access to better technology, high cost of hiring machines, lack of technical skills, and stressful nature of rice value chain. It was recommended that Policies and interventions that ensure equal access to resources, opportunities, and decision-making power for both male and female youth actors should be implemented through targeted training programmes, awareness campaigns and capacity-building initiatives that promote gender equality.

: F-box proteins are a large gene family in plants, and play crucial roles in plant growth, development, and stress response. However, a comprehensive investigation of F-box family genes in cultivated peanut (Arachis hypogaea L.), has not been undertaken. In this study, we used a bioinformatics approach to identify and analyze F-box protein family genes in the cultivated peanut genome, and examined their expression profiles. Herein, a total of 95 F-box protein family members on 18 chromosomes were identified in cultivated peanut, which were sequentially designed as AhFBX1-AhFBX95. Phylogenetic analysis classified these AhFBXs into four different groups (Group I - IV), and eight subfamilies based on the structural domains of the C-terminal. Structural analysis revealed the presence of ten unique motifs within these genes. Additionally, the investigation of non-synonymous and synonymous substitution rates (Ka/Ks), and synteny in the F-box proteins, provided valuable insights into the evolutionary and domestication processes of AhFBX genes in peanut. Furthermore, in silico expression profiling of 22 different tissues demonstrated that most genes were predominantly expressed in leaves, pistils, and the aerial gynophore tip, while some members showed tissue-specific expression at the apex of both vegetative and reproductive shoots. Subsequently, we validated the expression of 9 differentially expressed genes (DEGs) using transcriptomeic data of lateral branch development and qRT-PCR analysis. These DEGs may be involved in regulating the development of lateral branches and subsequently affect the architecture of peanut plants.

In this work, we report a series of undoped and 1% Ga or Y doped lanthanum manganite compounds which have been synthesized via sol-gel technique. The obtained materials were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and scanning electron microscopy (SEM/EDX). The XRD results confirmed a good crystallinity for all the studied samples and a change in the crystal structure of Ga or Y doped lanthanum manganite (Pm-3m space group) is observed compared to pristine sample (R-3c space group). Thermal analysis highlighted a different behavior of the doped samples compared to the undoped sample observed by the different mass loss in the analyzed temperature range. For these materials, it is shown for the first time that the static electrical conductivity, σDC of Ga or Y doped LaMnO3 compounds increases compared to the σDC pristine sample, and the thermal activation energy of the conduction EA,cond increases linearly with the temperature, for all three studied sample.

We are currently witnessing a strong global transition towards new, more sustainable models of development and consumption. This transition is both activating and highlighting a series of discrepancies between the various actors in agri-food marketing systems, including the institutions that govern regulatory and trade aspects. These discrepancies highlight how, on a global level, the agri-marketing system is unable to provide adequate responses to the new principles of sustainability that are increasingly expected by civil society. This inability is often linked to the phenomena of opportunism, information asymmetries and lock-in effects (resulting from the slowness or inability of some sectors or actors to evolve technologically and culturally). The control of all these factors is often ineffective and often involves high transaction costs . These conditions lead the global market into an ongoing state of failure, creating structural holes, in which new forms of exchange are born, in response to the principles mentioned above. We identify these as nested markets: hybrid market forms that, thanks to new information technologies, create a new form of proximity that transcends the classical concept of physical proximity. This new proximity directly links producers and consumers in relationships based mainly on two substantial values: reciprocity and reputation. Both of these values significantly reduce transaction costs which today play an increasing role in determining the form and repetition of exchanges. This condition is difficult to interpret from classical and neo-classical economic theories. To do so it is necessary to use a multidisciplinary approach. In this article we combine neo-institutional theory and network analysis to interpret phenomena that are increasingly emerging in all areas of the world. Using these two theories, and case studies from Brasil, North America, China and Europe, we try to highlight how the market is not only the place where the price of products and of their quantities are formed. Market is a complex social spaces, where more-or-less stable relationships are formed, based on the values of reciprocity and reputation that determine behaviour and codes of conduct aimed at containing opportunism and encouraging sustainable, autonomous, forms of self-regulation. These relationships are also the driving forces that ensure the ongoing continuation of the exchanges and the diffusion and preservation of knowledge over time and space. These relationships represent the beating heart of nested markets and their dynamic ability to adapt to the continuous changes that occur in the socio-economic and environmental context in which they are rooted. In this way sustainability is built as an expression of the co-production between different actors (producers, consumers, institutions) and natural resources.

In the moist environment of soil-water-air, there is a problem of low accuracy in monitoring Volatile Organic Compounds (VOCs) using a Photoionization Detector (PID). This paper analyzes the reasons for the low accuracy of the traditional Support Vector Machine (SVM) regression method. To address the issue, the PID signal is subjected to feature extraction and Principal Component Analysis (PCA) to reduce the data dimensionality. Moreover, the optimal SVM parameters are selected using a Genetic Algorithm (GA), and a combined approach of SVM regression with PCA and GA is utilized for PID signal regression analysis. And the effectiveness of the method is validated through extensive experiments and simulations. Furthermore, the influence of the sample quantity on the regression accuracy is analyzed, enabling accurate monitoring of VOCs concentration in a moist environment.

The detrimental consequences of soil pollution caused by crude oil or petroleum products are immense, leading to land degradation, property damage, and rendering agricultural practices ineffective. Extensive research has been conducted in the field of soil remediation, but further studies are still required to explore additional details of the remedial process. As a result, this study focuses on evaluating the effectiveness of Vernonia Galamensis and Vernonia Amygdalina, commonly known as bitter leaf, in remediating hydrocarbon-contaminated soil. In the analysis of micro-organisms, it was found that the bitter leaf extracts contained three types of bacteria: Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli. The leaf extracts were prepared using different methods, including sun drying, room drying, and using them in their wet form, which were then blended into the contaminated soil. The study also took into consideration three different types of soil: sandy-loamy soil, clay soil, and swamp soil. These advanced techniques and considerations are relevant to the topic of revolutionizing soil remediation, as they explore the potential of bitter leaf extracts and different soil types in effectively mitigating the effects of hydrocarbon contamination.The findings revealed that the wet blended extracts of Vernonia performed exceptionally well in the remediation process, surpassing a 50% reduction in the initial contamination levels. The study involved utilizing a quantity of bitter leaf ranging from 10g to 40g, which was added to the contaminated soils and monitored for a duration of 40 days. Remarkably, this approach led to a significant decrease in the concentration of contaminants within the soil, indicating the effectiveness of the bitter leaf extracts in the remediation process. Towards the conclusion of the study, predictive models were constructed to forecast the impact of hydrocarbon content, as well as the levels of lead, zinc, and chromium in the soil. These variables served as the dependent variables in the models, while the mass of bitter leaf, the duration of treatment, and the pH of the soil were considered as independent variables. Significantly, the models achieved a level of significance of less than 0.05, indicating their statistical validity. Furthermore, the r2 value, which represents the goodness of fit, demonstrated an appreciable level of accuracy in predicting the remediation effects. These results highlight the potential of the developed models in assessing and predicting the remedial outcomes of hydrocarbon contamination using bitter leaf extract.

Climate research shows that extreme weather events and their effects are increasing in frequency, severity, and impact. Extreme wind events can cause massive societal and economic damage. High wind occurrences and their causes must be identified because they are hard to define and understand. Extreme wind modeling affects wind power plant planning, installation, and building modeling. This applies to city, vegetation, and vegetational area modeling. The scientific literature utilizes statistical, dynamic (Weather Research and Forecasting-WRF), and machine learning methods to identify extreme wind speeds. This study explores the relationship between lower-level extreme wind speeds and upper-level parameters like Z500, and T850 based on cluster analysis. The aim is to explore the correlation between upper-level atmospheric dynamics and lower-level wind behavior. K-Means cluster analysis method was employed to classify Turkish regions with similar severe wind episodes. WRF model simulation findings from NEWA (New European Wind Atlas) project (2019) were used for that purpose. The NEWA project's wind speed data at 100m a.g.l. was used for cluster analysis. We simplify the process by grid point reduction and applying the top 2% percentile as threshold value. Self-Organized Maps (SOM) clustering was also applied using the upper-level variables.

The increased number of buildings in urban areas limits the creation of vegetated areas, leading to the search for alternatives to create spaces to promote contact with nature. In this context, green roofs have been increasingly studied. These structures have specific microclimatic condi-tions requiring an accurate study of the most appropriate vegetation to use. This study aims to analyze the long-term viability of vegetation installed on an experimental green roof open-air lab. This analysis was done through images obtained from photographic records and later in-serted into the ImageJ program, to identify species and evaluate the area covered by vegetation. Only a few of the species that were planted in the test beds over the years have persisted to the present, while other species have spontaneously appeared. Also, surveys were used to learn about people's preferences for the vegetation on these test beds. These showed that people favor recognizable plants with plenty of vibrantly colored blossoms. It was feasible to choose the best plants for green roofs in the studied conditions as a result of the analysis, taking into account the ground cover percentage by vegetation, its persistence, and the preferences of the respondents.

Prosthetic infections are associated with high morbidity, mortality, and relapse rates making them still a serious problem for implantology. Staphylococcus aureus is one of the most common bacterial pathogens causing prosthetic infections. In response to the increasing rate of bacterial resistance to commonly used antibiotics, the work proposed a method of combating pathogenic microorganisms by modifying the surface of synthetic polymeric biomaterials using proteolytic enzyme inhibitors (serine protease inhibitors - 4-(2-aminoethyl)benzenesulfonyl fluoride hydrochloride and puromycin). While using techniques based on immobilization of biologically active molecules, it is important to monitor changes occurring on the surface of the modified biomaterial, where spectroscopic techniques (e.g. FTIR) are ideal. The ATR-FTIR measurements demonstrated that the immobilization of both inhibitors caused large structural changes on the surface of the tested vascular prostheses (polyester or polytetrafluoroethylene) and showed that they were covalently bonded to the surface of the biomaterials. Next, the bactericidal and antibiofilm activities of the tested serine protease inhibitors were determined using the CLSM microscopic technique with fluorescent staining. During LIVE/DEAD analyses, a significant decrease in the formation of Staphylococcus aureus biofilm after exposure to selected concentrations of native inhibitors (0.02-0.06 mg/mL for puromycin and 0.2-1 mg/mL for 4-(2-aminoethyl)benzenesulfonyl fluoride hydrochloride was demonstrated.

Several specialized terpenoids that are used as phytoalexins are produced in the plant of tobacco. They play a key role in plant defense against pests and pathogens. Here, we performed genome-wide identification, classification, and expression profiling of the terpene synthase (TPS) genes in the tobacco genome. A total of 186 TPS genes were identified, which is significantly higher than that in wild Nicotiana, Capsicum and Solanum species. Phylogenetic analysis of these TPS genes in tobacco and Solanaceous relatives as well as Arabidopsis showed that a few tobacco-exclusive TPS clades occurred recently through gene duplication events. These clades are likely to be responsible for a range of compounds that separate N. tabacum cultivars from its wild relatives. Transcriptome sequencing of two cultivars of N. tabacum at several developmental stages revealed that some TPS genes from the TPS-a and TPS-b lineages are constitutively expressed and a few duplicated TPS genes are only expressed post anthesis at the time of harvest. Additionally, some TPS genes underwent alternative splicing, further diversifying the gene products of the TPS genes. Our findings provide an important candidate genes for further understanding of the diversity and evolution as well as functionality of TPS genes in the plant of tobacco.

The previous studies on the diversion pipe of pumped storage power stations (PSPS) primarily focused on numerical simulations of pulsating pressure propagation within the pipe and its inducing factors. However, there was a lack of field monitoring and analysis of measured data to support surface ambient vibration, resulting in insufficient credibility and practicality of the numerical results. Based on ambient monitoring and data analysis, this paper examines the factors contributing to the ambient vibration of the water diversion pipeline in Changlongshan (CLS) PSPS. It is concluded that: (1) The primary source of vibration originates from units operation-induced ambient vibrations; (2) Vibration during pumping conditions exceeds that during power generation conditions; (3) The ground measuring point's vibration acceleration primarily depends on the distance from the vibration source; (4) Different types of units have varying effects on water diversion pipeline vibrations.

Hollow and concrete-filled steel tubes (CFSTs) are widely used as columns in many structural systems and local buckling can occur when they are subjected to axial compression loading. The critical regions for the local buckling of the columns are near the ends of the column where the moments are the highest. To strengthen the critical regions of the CFSTs the CFRP provides a simple and effective method which used previously as an enhancement for both of strength and ductility. So, this study suggests investigating the axial load behaviour of CFST strengthened with CFRP using FE method after a verification study and then a various parametric studies were carried out to cover the effect of the CFRP layers, number, confinement length and position. The FE results proved that, one CFRP sheet with thickness equal to 1.2 mm was strengthened the composite column by 8.5%, two CFRP sheets with total thickness equal to 2.4 mm was strengthened the composite column by 23.5%, three CFRP sheets with total thickness equal to 3.6 mm was strengthened the composite column by 35.1% and four CFRP sheets with total thickness equal to 4.8 mm was strengthened the composite column by 44.5%. Moreover, this study proved that by strengthened the composite columns with CFRP sheets with different lengths, the axial load resistance was improved by 8.5%, 4.6%, 0.1% and 0.0% for length percentages 100%, 75%, 50% and 25% respectively.

There has been a rapid increase in the use of Marine Spatial Planning (MSP) throughout the world recently, partly due to the continued loss of marine biodiversity and habitat. The sustainability of marine resources is threatened in all regions of the world by major events such as climate change, marine pollution, overfishing, illegal, unreported and unregulated fishing both on the high seas and in country waters. Here, we present a comprehensive review and analysis of how economic information has been applied and used to inform decisions about MSP in British Columbia (BC), Canada and other similar jurisdictions. This focus for the paper was selected because important gaps remain in the literature in terms of incorporating economic questions into MSP. We first present different definitions of MSP, and then we extract useful lessons from MSP regimes with well–tested decision support tools, and use this to guide MSP implementation in BC. We finally present and discuss case studies from Australia, South Africa and Belgium. Our review suggests that applying economic information to support the design and implementation of MSPs would lead to better decisions. This in turn would foster livelihoods, attract finance, increase buy–in and advance United Nations Sustainable Development Goal 14: Life Below Water, thereby achieving Infinity Fish, i.e., ensuring that ocean benefits flow to humanity forever.

Climate change is affecting the planet, compromising survival and altering forest ecosystems. Investigating climatic variations in all regions is important. The aim of this research was to assess such variations in the W and Pendjari Biosphere Reserves in Benin from 1985 to 2015 and predict climate up to 2045. Drought frequency was assessed by using the Standardized Precipitation and Evapotranspiration Index (SPEI) and correlation was performed between climatic variables. Then a predictive analysis was computed. From 1985 to 2015, there was change in the climate with few severe dry and wet years while no extreme climatic condition was noticed. Most of the years showed nearly normal climatic conditions. The predicted climate indicates an absence of extreme conditions up to 2045 but an increase in the annual humidity is expected. A low decrease in annual mean temperature with high confidence is expected. Decision-makers can better organize adaptation programs and sensitize people in the reserves on their role in environmental conservation and mitigation of climate change using these outputs.