Non-ulcerated cutaneous leishmaniasis (NUCL) is an atypical and peculiar clinical form of the disease that was first described by Ponce et al. (1988) in Honduras (Central America), where iso-lated or disseminated popular and/or nodular skin lesions are commonly seen in adolescents and young adults. Leishmania (L.) chagasi was then incriminated as the causal agent of the disease, as well as of American visceral leishmaniasis (AVL), although NUCL has been shown to be more prevalent than AVL in the region. Considering, however, the dubious taxonomic character of the causal agent of NUCL, as there is no evidence of this form of disease associated with L. (L.) cha-gasi in South America, this study aimed to carry out a retrospective taxonomic analysis on a se-ries of studies on the diagnostic approach to the Honduran parasite in loco (El Tigre island, Amapala municipality, west coast of Honduras), where taxonomic characters of a biological (in vitro and in vivo), epidemiological (vector and canine reservoir), clinical-immunological (im-munopathological features of in situ skin lesion and systemic infection) and molecular (ge-nomic/phylogenetic analyses) nature were considered. The findings, particularly those of mo-lecular nature, such as: the Honduran parasite showed to be more ancestral (molecular clock analysis of the DNA polymerase alpha subunit gene) than all viscerotropic ones [L. (L.) chagasi, L. (L.) donovani and L. (L.) infantum], as well as presenting an absolutely unprecedented structural variation on chromosome 17 and the highest frequency of genomic SNPs (more than twice the number seen in the Brazilian parasite), in addition to representing a different phylogenetic lin-eage (mainly in the NJ - RNA Pol II tree) from those viscerotropic ones, have pointed to the characterization of a new leishmanine parasite, Leishmania (Leishmania) poncei n. sp. (Kinetoplas-tea: Trypanosomatidae), in honor of Professor Ponce, who was the first researcher to describe NUCL in Honduras.

Since its emergence, COVID-19 has caused a great impact in health and social terms. Governments and health authorities have attempted to minimize this impact by enforcing different mandates. Recent studies have addressed the relationship between various socioeconomic variables and compliance level to these interventions. However, little attention has been paid to what constitutes people's response and whether people behave differently when faced with different interventions. Data collected from different sources show very significant regional differences across the United States. In this paper, we attempted to shed light on the fact that a response may be different depending on the health system capacity and each individuals’ social status. For that, we analyzed the correlation between different societal variables (i.e. education, income levels, population density, etc.) along with healthcare capacity related variables (i.e. hospital occupancy rates, percentage of essential workers, etc.) with regards to people's level of compliance with three main governmental mandates in the United States: mobility restrictions, mask adoption, and vaccine participation. Our aim was to isolate the most influential variables impacting behavior in response to these policies. We found that there was a strong relationship between individuals' educational levels and political preferences with respect to compliance with each of these mandates.

Background (1) In the time of the 4th Industrial Revolution or Industry 4.0, a conglomerate of technical and social inventions, political contexts, socio-cultural circumstances, environmental policies, business models, and economic policies has emerged. Sustainability policy in theory and practice aims to deal with the effects of all these factors and to try to make decisions that ensure both social and economic development sustainably. The question is how to familiarize oneself with the current knowledge about the relationship between Industry 4.0 and sustainability?; (2) Methods: This research utilizes an automated content analysis method to analyses scientific journals, newspapers and magazines. The comparison of results of both research group shows that the scientific literature focuses more on changes in business models, production processes and technologies that enable sustainable development; (3) We found that the scientific literature focuses more on changes in business models, production processes and technologies that enable sustainable development. Newspapers and magazines articles write more about sustainable or green investment, sustainable standards and sustainable reporting. Newspapers and magazines articles write more about sustainable or green investment, sustainable standards and sustainable reporting. Newspapers, as well as some latest research journals, include articles of the COVID-19 outbreak and its effect on the economy and the environment. Indeed, the outbreak of the virus brings a new thought to the reorganization of the complex relationships between consumers, businesses and the state; (4) Conclusions: According to the comparison of the analyses of the results, it can is that the analyses of both types of literature, both scientific and professional, shows that there are common topics they write about, which are related to the field of clean production, emissions, renewable energy, climate change, sustainable investments and corporate sustainability. An urgent global issue that extends all over the world is the promotion of energy-saving technologies and reduction of carbon dioxide emissions.

The salt marshes, lying at the land-sea temperate interface, furnish a plethora of ecosystems services such as biodiversity niche support, ocean-climate change regulation, ornithology recreo-tourism or plant gathering by hand. They undergo significant worldwide losses due to their conversion into crop fields and to their spatial compression between the rising sea-level and the armoring shoreline. Their monitoring however requires to use a suite of remote sensing sensors to embrace the regional scale while capturing the plant details. This research innovatively adopts a multiscale approach using a cascading spaceborne and airborne process, from the 10-m Sentinel-2, through the 3-m Dove, to the 0.03-m unmanned airborne vehicle (UAV) imageries. The high to very high temporal resolution of the Sentinel-2 and Dove enabled to cover twenties and tens of km2 over five and four years, respectively, in the form of normalized difference vegetation index (NDVI) classes, associated with microphytobenthos, low, medium and high salt marsh vegetation, including the opportunistic Elyma genus. The NDVI was then modelled at the UAV scale (a few km2) using a three-layered NN prediction, providing the final near-infrared (NIR), and the intermediate red, green and blue reflectance imageries, calibrated/validated/tested with the Dove reflectance imageries (R2NIR=0.98, R2red=0.88, R2green=0.84, and R2blue=0.90). The 100fold increase in pixel size allowed to detect the decimeter-scale objects of the tidal flats and salt marshes, to enlarge the NDVI class ranges, and hold great promise to model other spectral bands at the UAV scale for further deeply enhancing the salt marsh mapping.

Auricular keloids pose significant aesthetic and functional challenges, and traditional treatments often fall short in ad-dressing these. Our study presents an innovative combined approach of ablative CO2 and dye laser therapy for improved keloid management. This treatment protocol was applied to 15 patients with auricular keloids after an initial multispec-tral analysis to assess keloid composition. The laser sequence was tailored per patient based on this analysis. Evalua-tions using the Vancouver Scar Scale and Patient and Observer Scar Assessment Scale were carried out at baseline and at 3-week intervals post-treatment. The results showed a significant reduction in these scores at the final follow-up (p<0.05), suggesting improvements in keloid color, texture, and pliability, with minimal adverse events. Additionally, no recur-rence of keloids was observed. Our findings indicate that this novel methodology of multispectral analysis followed by tailored laser therapy may offer a safe and effective solution for auricular keloids, promising enhanced keloid treatment and prevention of recurrence. However, further investigations, including randomized controlled trials, are needed to confirm and optimize this treatment protocol.

Despite significant advances in metallurgy and the potential to create high and very high-strength steel, all international specifications for steel design provided little information about the limits of slenderness for high-strength steel sections (HSSs) and didn’t give anything about the design of very high-strength steel sections (VHSSs). The American structural steel design specification covers circular hollow sections (CHS) of steel grades up to S690, while it isn't in ANSI/AISC 360-16 and ASTM A514/A514M. Euro code EN 1993-1-1 can be applicable only to steel grades up to S460. EN 1993-1-3 and EN 1993-1-12 contain supplementary rules for cold-formed members and HSS (S460- S700), respectively. Though ductility requirements for (HSS) are specifically established in EN 1993-1-12, it is largely a continuation of the normal-strength-steel design procedures standardized in EN 1993-1-1, where (HSSs) are largely treated as normal-strength steels in these rules, and the same design approaches are used. The behavior of high and very high-strength steel circular hollow sections under axial compression load is studied in this research. A total of 16 nonlinear finite-element (FE) models were generated to replicate stub column tests that were experimentally tested by others in previous research. Hence, a parametric study is conducted using forty FE models developed to investigate the local buckling behavior under various slenderness ratios comprehensively. The developed models cover slenderness ratios ranging from 20 to 1226 and steel grades S460 and S1100 with yield stress equal 460 MPa and 1152 MPa, respectively. The FE results were combined with 105 previously collected experimental results to assess the applicability of existing codified design methodologies in the Euro code and the North American codes of cold-formed CHS. Based on the results of this study, new cross-sectional slenderness limits and new design equations for more efficient simple designs were presented for high and very-high strength steel circular hollow sections and Compared with the results of experimental tests and FE models.

Liability to prevent consequences of an unhealthy situation due to accumulating toxic and hazardous emissions caused by open dumping of municipal solid waste with increasing urbanization has necessitated a renewed thinking on waste disposal. Grate fired incineration systems were adopted by urban managements in past and present but with criticisms due to formation of air borne emissions. Improved combustion methods like fluidized bed are now propagated because of current requirements like efficient energy recovery potential, stricter emission norms, adaptability with urban growth, adaptability to co-firing with other waste like biomass, edible oil wastes or industrial effluent and integration with conventional energy generation. Such a comprehensive and futuristic approach is more sustainable for the community. Multi-criteria decision-making tool is used to identify the best technology option among grate combustion and fluidized bed combustion for disposing and energy recovery from waste. Ten different collection and disposal options involving two combustion methods, namely, grate combustion and fluidized bed method are considered. Utilization of the energy is done for three end uses, namely, power generation, water distillation and district cooling. Two different regions in Saudi Arabia are considered for this study under two types of scenario, namely with recycling and without recycling. The different options are prioritized based on their overall ranking using five major performance factors.

Modern surface analysis techniques are expanding significantly possibilities in description and attribution of historical artifacts. The present study is aimed to compare analytical ability of the X-ray fluorescent analysis (XRF) typically applied for studies ceramic art items with novel surface analysis methods: time-of-flight secondary ions mass spectroscopy (SIMS) and X-ray photoelectron spectroscopy (XPS) that were applied on measurements of elemental compositions of powder raw materials for porcelain fabrication: pegmatite P-1, Vesco-prima clay, kaolin, bone earth, dolomite, and feldspar. It was shown that SIMS successfully measures light elements unnoticeable for XRF such as lithium, beryllium, boron and heavy impurities not found out by XRF such as cobalt, cerium, copper, rubidium, vanadium. XPS has found thorium in the kaolin sample that was not detected by SIMS and XRF. The paper provides also values of chemical shifts of some XPS lines observed in materials under examination. The obtained information on relations of basic and impurity elements revealed by XRF and SIMS together with XPS chemical shifts data could be used to expose contemporary falsifications of historical porcelain and to attribute porcelain items during conservation process.

Wicked problems, such as climate change, systemic poverty, and healthcare inequalities, rep- resent the most pressing and intractable challenges facing society today. We propose a novel approach that leverages complex systems theory as a comprehensive framework for understanding and tackling these challenges. By applying concepts such as state space analysis, attractors, and nonlinear dynamics, we offer a sophisticated toolkit for mapping the intricate web of interactions defining wicked problems and exploring potential interventions. Our approach moves beyond tradi- tional reductionist methods, embracing the inherent complexity and dynamism of these issues. We demonstrate how this framework can lead to more nuanced, adaptive, and effective strategies for addressing persistent societal problems. Despite the prevalence of wicked problems and the avail- ability of complex systems theory, practical applications of this approach remain uncommon. This paper aims to bridge this gap, illustrating the potential of complex systems theory to transform our engagement with intractable problems and pave the way for more robust, holistic solutions. By synthesizing insights from mathematics, physics, and social sciences, we present a transdisci- plinary approach that promises to revolutionize how we conceptualize and address society’s most challenging issues.

Neisseria gonorrhoeae is a bacterium that causes the sexually transmitted infection gonorrhea. This study employed molecular docking simulations to investigate potential interactions between natural molecules and the Fe(3+) ions import ATP-binding protein fbpC in Neisseria gonorrhoeae FA 1090. Among the screened molecules, Hypericin exhibited a remarkable binding energy of approximately -13.2 kcal/mol, indicating a robust and favorable binding affinity with the target protein. This promising outcome suggests that Hypericin could serve as a potential candidate for further exploration as a therapeutic agent against Neisseria gonorrhoeae infections. However, it is imperative to emphasize that these in silico findings necessitate experimental validation through in vitro and in vivo studies to ascertain the actual efficacy and safety of Hypericin as a treatment option.

Steel damper columns (SDCs) are energy-dissipating members that are suitable for reinforced concrete (RC) buildings and are often used for multistory housing. The evaluation of the peak deformation and hysteretic dissipated energy of such building structures is essential for the rational seismic design of RC buildings with SDCs. In a previous study, the authors proposed an energy-based prediction procedure for the peak and cumulative response of an RC frame building with SDCs. In this procedure, the accuracy of the equivalent velocity of the maximum momentary input energy (V_ΔE1^*)–peak equivalent displacement (D₁^*_max) relationship is essential for high quality predictions. In this article, the V_ΔE1^*–D₁^*_max relationships of RC moment-resisting frames with and without SDCs are invested using a critical pseudo-double impulse (PDI) analysis based on a study by Takewaki and coauthors. The results show that the V_ΔE1^*–D₁^*_max relationship obtained from the critical PDI analysis agrees well with that calculated from the equations proposed in the previous study.

Spectral image analysis has the potential to replace traditional approaches for assessing plant responses to different types of stress including herbicide, through non-destructive and high-throughput screening (HTS). Therefore, this study was conducted to develop a rapid bioassay method using a multi-well plate and spectral image analysis for diagnosis of herbicide activity and mode of action. Crabgrass (Digitaria ciliaris) as a model weed was cultivated in multi-well plates and subsequently treated with 6 herbicides with different modes of action when they reached the 1-leaf stage, using only a quarter of the recommended dose. To detect plant response to herbicides, plant spectral images were acquired after herbicide treatment using RGB, infrared (IR) thermal, and chlorophyll fluorescence (CF) sensors and analyzed for diagnosing herbicide efficacy and mode of action. Principal Component Analysis (PCA) using all spectral data successfully distinguished herbicides and clustered depending on their modes of action. The performed experiments have shown that the multi-well plate assay combined with spectral image analysis can be successfully applied for herbicide bioassay. In addition, the use of spectral image sensors, especially in CF image, would facilitate HTS by enabling rapid observation of herbicide response, as early as 3 hours after herbicide treatment.

Recent studies have shown that the mechanical properties of bioinspired periodic composite materials can be strongly influenced by finite deformation effects, leading to highly nonlinear static and dynamic behaviors at multiple length scales. For instance, in porous periodic nacre-like microstructures, microscopic and macroscopic instabilities may occur for a given uniaxial loading process and, as a consequence, wave attenuation properties may evolve as a function of the microstructural evolution, giving it the designation of metamaterials. The numerical outcomes provide new opportunities to design bioinspired soft composite metamaterials characterized by high deformability and enhanced elastic wave attenuation capabilities given by the insertion of voids and lead cores.

Agricultural productivity is expected to decrease under changing climate conditions that correspond to the stability of West African food systems. Although numerous studies evaluated food production gaps or impacts of climate variability on crop yields, many uncertainties are associated with climate extremes, but also rapid population growth and the corresponding dietary lifestyle determine farming activities. Here, we present a food supply and demand analysis based on the relationship between climate change, crop production, as well as population growth in three experimental sites from southwestern Burkina Faso to southwestern Ghana. The method consists of a tiered approach that encompassed a statistical analysis of historical climate as well as agricultural time series using boxplots and a trend test by Mann-Kendall. Food balance sheets were calculated by estimating the demand using a population growth model linked to food supply with local consumption patterns. Results of the boxplot analysis revealed for the majority of climate time series light to strong right-skewed distributions with either a few heavy rainfall or hot temperature events. We found almost insignificant rainfall and temperature trends for both sites in the Sudano-Guinean savannah. The rainfall regime of southwestern Ghana indicated an insignificant upward or downward trend while the temperature significantly increased over time. Crop yield boxplots demonstrated mainly right-skewed distributions for cereals, legumes and tubers as well as cash crops such as cotton. Backed by the Mann-Kendall test, maize and sorghum significantly enhanced in both study areas of the Sudano-Guinean savannah. Southwestern Ghana depicted a different crop pattern where cassava and plantain showed a strong upward trend at a confidence level of 1%. The grouped food balances sheets across the regions illustrated a surplus for the Sudano-Guinean savannah ranging from 221.2 megatons (Mt) to 4,846.77 Mt while southwestern Ghana exhibited a deficit between -193.33 Mt and -16,188.82 Mt. Despite a growing yield of various crops, food demand outpaces the regional production. Hence, it is recommended to focus on a larger array of commodities, imports and marketing of farm products.

A sustainable smart tourism ecosystem relies on building digital networks that link tourists to destinations. This study explores how web and immersive technologies, such as the VRRO platform, can improve tourism by offering interactive experiences to augment traditional travel. The study underscores the role of VRRO's design in enhancing user interaction and engagement, thereby encouraging the generation of user content and the development of a virtual tour network. An empirical analysis investigates user engagement, revealing that the platform is accessible to users regardless of their technological familiarity. It suggests a high likelihood of sustained use and recommendations. In conclusion, VRRO demonstrates how accessible and innovative technologies in tourism modernize travel experiences and significantly contribute to the evolution of the broader tourism ecosystem.

The present article summarises Earth Observation (EO)-based rice mapping strategies since 1979, with a focus on data, methodologies, and methods based on 3,700 research publications across global literature and its comparison with the Vietnamese Mekong Delta (VMD). Various quan-titative analyses were conducted through bibliometric analysis using the VOS viewer and Scopus database. Optical images, particularly MODIS and Landsat time series datasets, were found to be the most commonly utilized. Landsat data had the highest share in the global context, while MODIS data research dominated in the VMD, while Sentinel series data and the Google Earth Engine (GEE) platform became more popular in recent years. The research on rice mapping using UAVs has been gradually creeping into global rice mapping research but is a loophole yet to be implemented in the VMD. The most widely used approaches for rice mapping globally were Random Forest, Support Vector Machine, and Principal Component Analysis. Indices like EVI, NDVI, and RVI were commonly used for rice mapping and monitoring. The findings underscore the critical role of EO-based rice mapping studies in the VMD in addressing sustainability and food security chal-lenges.

The Chen circuit system, a three-dimensional autonomous system with intriguing dynamics, has gained considerable attention due to its potential applications in diverse scientific fields. In this article, a Chen circuit-like system is defined and a comprehensive dynamics has been studied. The system exhibits chaotic dynamics, limit cycles, and bifurcations, making it a captivating subject of study. By employing numerical simulations and analytical techniques, we explore the system's stability and identify critical parameter values leading to qualitative changes. Notably, we delve Hopf bifurcations, which give rise to stability changes and the emergence of limit cycles. Furthermore, we analyze the fractal dimension of the system's attractor, providing insights into its complexity and self-similarity. Through a systematic examination of the Chen circuit-like system, we deepen our understanding of its intricate dynamics and offer valuable insights into the underlying mechanisms. The findings contribute to the field of dynamical systems and hold potential implications in areas such as chaos-based secure communications, signal processing, and nonlinear control. This work serves as a valuable reference for researchers and practitioners interested in the dynamics and bifurcation analysis of nonlinear systems.

Concrete-filled steel tube (CFST) members have been widely used in the field of civil engineering due to their advanced superior mechanical properties. However, internal defects such as concrete core voids and interface debonding are likely to weaken the load-carrying capacity and stiffness of these members, which affects safety and serviceability of CFST structures. Visualizing the inner defects of concrete core in CFST members have been a critical need in civil engineering construction, a travel time tomography (TTT) is introduced to quantitatively identify and visualize the sizes and positions of CFST members in this paper. Moreover, a parameter analysis is performed to investigate the relationship between TTT imaging qualities and influence factors, e.g. inversion parameters, defect sizes and positions. The effectiveness and accuracy of the TTT algorithm are verified by several numerical examples and the results demonstrate that TTT can identify the sizes and positions of concrete core void defects in CFST members efficiently and several inversion parameters including model weighting matrix and inversion grid size really pose a significant impact on the imaging results of CFST members. In addition, several optimum parameters are recommended to benefit the future study of the promising TTT approach for CFST members.

This study aimed to present a systematic meta-analysis examining the association between Helicobacter pylori (H. pylori) infection, hemoglobin A1c levels, and the development of type 1 diabetes mellitus. The initial search identified 451 articles related to the association between H. pylori infection and T1DM. Among them, 14 articles had 2,971 participants meeting the inclusion criteria for advanced meta-analysis. A significant association was observed between H. pylori infection and T1DM, with an odds ratio (OR) of 1.87 (95% confidence interval [CI]: 1.27–2.78, p = 0.002). A subgroup analysis on H. pylori status showed that a longer duration of diabetes diagnosis and higher hemoglobin A1c levels were significantly associated with H. pylori infection (p < 0.001 for both). However, there was no significant association between H. pylori infection and the diagnostic age of diabetes (p = 0.306). These findings contribute to the understanding of the association between H. pylori infection and T1DM, thereby highlighting the potential role of H. pylori in influencing the duration and glycemic control of diabetes.

The number of disputes related to water that reach courts in Chile has increased in the last decades, the topics of these disputes have become more complex, and the current conflict resolution system has not been able to adjust to this situation. This study analyses colonial texts from water-related conflicts that were addressed at the Royal Hearings in Santiago (1691-1800) and from the Cabildo gatherings (1541-1802), using an adaptation of the Institutional Analysis and Development framework. The research shows a strong institutional system surrounding conflict resolution during colonial times, with nested schemes and empowered figures appointed in leading roles. However, a lack of equity and inclusion of all actors is also visible, reducing its legitimacy. At present, the increasing value of water and a sense of distrust in the institutional system have led to longer and more complex conflict resolution processes. Here, learning from past times about the empowerment of the institutional system for solving water disputes could be useful. An increased support towards initial conflict resolution mechanisms, giving space for local knowledge and generating stronger participation in these initial steps, could also be a lesson for the future.

In the context of Inverse Analysis in Civil Engineering, parameter identification and model calibration, of a structural system, relying on dynamic measurements, are subjects of a growing research interest. In the present contribution, the topic is tackled with reference both to simplified structural numerical examples and to a specific case study, namely a historical road three-span reinforced concrete arched bridge, with vibrational data previously acquired by standard wired accelerometers on the deck, under operational traffic conditions. In particular, the present work aims at focussing on the identification issues, concerning the definition of a maximum allowable threshold number of sought material parameters (e.g., Young’s moduli and mass densities of different structural components), with respect to the amount of available measurement data, and the investigation of the inverse analysis discrepancy function to be optimised, in order to set the intrinsic issue of multiple “realizations”, in case of a plain use of modal properties, and in view of forming a well–posed optimisation problem. Structural modelling, sensitivity analysis and numerical optimisation approaches are herein combined toward a robust and efficient identification strategy, to be effectively employed in structural assessment and diagnosis, also with respect to originally available or enriched sets of experimental data. The proposed methodology, and collected results, shall outline an efficient identification procedure, in view of automated inverse analysis, practically oriented to the dynamic assessment and structural diagnosis in the Civil Engineering context, as applied e.g. to strategic bridge infrastructures.

There is a need for cost-effective and non-invasive methods of monitoring feeding behavior in livestock operations, considering the significant impact of feed costs on economic efficiency and assisting in detecting health issues of group-fed animals. This paper proposes using deep learning-based computer vision techniques to detect pen-fed beef cattle feeding behavior using Mask Region-based Convolutional Neural Network (RCNN). A deep learning model was pre-trained on the Common Objects in Context (COCO) dataset to generate cattle instance segmentation. Manually defined feed bunk polygons are compared with these segmentation masks to derive feeding time for each bunk. A full day’s worth of video data and the corresponding physical sensor data are collected for the experiment. By benchmarking the computer vision detected data with physical ground truth over random time segments from morning to evening (thus various lighting conditions), the optimal thresholds for Mask RCNN are determined to be 0.7 for bounding boxes and 0.1 for masks. Using these parameters. The reports suggest that the computer vision system achieved a precision of 87.2% and a recall of 89.1%, signifying precise detection of feeding events. Our study, to the best of our knowledge, was one of the first investigations of instance segmentation on feeding time sense, which combines deep learning methods with traditional computer vision logistics, reporting on feeding time data collection and processing, camera testing and adjustment, and performance evaluation. Future research directions include computer vision applied in feed grading and animal re-identification for individual production analysis.

In a limestone quarry mine, fragmentation is a crucial outcome of blasting operations. The optimization of blasting operations greatly benefits from the prediction of rock fragmentation. The main factors that affect fragmentation are rock mass characteristics, blast geometry, and explosive properties. This paper is a step towards the implementation of machine learning and deep learning algorithms for predicting the extent of fragmentation (in percentage) in opencast mining. Various parameters can affect fragmentation. But, in this paper initially, ten parameters (spacing, drill hole diameter, burden, average bench height, powder factor, number of holes, charge per delay, uniaxial compressive strength, specific drilling, and stemming) are collected to train the model. However, due to a weak correlation with rock fragmentation, drill diameter, Average bench height, compressive strength, stemming, and charge per delay are eliminated to reduce model complexity. A total of 219 data sets having five input features i.e., the number of holes, spacing, burden, specific drilling, and powder factor are used to develop the models. To predict rock fragmentation due to blasting in limestone quarry mines, both machine learning models (Random Forest Regression (Bagging), Support Vector Regression, and XG Boost Regression (Boosting)), as well as a deep learning model (Neural Network Regression), are applied to develop a model that can optimize the prediction of fragmentation. The Artificial neural network model optimization showed that the model with architecture 64-32-16-1 can perform well giving MSE (mean squared error) values of 41.32 and 28.59 on training and test data respectively. The R2 value for both training and test is 0.83. Random Forest regression is also performing well compared to SVR and XG boost with the MSE value 12.37 and 9.89 on training and testing data respectively. Here, the R2 value for both sets are 94%. Based on the permutation importance and Shapely plot values, the powder factor has the highest impact, and the burden has the lowest impact on fragmentation.

Farmers are continually looking for new reliable, objective and non-invasive methods for evaluation of ewe condition. Live weight (LW) and body condition score (BCS) are used by farmers as a basis to determine the condition of the animal. Body composition is an important aspect of monitoring animal condition. The body composition is the amount of fat, lean and bone; knowing the amount of each is important because the information can be used for better strategic management interventions. Experiments were conducted to establish the relationship between body composition and body parameters, at key life’s stages (weaning and pre-mating), using measurements automatically determined by an image processing ap-plication at Lincoln University sheep farm for 88 Coopworth ewes. Computerized Tomography technology was used to develop relationship with body parameters and a subset was used to validate the predicted model. Multivariate linear regression (MLR), artificial neural network (ANNs) and regression tree (RT) statistical analysis methods were evaluated to determine their efficacy to predict body fat, lean and bone. The results showed a correlation between fat, lean and bone determined by CT and the fat, lean, bone weight estimated by live weight and body parameters calculated using the image processing application with R2 values of 0.90 for fat, 0.72 for lean and 0.50 for bone using ANNs statistical model. From these results, farmers can utilize accurate measures of fat which will enhance nutritional and management practices.

With the growing of university chemistry experiment projects, scientific research personnel, specialized equipment, laboratory accident is increasing yearly. And accident data lacks a safety platform to store related information and cannot be guaranteed for efficient conditional sharing. To solve these problems, we designed a laboratory accident system to store, share related data and predict risk level. In this paper, we manually collected chemistry laboratory accidents by python software and class assignments, then analyses risk factor variables using Spsspro, finally established a prediction model using Stata. We intended to register laboratory related data into proposed chemistry accident system based on data ownership safety architecture. The chemistry accident system can break data barriers using confirmation and authorization key technology to trace non-tampered data source in real time when emergency accident happens. Meanwhile, our proposed system can use our designed accident risk model to predict risk level of any experiment project. It can also recommend appropriate safety education models.

Converting natural language into machine language that can be recognized by distributed systems is the core challenge of intelligent interactive interfaces and human-machine dialogue systems. The human-machine interface interaction of large distributed SCADA measurement and control system is tedious and the operation and maintenance cost is high, so it is significant to design an intelligent natural language interaction interface for distributed measurement and control system. In this paper, we design the intermediate language format of SCADA system, i.e., Key-value logic form, and then formulate the Text2SCADA framework and propose the TICS algorithm and SDPA algorithm, the former adopts the keyword extraction and cosine similarity optimization algorithm to complete the structured extraction of natural language for basic natural language instructions, and the latter adopts the keyword extraction and cosine similarity optimization algorithm to complete the structured extraction of natural language for relatively The latter one adopts the algorithm of dependent syntactic analysis for the structured representation of natural language instructions with relatively complex natural language instructions. Based on the above algorithms, a lightweight information extraction model based on DGCNN and probabilistic graph ideas is constructed, aiming to enhance the scientific generalization ability of the framework on unknown instruction sets. The experimental results show that the proposed intelligent natural language interface can better solve the human-machine interface interaction problem of distributed SCADA measurement and control system. The average accuracy, recall and F-value of instruction parsing reach 89.27\%, 89.28\% and 89.27\%, respectively. The average response time is 1.593 s. Especially, it provides a more convenient means of interaction for industrial and agricultural information control.

The thoracic surgery has increased drastically in recent years, especially in the light of the severe outbreak of 2019 novel coronavirus disease (COVID-19). Routine “passive” chest computed tomography (CT) screening of inpatients detects some pulmonary diseases requiring thoracic surgeries timely. As an essential device for thoracic anesthesia, the double-lumen tube (DLT) is particularly important for anesthesia and surgery. With the continuous upgrading of the DLTs and the widespread use of the fiberoptic bronchoscopy (FOB), the position of DLT in thoracic surgery is gradually becoming more stable and easier to observe or adjust. However, the DLT malposition still occurs during transferring patients from supine to lateral position in thoracic surgery, which leads to lung isolation failure and hypoxemia during one-lung ventilation (OLV). Recently some innovative DLTs or improved intervention methods have shown good results in reducing the incidence of DLT malposition. This review aims to summarize the recent studies of the incidence of left-sided DLT malposition, the reasons and effects of malposition, and summarize current methods for reducing DLT malposition and prospects for possible approaches. Meanwhile, we use bibliometric analysis to summarize the research trends and hot spots of the DLT research.

Fibroepithelial lesions (FL) of the breast, in particular Phyllodes tumors (PT) and fibroadenomas, pose a significant diagnostic challenge. There are no generally accepted criteria that distinguish benign, borderline, malignant PT, and FA. Combined genome-wide DNA methylation and copy number variant (CNV) profiling is an emerging strategy to classify tumors. We compiled a series of patient-derived archival biopsy specimens reflecting the FL spectrum and histological mimickers including clinical follow-up data. DNA methylation and CNVs were determined by well-established microarrays. Comparison of the patterns with a pan-cancer dataset assembled from public resources including "The Cancer Genome Atlas" (TCGA) and "Gene Expression Omnibus" (GEO) suggests that FLs form a methylation class distinct from both control breast tissue as well as common breast cancers. Complex CNVs were enriched in clinically aggressive FLs. Subsequent fluorescence in situ hybridization (FISH) analysis detected respective aberrations in the neoplastic mesenchymal component of FLs only, confirming that the epithelial component is non-neoplastic. Of note, our approach could lead to the elimination of the diagnostically problematic category of borderline PT and allow for optimized prognostic patient stratification. Furthermore, the identified recurrent genomic aberrations such as 1q gains (including MDM4), CDKN2a/b deletions and EGFR amplifications may inform therapeutic decision-making.

Individuals from rural areas are increasingly using social media as a means of communication, receiving information, or actively complaining of inequalities and injustices. This study captured 57 days’ worth of Twitter data from June to August 2021 related to rural health using English language keywords. The study utilised social network analysis and natural language processing to analyse the data. It was found that Twitter served as a fruitful platform to raise awareness of problems faced by those living in rural areas. Overall, Twitter was utilised in rural areas to express complaints, to debate, and share information. Twitter could be leveraged as a powerful social listening tool for individuals and organisations who want to gain insight into popular narratives around rural health.

Tuberculosis (TB) remains a global healthcare crisis with an estimated 10 million new cases and 1.4 million deaths per year TB is caused by infection with the major human pathogen Mycobacte-rium tuberculosis, which is difficult to rapidly diagnose and treat. There is an urgent need for new methods of diagnosis, sufficient in vitro models which capably mimic all physiological condi-tions of the infection, and high-throughput drug screening platforms. Microfluidic-based tech-niques provide single-cell analysis which reduces experimental time, the cost of reagents, and have been extremely useful for gaining insight into monitoring microorganisms. This review out-lines the field of microfluidics and discusses the use of this novel technique so far in M. tuberculo-sis diagnostics, research methods, and drug discovery platforms. The practices of microfluidics have promising future applications for diagnosing and treating TB.

Methods to handle uncertainty in economic evaluation have gained much attention in the literature and the cost-effectiveness acceptability curve (CEAC) is the most widely used method to summarize and present uncertainty associated with program costs and effects in cost-effectiveness analysis. Some researchers have emphasized the limitations of the CEAC for informing decision and policy makers as the CEAC is insensitive to radial shifts of the joint distribution of incremental costs and effects in the North-East and South-West quadrants of the cost-effective plane (CEP). Furthermore, it has been pointed out that the CEAC does not incorporate risk-aversion in valuing uncertain costs and effects. In the present article we show that the cost-effectiveness affordability curve (CEAFC) captures both dimensions of the joint distribution of incremental costs and effects on the CEP and is therefore sensitive to radial shifts of the joint distribution on the CEP. Furthermore, the CEAFC also informs about the budget impact of a new intervention as it estimates the joint probability an intervention is both affordable and cost-effective. Moreover, we show that the cost-effectiveness risk-aversion curve (CERAC) allows to incorporate risk-aversion into the analysis and can therefore be used to inform decision-makers who are risk-averse. We use data from a published cost-effectiveness model of palbociclib in addition to letrozole versus letrozole alone for the treatment of oestrogen-receptor positive, HER-2 negative, advanced breast cancer to demonstrate the differences between CEAC, CEAFC and CERAC and show how these can jointly be used to inform decision and policy makers.

The City of Meycauayan is considered as one of the most polluted cities in the developing world on the account of industrial discharges of toxic materials to the environment. This work investigated the sources of the heavy metal pollution by analyzing soil and sediment samples for heavy metals (Cr, Hg, Ni, and Pb) together with selected environmental indicators (TN, TOM, and TP) located along the Meycauayan River. Hierarchical cluster analysis (HCA), principal components analysis (PCA), and Pearson correlation analysis (CA) were used to identify the sources of the metals. Results showed delineated locations of severe levels of heavy metal pollution downstream because of the concentration of industrial activities. Cr contributed more than any other heavy metals analyzed due to proliferation of tanneries discharging untreated wastewaters to the river. Significant inputs of Pb and Hg from Pb-acid battery recycling and gold smelting industries were also found. Risk assessments indicated severe levels of heavy metal pollution where industrial activities are concentrated. The mean Cr, Pb, Ni, and Hg in the sampling locations have mean incidences of toxicity of 91.7 %, 53.6 %, 27.7 %, and 70.0 %, respectively. Our study showed a serious need to address heavy metal pollution in Meycauayan.

There exists a compelling need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February of 2021 and late March of 2021 shows that in spite of overall strength of positive sentiment, and increasing numbers of Americans being fully vaccinated, negative sentiment about COVID-19 vaccines still persists among sections of people who are hesitant towards the vaccine. In this study, we performed sentiment analytics on vaccine tweets, studied changes in public sentiment over time, conducted vaccination sentiment validation using actual vaccination data from the US CDC and Household Pulse Survey (HPS), explored influence of maturity of Twitter user-accounts and generated geographic mapping of sentiments by location of Twitter users. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework which was developed for COVID-19 sentiment analytics, to systematically analyze directions for public policy processes to potentially improve the administration of vaccines. Application of the PSS framework provides important time sensitive insights for state and federal government agencies and associated organizations to better implement public policy processes for healthcare management, communication, transparency, motivation and societal operational policies such as social distancing. These insights are expected to contribute to processes that can expedite the vaccination program and move closer to the cherished herd immunity goal.

Proposed as a response to the increasing global need for environmental protection, a green port balances economic vibrancy and environmental protection. However, because exhaust emissions (e.g., CO2 or sulfide) are difficult to monitor in and around ports, data on such emissions are often incomplete, which hinders research on this topic. To remedy this problem, this study aimed to formulate a method for collecting CO2 emissions data at their source; this method was applied to collect real-world operating data from a large container-handling company in Taiwan. Specifically, to account for undesirable outputs, we formulated a method that combines (1) data envelopment analysis based on a modified slack-based measure and (2) a multichoice goal programming approach. We found that rubber-tired gantry cranes are the greenest and should be used. Our findings aid port managers in selecting port equipment that best balances between environmental protection and profitability.

Current acceleration in digital innovations, the unexpected challenges in our social interactions, open access to virtualization, huge limitation in our physical spaces, and unpredictable changes in our old lifestyles - as originated from the COVID-19 global pandemic in 2020 - continue to provide us with a framework, rapidly updating under our eyes, of the modifications our world is undergoing by pursuing into the “new digital age”. Or, as many scholars say nowadays, into the new normal! These are shared and deep changes that, regardless of their permanence or temporariness in the time, concretely stress, ever more greatly, their “own” effects on how ideally a city should function. Forcing us to reflect on the real ability to achieve choices and visions for the future by taking vantage from the new digital platforms. In the pages of this article authors, through different eyes but sharing an early response to the matter of new Governance, explore the theme of a radical change of those already consolidated paradigms and, therefore, of the innovations that are transforming the way we understand our society and its technologic advancements, economics, and culture, as defined through dimensions of time and space. This article identifies a methodologic vision for acquiring a more democratic and participatory (inclusive) dimension in the newest configuration of contemporary cities, the new smart city, and in the possible innovations in reading the common sentiments and wishes through the new digitalized world. The analysis investigates how ICT is altering the meanings/ideas of “urban planning”, driving us toward a more effective “governance” through a citizen-centred digital approach. Indeed, city governance's success must be measured based on the “listening capacity” of the inhabitants and the facilities that we are capable to provide to citizens. “Sentiment Analysis” tool is tested as a useful tool to achieve these aims.

The spatiotemporal variability of vegetation in the Middle East was investigated for the period 2001–2019 using the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day/500 m composites of the Normalized Difference Vegetation Index (NDVI; MOD13A1). The results reveal a strong increase in the NDVI coverage in the Middle East during the study period (R = 0.75, p-value = 0.05). In Egypt, the annual coverage exhibits the strongest positive trend (R = 0.99, p-value = 0.05). In Turkey, both the vegetation coverage and density increased from 2001 to 2019, which can be attributed to the construction of some of the biggest dams in the Middle East, such as the Atatürk and Ilisu dams. Significant increases in the annual coverage and maximum and average NDVI in Saudi Arabia are due to farming in the northern part of the country for which groundwater and desalinated seawater are used. The results of this study suggest that the main factors affecting the vegetation coverage in the Middle East are governmental policies. These policies can have a positive effect on the vegetation coverage in some countries such as Egypt, Saudi Arabia, Qatar, Kuwait, Iran, and Turkey.

The understanding of water quality and its underlying processes is important for the protection of aquatic environments enabling the rare opportunity of access to a domain expert. Hence, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series resulting in explanations that are interpretable by a domain expert. The XAI combines in three steps a data-driven choice of a distance measure with explainable cluster analysis through supervised decision trees. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The XAI does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two comparable decision-based XAIs were unable to provide meaningful and relevant explanations from the multivariate time series data. Open-source code in R for the three steps of the XAI framework is provided.

The outbreak of novel Coronavirus (SARS-COV-2 ) disease (COVID-19) in Wuhan has attracted worldwide attention. SARS-COV-2 known to share a similar clinical manifestation that includes various symptoms such as pneumonia, fever, breathing difficulty, and in particular, SARS-COV-2 also causes a severe in ammation state that leads to death. Consequently, massive and rapid research growth has been observed across the globe to elucidate the mechanisms of infections and disease progression in genotype and phenotype scale. Data Science is playing a pivotal role in in-silico analysis to draw hidden and novel insights about the SARS-COV-2 origin, pathogenesis, COVID-19 outbreak forecasting, medical diagnosis, and drug discovery. With the availability of multi-omics, radiological, biomolecular, and medical data urges to develop novel exploratory and predictive models or customise exiting learning models to t the current problem domain. The presence of many approaches generates the need for the systematic surveys to guide both data scientists and medical practitioners. We perform an elaborate study on the state-of-the-art data science method ologies in action to tackle the current pandemic scenario. We consider various active COVID-19 data analytics domains such as phylogeny analysis, SARS-COV-2 genome identi cation, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological analysis, and most importantly (existing) drug discovery. We highlight types of data, their generation pipeline, and the data science models in use. We believe that the current study will give a detailed sketch of the road map towards handling COVID-19 like situation by leveraging data science in the future. We summarise our review focusing on prime challenges and possible future research directions .

Hyper spectral imaging (HSI) is a technique that is used to obtain the spectrum for each pixel in the image. It helps in finding objects and identifying materials etc. Such an identification is very difficult using other imaging techniques. It allows the researchers to investigate the documents without any physical contact. Nowadays detection of unequal Ink mismatch based on HSI has shown vast improvement in distinguishing the inks. Detection of unequal Ink mismatch is an unbalanced clustering problem. This paper used K-means Clustering for ink mismatch detection. K-means Clustering find same subgroups in the data based on Euclidean distance. This paper demonstrates performance in unequal Ink mismatch based on HSI.

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics, in order to provide coherent and high-quality data that enables discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce NoTaMe, an analytical workflow for non-targeted metabolic profiling approaches utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research, and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data, and finally to identify and interpret the compounds that have emerged as interesting.

We examined whether the ambient illuminance during extended wakefulness modulates the homeostatic increase in human deep sleep [i.e. slow wave sleep (SWS) and electroencephalographic (EEG) slow-wave activity (SWA)] in healthy young and older volunteers. Thirty-eight young and older participants underwent 40 hours of extended wakefulness [i.e. sleep deprivation (SD)] once under dim light (DL: 8 lux, 2800K), and once under either white light (WL: 250 lux, 2800K) or blue-enriched white light (BL: 250 lux, 9000K) exposure. Subjective sleepiness was assessed hourly and polysomnography was quantified during the baseline night prior to the 40-h SD and during the subsequent recovery night. Both the young and older participants responded with a higher homeostatic sleep response to 40-h SD after WL and BL than after DL. This was indexed by a significantly faster intra-night accumulation of SWS and a significantly higher response in relative EEG SWA during the recovery night after WL and BL than after DL for both age groups. No significant differences were observed between the WL and BL condition for these two particular SWS and SWA measures. Subjective sleepiness ratings during the 40-h SD were significantly reduced under both WL and BL compared to DL, but were not significantly associated with markers of sleep homeostasis in both age groups. Our data indicate that not only the duration of prior wakefulness, but also the experienced illuminance during wakefulness affects homeostatic sleep regulation in humans. Thus, working extended hours under low illuminance may negatively impact subsequent sleep intensity in humans.

This paper is focused on mapping the current evolution of Internet of Things (IoT) and its associated cyber risks for the Industry 4.0 (I4.0) sector. We report the results of a qualitative empirical study that correlates academic literature with 14 - I4.0 frameworks and initiatives. We apply the grounded theory approach to synthesise the findings from our literature review, to compare the cyber security frameworks and cyber security quantitative impact assessment models, with the world leading I4.0 technological trends. From the findings, we build a new impact assessment model of IoT cyber risk in Industry 4.0. We therefore advance the efforts of integrating standards and governance into Industry 4.0 and offer a better understanding of economics impact assessment models for I4.0.

Palmitic acid metabolism involves delta-9 and delta-6 desaturase enzymes forming palmitoleic acid (9cis-16:1; n-7 series) and sapienic acid (6cis-16:1; n-10 series), respectively. The corresponding biological consequences and lipidomic research on these positional MUFA isomers are under development. Furthermore, sapienic acid can bring to the de novo synthesis of the n-10 polyunsaturated fatty acid (PUFA) sebaleic acid (5cis,8cis-18:2), but such transformations in cancer cells are not known. The model of Caco-2 cell line was used to monitor sapienic acid supplementation (150 and 300 μM) and evidence the formation of n-10 fatty acids as well as their incorporation at levels of membrane phospholipids and triglycerides. Comparison with palmitoleic and palmitic acids evidenced that lipid remodeling was influenced by the type of fatty acid and positional isomer, with increase of 8cis-18:1, n-10 PUFA and decrease of saturated fats in case of sapienic acid. Cholesteryl esters were formed only in case of sapienic acid. EC₅₀ of sapienic acid (232.3 μM at 96 hrs) was the highest found among the tested fatty acids, thus influencing cell viability that was only reduced at 25% at 300 μM, whereas palmitoleic acid induced cell death. Two-photon fluorescent microscopy with Laurdan as a fluorescent dye provided information on membrane fluidity, highlighting that sapienic acid increases the distribution of fluid regions, probably connected with the formation of 8cis-18:1 and the n-10 PUFA in cell lipidome. Our results bring evidence for MUFA positional isomers and de novo PUFA synthesis for developing lipidomic analysis and cancer research.

We present the real number system as a natural generalization of the natural numbers. First, we prove the co-finite topology, $Cof(\mathbb N)$, is isomorphic to the natural numbers. Then, we generalize these results to describe the continuum $[0,1]$. Then we prove the power set $2^{\mathbb Z}$ contains a subset isomorphic to the non-negative real numbers, with all its defining structure of operations and order. Finally, we provide two different constructions of the entire real number line. We see that the power set $2^{\mathbb N}$ can be given the defining structure of $\mathbb R$. The constructions here provided give simple rules for calculating addition, multiplication, subtraction, division, powers and rational powers of real numbers, and logarithms. The supremum and infimum are explicitly constructed by means of a well defined algorithm that ends in denumerable steps. In section 5 we give evidence our construction of $\mathbb N$ and $\mathbb R$ are canonical; these constructions are as natural as possible. In the same section, we propose a new axiomatic basis for analysis. In the last section we provide a series of graphic representations and physical models that can be used to represent the real number system. We conclude that the system of real numbers is completely defined by the order structure of $\mathbb N$.}

In this work a new pre-tuning multivariable PID controllers method for quadrotors is put forward. A procedure based on LQR/LQG theory is proposed for attitude and altitude control. With the aim of analyzing performance and robustness of the proposed method, a non-linear mathematical model of the DJI-F450 quadrotor is employed, where rotors dynamics, togheter with sensors drift/bias properties and noise characteristics of low-cost comercial sensors typically used in this type of applications (such as MARG with MEMS technology and LIDAR) are considered. In order to estimate the state vector and compensate bias/drift effects on rate gyros of the MARG, a combination of filtering and data fusion algorithms (Kalman filter and Madgwick algorithm for attitude estimation) are proposed and implemented. Performance and robutsness analysis of the control system is carried out by means of numerical simulations, which take into account the presence of uncertainty in the plant model and external disturbances. The obtained results show that the proposed pre-tuning method for multivariable PID controller is robust with respect to: a) parametric uncertainty in the plant model, b) disturbances acting at the plant input, c) sensors measurement and estimation errors.

In the fifth generation communication system, millimeter wave (mmWave) networks will coexist with traditional micro wave (μWave) networks, which allows for higher data transmission rate and better user experience. In this paper, we give a comprehensive framework of mathematical models and analytical methods for multi-tier mmWave and μWave hybrid caching networks based on stochastic geometry. We propose an association strategy by assuming average biased-received power association and Rayleigh fading. Accordingly, by using a D-ball approximating blockage model of mmWave networks, expressions of the cell association probability and the coverage probability of the hybrid networks are derived. Also, we use the average successful delivery probability as the performance metric to analyze the existing caching placement strategies. Simulation results validate the accuracy of our analytical conclusions.

Metal hydrides are one of the types of functional materials that allow safe and compact storage of a large amount of hydrogen, which is increasingly used today as an alternate fuel or energy source. The possibility of obtaining the initial energy necessary for the production of hydrogen by electrolysis process from renewable energy sources, such as solar panels and wind generators, makes hydrogen energetic quite attractive and rapidly developing industry sector. Solid form of hydrogen storage with the possibility of reversible sorption, gives opportunity for creation autonomous energy storage systems. La-Ni based alloys allow hydrogen storing at ambient temperatures and pressure not higher than 15 bar, which makes the application of these alloys quite practical, interesting and prospects for analysis and modifications on the ways of stored hydrogen capacity increasing, alloys price reducing and application for renewable energy storage.

Suicidal behaviour remains difficult to predict and prevent, even for experienced mental health care professionals. The known distal risk factors for suicidal behaviour are not sufficiently specific to fully understand the complex dynamic processes that precede a suicide attempt. Real-time mobile monitoring data can be used to analyse proximal risk mechanisms within the suicidal process. At the same time smartphone-based safety planning and self-monitoring may enhance a patient’s self-management skills thereby increasing their capacity to respond to a suicidal crisis and to become more aware of crisis symptoms. The current paper describes the theoretical and conceptual rationale for the CASPAR study which applies an innovative approach to the study of suicidal processes. It uses basic science approaches to inform the implementation of an innovative suicide prevention intervention. We aim to develop and implement mobile safety plan in conjunction with real-time monitoring in order to both directly implement suicide prevention interventions and to study the ongoing dynamics of individual suicidal behaviour by applying network analysis.

This article shortly introduces Gaussian processes as a new approach for modelling time series in the field of blazar physics. In the second part of the paper, recent results from an application of GP modelling to the multi-wavelength light curves of the blazar PKS 1502+106 are discussed.

Background: In Transcranial Direct Current Stimulation (tDCS) the injected current gets distributed across the brain areas. The motive is to stimulate the target region-of-interest (ROI), while minimizing the current in non-target ROIs. For this purpose, determining the appropriate current-dose for an individual is difficult. Aim: To introduce Dose-Target-Determination-Index (DTDI) to quantify the focality of tDCS and examine the dose-focality relationship in three different populations. Method: Here, we extended our previous toolbox i-SATA to the MNI reference space. After a tDCS montage is simulated for a current-dose, the i-SATA(MNI) computes the average (over voxels) current density for every region in the brain. DTDI is the ratio of average current density at target ROI to the ROI with maximum value (peak region). Ideally target ROI should be the peak region, so DTDI shall range from 0 to 1. Higher the value, the better the dose. We estimated the variation of DTDI within and across individuals using T1-weighted brain images of 45 males and females distributed equally across three age groups- (a) Young adults (20 ≥ x ˂ 40 years), (b) Mid adults (40 ≥ x ˂ 60 years), and (c) Older adults (60 ≥ x ˂ 80 years). DTDI’s were evaluated for the frontal montage with electrodes at F3 and right supra-orbital for three current doses 1mA, 2mA, and 3mA with the target ROI at left middle frontal gyrus. Result: As the dose is incremented, DTDI may show (a) increase, (b) decrease, and (c) no change across the individuals. The focality decreases with age and the decline is stronger in males. Higher current dose at older age can enhance the focality of stimulation. Conclusion: DTDI provides information on which tDCS current dose will optimize the focality of stimulation. DTDI recommended dose should be prioritised based on the age (> 40 years) and sex (especially males) of an individual. The toolbox i-SATA(MNI) is freely available.

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

The world is currently experiencing the fourth industrial revolution driven by the newest wave of digitisation in the manufacturing sector. The term Industry 4.0 (I4.0) represents at the same time: a paradigm shift in industrial production, a generic designation for sets of strategic initiatives to boost national industries, a technical term to relate to new emerging business assets, processes and services, and a brand to mark a very particular historical and social period. I4.0 is also referred to as Industrie 4.0 the New Industrial France, the Industrial Internet, the Fourth Industrial Revolution and the digital economy. These terms are used interchangeably in this text. The aim of this article is to discuss major developments in this space in relation to the integration of new developments of IoT and cyber physical systems in the digital economy, to better understand cyber risks and economic value and risk impact. The objective of the paper is to map the current evolution and its associated cyber risks for the digital economy sector and to discuss the future developments in the Industrial Internet of Things and Industry 4.0.

(1)Background: Among new anti-angiogenetic agents being developed and everchanging guidelines indications, the question of benefits/safety ratio remains unclear. (2)Methods: we have conducted a systematic review and meta-analysis of 25 randomized controlled trials (15487 patients), evaluating overall survival – OS, progression free survival– PFS and toxicity (grade ≥3 adverse effects, type and number of all adverse effects. (3)Results: analysis showed improvement of pooled-PFS (HR 0.72, 95%CI 0.66–0.78, I2 = 77%, P<0.00001) regardless of treatment settings (first-line - HR 0.83, 95%CI 0.77-0.90, P<0.00001, recurrent cancer – HR 0.61, 95%CI 0.54-0.68, P<0.00001 or maintenance – HR 0.82, 95%CI 0.67-1.00, P=0.04) and type of anti-angiogenetic drug used (VEGF inhibitors, VEGF-R inhibitors or angiopoietin inhibitors). Improved OS was also observed (HR 0.95, 95%CI 0.91–0.99, P=0.02). OS benefits were only observed in recurrent platinum-sensitive or platinum-resistant cancers. Grade≥3 adverse effects were increased across all trials. Anti-angiogenetic therapy increased the risk of hypertension, infection, thromboembolic/ hemorrhagic events, gastro-intestinal perforations but not the risk of wound related issues, anemia or posterior leukoencephalopathy syndrome. (4)Conclusions: Although angiogenesis inhibitors improve PFS, there is little to no OS benefits. Given the high risk of severe adverse reactions a careful selection of patients is required for obtaining the best results possible.

(1) Background: Stereological estimations significantly contributed to our anatomical and physiological understanding of the lung by providing an average distribution of measured parameters over the entire lung parenchyma. However, most structural lung diseases show a very inhomogeneous pattern of alterations. (2) Methods: We developed a novel protocol for the analysis of all pulmonary airspaces. Our pipeline starts with high-resolution synchrotron radiation-based X-ray tomographic microscopy (SRXTM) and consists of (i) image segmentation with the combination of the free machine-learning tool Ilastik and ImageJ and (ii) calculation of the airspace diameter distribution of an entire lung using a diameter map function. To evaluate the new pipeline, lungs from adult mice with cystic fibrosis (CF)-like lung disease (ENaC-transgenic mice) or mice with elastase-induced emphysema were compared to healthy controls. (3) Results: We were able to show the distribution of airspace diameters throughout the entire lung, as well as separately for the conducting airways and the gas-exchange area. In the pathobiological context, we observed an irregular widening of parenchymal airspaces in mice with CF-like lung disease and elastase-induced emphysema. Comparable results were obtained when analyzing lungs imaged with μCT, suggesting that our pipeline is applicable to different kinds of imaging modalities. (4) Conclusions: We conclude that the airspace diameter map is well suited for a detailed analysis of unevenly distributed structural alterations in chronic muco-obstructive lung diseases such as cystic fibrosis and COPD.

This paper develops Bayesian inference in reliability of a class of scale mixtures of log-normal failure time (SMLNFT) models with stochastic (or uncertain) constraint in their reliability measures. The class is comprehensive and includes existing failure time (FT) models (such as log-normal, log-Cauchy, and log-logistic FT models) as well as new models that are robust in terms of heavy-tailed FT observations. Since classical frequency approaches to reliability analysis based on the SMLNFT model with stochastic constraint are intractable, the Bayesian method is pursued utilizing a Markov chain Monte Carlo (MCMC) sampling based approach. This paper introduces a two-stage maximum entropy (MaxEnt) prior, which elicits a priori uncertain constraint and develops Bayesian hierarchical SMLNFT model by using the prior. The paper also proposes an MCMC method for Bayesian inference in the SMLNFT model reliability and calls attention to properties of the MaxEnt prior that are useful for method development. Finally, two data sets are used to illustrate how the proposed methodology works.

The planetary boundary layer height (PBLH) is one of the key factors in influencing the dispersion of the air pollutants in the troposphere and hence the air pollutant concentration on ground level. For this reason, accurate air pollutant concentration depends on the performance of PBLH prediction. Recently, ceilometer, a lidar instrument to measure cloud base height, has been used by atmospheric scientists and air pollution control authorities to determine the mixing level height (MLH) in improving forecasting and understanding the evolution of aerosol layers above ground at a site. In this study, ceilometer data at an urban (Lidcombe) and a rural (Merriwa) location in the New South Wales, Australia was used to validate the PBLH prediction from two air quality models (CCAM-CTM and WRF-CMAQ) as well as to understand the aerosol transport from sources to receptor point at Merriwa for the three case studies where high PM10 concentration was detected in each of the three days. The results show that the PBLH prediction by the two air quality models corresponds reasonably well with observed ceilometer data and the cause and source of high PM10 concentration at Merriwa can be found by using ceilometer MLH data to corroborate with back trajectory analysis of transport of aerosols to the receptor point at Merriwa. Of the three case studies, one had aerosol source from north and north west of Merriwa in remote NSW where windblown dust is the main source, and the other two had sources from south and south east of Merriwa where anthropogenic sources dominate,

Intrinsically disordered proteins mediate crucial biological functions through their interactions with other proteins. Mutual synergistic folding (MSF) occurs when all interacting proteins are disordered, folding into a stable structure in the course of the complex formation. In these cases, the folding and binding processes occur in parallel, lending the resulting structures uniquely heterogeneous features. Currently there are no dedicated classification approaches that would take into account the particular biological and biophysical properties of MSF complexes. Here we present a scalable clustering-based classification scheme, built on redundancy-filtered features that describe the sequence and structure properties of the complexes, and the role of the interaction, which is directly responsible for structure formation. Using this approach, we define six major types of MSF complexes, corresponding to biologically meaningful groups. Hence, the presented method also shows that differences in binding strength, subcellular localization, and regulation are encoded in the sequence and structural properties of proteins. While current structure classification methods can also handle complex structures, we show that the developed scheme is fundamentally different, and since it takes into account defining features of MSF complexes, it serves as a better representation of structures arising through this specific interaction mode.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI–TOF MS) is a commonly used technique for analyzing large biomolecules. However, the utilization of organic matrices limits the small-molecule analysis because of the interferences in the low-mass region and reproducibility issues. To overcome these limitations, a surface-assisted laser desorption/ionization (SALDI), which utilizes nanostructured metallic surfaces, has been developed. Herein, a novel approach for SALDI–MS was proposed using nanoengineered gold shell with nanogaps on the silica core (SiO2@Au NGS), which is an emerging material due to its excellent heat-generating capabilities. The gold shell thickness was controlled by adjusting the concentration of gold precursor for the growth of gold nanoparticles. SALDI-MS measurements were performed on a layer formed by drop-casting a mixture of SiO2@Au NGS and analytes. At the optimized process, the gold shell thickness was observed to be 17.2 nm, which showed the highest absorbance. The ion desorption efficiency was confirmed with a survival yield upon fragmentation. Based on the enhanced SALDI capability, SiO2@Au NGS was utilized to detect various small molecules including amino acids, sugars, and flavonoids. The limits of detection, reproducibility, and salt tolerance of SiO2@Au NGS demonstrate its potential as an effective and reliable SALDI material for small-molecule analyses.

Wind technology is considered to be among the most promising types of renewable energy sources, and due to high oil prices and growing concerns about climate change and energy security, it has been the subject of extensive considerations in recent years, including questions related to the relative sustainability of electricity production when the manufacturing, assembly, transportation and dismantling processes of these facilities are taken into account. The present article evaluates the environmental impacts, carbon emissions and water consumption, derived from the production of electric energy of the Villonaco wind farm, located in Loja-Ecuador, during its entire life cycle, using the Life Cycle Analysis method. Finally, it is concluded that wind energy has greater environmental advantages, since it has lower values of carbon and water footprints than other energy sources. Additionally, with the techniques Cumulative Energy Demand and Energy Return on Investment, sustainability in the production of electricity from wind power in Ecuador is demonstrated; and, that due to issues of vulnerability to climate change, the diversification of its energy mix is essential considering the inclusion of non-conventional renewable sources such as solar or wind, this being the only way to reduce both the carbon footprint and the water supply power.

Recent studies on seafloor mapping have presented different modelling methods for the automatic classification of seafloor sediments. However, most of these studies have applied these models to seafloor data with appropriate number of ground-truth samples, which raises the question whether these methods are applicable to studies with smaller numbers of ground-truth data. In this study, we aim to address this issue by conducting sediment class-specific predictions using ensemble modelling to map areas with limited or without ground-truth data and combined with hydro-acoustic datasets. The resulting class-specific maps were then assembled into one map, where the most probable class was assigned to the appropriate location. Our approach was able to predict sediment classes without bias to the class with more ground-truth data and produced reliable seafloor sediment distributions maps that can be used for seafloor monitoring. Sediment shifts of a heterogenous seafloor in the Sylt Outer Reef, German North Sea were also assessed to understand the sediment dynamics in the area. The analyses of sediment shifts showed that the western area of the Sylt Outer Reef is highly active, and the results of the analyses assisted in providing recommendations on future seafloor monitoring activities.

The analysis of data produced by the MV20/20 sensor, tagged with quality outcomes, is presented with the aim of developing a predictor model for real-time anomaly detection and classification. Three types of inclusions, undesired particles that deteriorate the quality of production, are used to tag the quality data using results from the lab. We explore both unsupervised and supervised learning, which both offer advantages in monitoring and controlling the quality of production. It is discovered that the dataset can be clustered using techniques like K-Means and DBSCAN. Bounding the data within a 95% confidence interval ellipse ensures we can detect anomalous events in real time. For supervised learning, a two-stage classifier is explored, which classifies the outcome of a cast and secondly the inclusion responsible for the negative outcome. We explore models from logistic regression and support vector machines, to two neural networks, namely the multi-layer perceptron and the radial basis function network. While the cast outcome is adequately predicted by all the models, the multi-layer perceptron provides a boundary performance for the inclusion type. A more advanced technique for model optimisation, namely grid search, is applied in order to improve on the results. The outcome for the grid search is not much better, which indicates a global maximum in the learning capacity of the model. Recommendations include the addition of sensor systems and an audit of data collection variation.

Implant-supported-screw-retained prostheses are really popular. Some of the most frequent complications have been mechanical complications. These include abutment screw loosening or even screw fracture. Using an intermediate abutment can offer several advantages. However, little literature has been found regarding how this affects the mechanical behavior of dental restorations. This study focuses on understanding the mechanical behavior of implant-supported restorations with a transepithelial component compared to those with a direct implant-supported restoration. It was carried out by using the Finite Element Method (FEM) and was experi-mentally validated. The results showed that in the case of transepithelial-supported restoration, the prosthetic screw mounted over the transepithelial component suffers higher stress than the one screwed directly to the implant. After applying a cyclic fatigue load, it is experimentally proven that, in the transepithelial-supported restorations, the fuse changes from being the screw that goes to the implant, to being the upper one. As a conclusion we can say that the use of an intermediate abutment in dental restoration not only provides better protection to the rest of the dental restoration but also allows easier repair in the event of a fracture. This can potentially lead to more efficient procedures and improved patient outcomes.

This study investigated the possible usage of beeswax oleogels instead of milk fat as a fat source in ice cream production and konjac gum as a stabilizer instead of salep. For this aim, 12 different ice cream samples were prepared using various fat and oil sources (milk fat and oleogel), stabilizers (salep and konjac gum), and emulsifiers (monoglyceride (MG), palsgaard (PG), and none emulsifier emulsifier-free (NE)). It was determined that the overrun ratio in ice cream samples containing milk fat was higher than in oleogel and the viscosity of the ice cream mix containing palsgaard and oleogel was greater than that of the other treatments (milk fat, MG, and NE). While the first dripping time of the samples with PG and konjac gum was longer compared to the samples without emulsifier (NE) and Monoglyceride (MG), the complete melting times of the samples were close to each other. Whereas the dissolution rate of the samples with salep was higher than that of konjac gum, the fat destabilization ratios of the samples with oleogel and konjac gum were lower than those of milk fat and salep. The fat destabilization ratio of samples containing PG as an emulsifier was statistically higher (p <0.01) than that of samples containing MG and NE. It was found that the unsaturated fatty acid (C18:1, C18:2, and C18:3) content of the samples containing oleogel was significantly higher (p <0.01) than that of the samples containing milk fat. However, butyric and caproic acids, which are aliphatic fatty acids, were found to be deficient in the samples to which oleogel was added instead of milk fat. As to the microscopic appearance, while water and oil particles were not homogeneously dispersed in the ice cream samples with oleogel, they were fully homogeneously dispersed in the milk fat-added ice cream samples. In addition, it was determined that panelists preferred the C samples (milk fat as fat source, salep and PG as emulsifier).Among the samples with added oleogel as oil source, they liked the J sample more (oleogel as fat source,conjak gum and none emulgator). Therefore, the C and J samples can be recommended for consumption

Protein import into the endoplasmic reticulum (ER) is the first step in the biogenesis of about 10,000 different soluble and membrane proteins in humans. It involves co- or post-translational targeting of precursor polypeptides to the ER and their subsequent membrane insertion or translocation. So far, three pathways for ER targeting of precursor polypeptides plus four pathways for ER targeting of mRNAs were described. Typically, these pathways deliver their substrates to the Sec61 polypeptide-conducting channel in the ER membrane. Next, the precursor polypeptides are inserted into the ER membrane or translocated into the ER lumen, which may involve auxiliary translocation components, such as the TRAP and Sec62/Sec63 complexes, or auxiliary membrane protein insertases, such as EMC and the TMCO1 complex. Recently, the PEX19/PEX3-dependent pathway, which has a well-known function in targeting and inserting various peroxisomal membrane proteins into pre-existent peroxisomal membranes, was also found to act in targeting and, putatively, inserting monotopic hairpin proteins into the ER. These either remain in the ER as resident ER membrane proteins or are pinched off from the ER as components of new lipid droplets. Therefore, the question arose if this pathway may play a more general role in ER protein targeting, i.e. represents a fourth pathway for ER targeting of precursor polypeptides. Thus, we addressed the client spectrum of the PEX19/PEX3-dependent pathway in both PEX3-depleted HeLa cells and PEX3-deficient Zellweger patient fibroblasts by an established approach, which involves label-free quantitative mass spectrometry of the total proteome of depleted or deficient cells and differential protein abundance analysis. The negatively affected proteins included twelve peroxisomal proteins and two hairpin proteins of the ER, thus confirming two previously identified classes of putative PEX19/PEX3-clients in human cells. Interestingly, fourteen collagen-related proteins with signal peptides or N-terminal transmembrane helices and belonging to the secretory pathway were also negatively affected by PEX3-deficiency, which may suggest compromised collagen biogenesis as a hitherto unknown contributor to organ failures in the respective Zellweger patients.

In this paper, intrusion detection systems are thoroughly investigated utilizing the CSE-CIC-IDS-2018 dataset. The research is divided into three key phases: first, applying Data Cleaning, Exploratory Data Analysis, and Data Normalization techniques (min-max and z-score) for preparing data across distinct classifiers. Second, feature importance is reduced using a combination of Principal Component Analysis (PCA) and Random Forest (RF), with the goal of improving processing speed and decreasing model complexity. This stage comprises a comparison with the entire dataset. Finally, machine learning algorithms (XGBoost, CART, DT, KNN, MLP, RF, LR, and Bayes) are applied to specific features and preprocessing approaches. Surprisingly, the XGBoost, DT, and RF models outperform in both ROC values and CPU runtime. Following evaluation, which includes PCA and RF feature selection, an optimal set is produced.

In times of water scarcity, water conservation measures from authorities are primarily directed to the residential sector—homeowners and renters who engage in individual activities to reduce water consumption. Business owners, manufacturers, and institutional workers—the ICI sector, generally, do not receive targeted information and education on water savings measures, and when they do, the information, typically, refers to indoor technologies, and behavior choice. In answer to this shortfall, this research illustrates how the ICI sector might engage in outdoor water conservation and, thus, realize water savings using the well-established Pittenger’s Simplified Landscape Irrigation Design Estimation (SLIDE) formula that estimates outdoor water conservation/savings for the lesser studied industrial, commercial, and institutional (ICI) sector. We applied the SLIDE formula five diverse climate areas in Texas to demonstrate the potential water savings in each, as well as identifying the type of water technology measures that yielded the greatest water savings for outdoor ICI landscapes. Overall, the greatest water savings for outdoor ICI landscapes was realized through smartscape design with soil moisture sensors-SMS technology, second best. Our findings will provide ICI facility owners/managers with knowledge and examples of a simplified system for quantitative decision making when considering choices among technologies and/or practices toward outdoor water conservation for their own facilities.

Investigations reporting positive effects of Extracorporeal Shock Wave Therapy (ESWT) on nerve regeneration are limited to the rat sciatic nerve model. The effects of ESWT on muscle-in-vein conduits (MVCs) have also not been investigated yet. This study aimed to evaluate the effects of ESWT after repair of the rat median nerve with either autografts (ANGs) or MVCs. In male Lewis rats, a 7-mm segment of the right median nerve was reconstructed either with an ANG or MVC. For each reconstructive technique, one group of animals received one application of ESWT while the other rats served as controls. Animals were observed for 12 weeks and nerve regeneration was assessed via computerized gait analysis, the grasping test, electrophysiological evaluations and histological quantification of axons, blood vessels and lymphatic vasculature. Here we provide for the first time a comprehensive analysis of ESWT effects on nerve regeneration in a rat model of median nerve injury. Furthermore, this study is among the first reporting the quantification of lymphatic vessels following peripheral nerve injury and reconstruction in vivo. While we found no significant direct positive effects of ESWT on peripheral nerve regeneration, results following nerve repair with MVCs were significantly inferior to those after ANG repair.

This study aimed to explore the feasibility of using airborne acoustic emission in laser beam butt welding for the development of an automated classification system based on neural networks. The focus was on monitoring the formation of joint gaps during the welding process. To simulate various sizes of butt joint gaps, controlled welding experiments were conducted, and the emitted acoustic signals were captured using audible to ultrasonic microphones. To implement an automated monitoring system, a method based on short-time Fourier transformation was developed to extract audio features, and a convolutional neural network architecture with data augmentation was utilized. The results demonstrated that this non-destructive and non-invasive approach was highly effective in detecting joint gap formations, achieving an accuracy of 98%. Furthermore, the system exhibited promising potential for low latency monitoring of the welding process. The classification accuracy for various gap sizes reached up to 90%, providing valuable insights for characterizing and categorizing joint gaps accurately. Additionally, increasing the quantity of training data with quality annotations could potentially improve the classifier model's performance further. This suggests that there is room for future enhancements in the study.

The minimum energy structures of the Si3C5 and Si4C8 clusters are planar and contain planar tetracoordinate carbons (ptCs). These species have been classified, qualitatively, as global () and local () aromatics according to the adaptive natural density partitioning (AdNDP) method, which is an orbital localization method. This work evaluates these species' aromaticity, focusing on confirming and quantifying their global and local aromatic character. For this purpose, we use an orbital localization method based on the partitioning of the molecular space according to the topology of the electronic localization function (LOC-ELF). In addition, the magnetically induced current density is analyzed. The LOC-ELF-based analysis coincides with the AdNDP study (double aromaticity, global and local). Besides, the current density analysis detects global and local ring currents. The strength of the global and local current circuit is significant, involving 4n+2 - and -electrons, respectively. The latter implicates the Si-ptC-Si fragment, which would be related to the 3c-2e -bond detected by the orbital localization methods in this fragment.

We describe a vision for a national-scale heavy mineral (HM) map generated through automated mineralogical identification and quantification of HMs contained in floodplain sediments from large catchments covering most of Australia. The composition of the sediments reflects the dominant rock types in each catchment, with the generally resistant HMs largely preserving the mineralogical fingerprint of their host protoliths through the weathering-transport-deposition cycle. Heavy mineral presence/absence, absolute and relative abundance, and co-occurrence are metrics useful to map, discover and interpret catchment lithotype(s), geodynamic setting, magmatism, metamorphic grade, alteration or mineralization. Underpinning this vision is a pilot project, focusing on a subset of the national sediment sample archive, which is used to demonstrate the feasibility of the larger, national-scale project. We preview a bespoke, cloud-based mineral network analysis (MNA) tool to visualize, explore and discover relationships between HMs as well as between them and geological setting or mineral deposits. We envisage that the Heavy Mineral Map of Australia and MNA tool will contribute significantly to mineral prospectivity analysis and modeling here, particularly for technology critical elements and their host minerals, which are central to the global economy transitioning to a more sustainable, decarbonized paradigm.

AbstractIntroduction: Increasing knowledge of obstetric danger signs (ODS) and encouraging birth preparedness and complication readiness (BPCR) practices are strategies to increase skilled maternal health service utilization in low-income countries. One of the methods to increase mothers' knowledge about ODS and promote BPCR practice is through health education intervention (HEI). However, the effect of context-specific community-based health education led by women's groups on these outcomes has not been comprehensively studied, and the existing evidence is inconclusive. Thus, we aimed to evaluate the effect of a context-specific community-based HEI led by women's groups on mothers' knowledge regarding ODS and BPCR practices in southern Ethiopia.Methods: An open-label, two-arm parallel group cluster-randomized controlled trial was conducted from January to August 2023 on pregnant women from 24 clusters (kebeles) (12 interventions and 12 controls) in the northern zone of the Sidama region. The Open Data Kit smartphone application was utilized to collect data. The intention-to-treat analysis was used to compare outcomes between groups. We fitted multi-level mixed-effects modified Poisson regression with robust standard error to account for between and within cluster effects. Results: One thousand and seventy pregnant women (540 in the intervention and 530 in the control clusters) responded to this study, making the overall response rate 95.02%. Excessive vaginal bleeding (94.3% in the interventional group vs. 88.7% in the control group) was the commonest ODS mentioned during childbirth. Overall, 68.7% of women in the intervention group and 36.2% of mothers in the control group had good knowledge of ODS (P-value < 0.001). Saving money and materials (97.1% in the interventional group vs. 92.7% in the control group) was the most frequently practiced BPCR plan. Overall, 64.3% of women in the intervention group and 38.9% of mothers in the control group practiced BPCR (P-value < 0.001). HEI significantly increased overall knowledge of ODS (adjusted risk ratio [ARR]: 1.71; 99% CI: 1.14-2.57) and improved overall BPCR practice (ARR: 1.55; 99% CI: 1.02-2.39). Conclusions: A community-based HEI led by women's groups improved mothers' knowledge regarding ODS and BPCR practices in a rural setting in southern Ethiopia. Interventions designed to increase women's knowledge of ODS and improve BPCR practice must implement context-specific, community-based HEI that aligns with World Health Organization recommendations. Trial registration: NCT05865873

An abdominal aortic aneurysm (AAA) is usually asymptomatic until rupture, which is associated with extremely high mortality. Consequently, early detection of AAAs is of paramount importance in reducing mortality; however, most AAAs are detected by medical imaging incidentally. The aim of this study was to investigate the feasibility of machine learning-based pulse wave (PW) analysis for the early detection of AAAs using a database of in silico PWs. PWs in the large systemic arteries were simulated using one-dimensional blood flow modelling. A database of in silico PWs representative of subjects (aged 55, 65 and 75 years) with different AAA sizes was created by varying the AAA-related parameters with major impacts on PWs – identified by parameter sensitivity analysis – in an existing database of in silico PWs representative of subjects without AAA. Then, a machine learning architecture for early detection of AAAs was proposed, which was trained and tested using the new in silico PW database. The parameter sensitivity analysis revealed that the AAA maximum diameter and stiffness of the large systemic arteries were the dominant AAA-related biophysical properties that significantly influence the PW. The simulated PW indexes extracted from the database showed that the PW was not only influenced by the presence of an AAA but was also significantly affected by multiple cardiovascular parameters that compromised the detection of AAAs by using individual PW indexes. Alternatively, the trained machine learning model performed well in classifying normal and AAA conditions using digital photoplethysmogram PWs from the database. These findings suggest that machine learning-based PW analysis is a promising approach for AAA screening using PW signals acquired by wearable devices.

At INFN-LNS, and in collaboration with the ATOMKI laboratories, an innovative multi-diagnostic system with advanced analytical methods has been designed and implemented. This is based on several detectors and techniques (Optical Emission Spectroscopy, RF systems, Interfero-polarimetry, X-ray detectors) and here we focus on high resolution spatially-resolved X-ray spectroscopy, performed by means of a X-ray pin-hole camera setup operating in the 0.5−20 keV energy domain. The diagnostic system was installed at a 14 GHz Electron Cyclotron Resonance (ECR) ion source (ATOMKI, Debrecen), enabling high precision X-ray spectrally-resolved imaging of ECR plasmas heated by hundreds of Watts. The achieved spatial and energy resolution were 0.5 mm and 300 eV at 8 keV, respectively. We here present the innovative analysis algorithm that we properly developed for obtaining Single Photon-Counted (SPhC) images providing the local plasma emitted spectrum in a High-Dynamic-Range (HDR) mode, by distinguishing fluorescence lines of the materials of the plasma chamber (Ti, Ta) from plasma (Ar). This method allows a quantitative characterization of warm electrons population in the plasma (and its 2D distribution) which are the most important for ionization, and also to estimate local plasma density and spectral temperatures. The developed post-processing analysis is also able to remove the readout noise, that is often observable at very low exposure times (msec). The setup is now under update including fast shutters and trigger systems in order to allow simultaneously space and time-resolved plasma spectroscopy during transients, stable and turbulent regimes.

Background: The epidemiological situation generated by COVID-19 has cast into sharp relief the delicate balance between public health priorities and the economy, with businesses obliged to toe a line between employee health and continued production. In an effort to detect as many cases as possible, isolate contacts, cut transmission chains and limit the spread of the virus in the workplace, mass testing strategies have been implemented in both public health and industrial contexts to minimize the risk of disruption in activity. Objective: To evaluate the economic impact of mass workplace testing strategy as carried out by a large automotive company in Catalonia in terms of health and healthcare resource savings. Methodology: Analysis of health costs and impacts based on the estimation of mortality and morbidity avoided because of screening and the resulting savings in healthcare costs. Results: The economic impact of the mass workplace testing strategies (using both PCR and RAT tests) was approximately €10.44 per test performed or €5,575.49 per positive detected. 38% of this figure corresponds to savings derived from better use of health resources (hospital beds, ICU beds and follow-up of infected cases), while the remaining 62% corresponds to improved health rates due to avoided morbidity and mortality. In scenarios with higher positivity rates and a greater impact of the infection on health and the use of health resources, these results could be up to ten times higher (€130.24 per test performed or €69,565.59 per positive detected). Conclusion: In the context of COVID-19, preventive actions carried out by the private sector to safeguard industrial production also have concomitant public benefits in the form of savings in healthcare costs. Thus, governmental bodies need to recognize the value of implementing such strategies in private settings and facilitate them through, for example, subsidies.

Footprints are powerful indicators for evaluating the impact of the bioeconomy of a country on environmental goods, domestically and abroad. In this study, we apply a hybrid approach combining a Multi-Regional Input-Output model and land use modelling to compute the agricultural land footprint (aLF). Furthermore, we added information on land-use change to the analysis and allocated land conversion to specific commodities. The German case study shows that the aLF abroad is larger by a factor of 2.5 to 3 than the aLF in Germany. In 2005 and 2010, conversion of natural and semi-natural land-cover types abroad allocated to Germany due to import increases was 2.5 times higher than the global average. Import increases to Germany slowed down in 2015 and 2020, reducing land conversion attributed to the German bioeconomy to the global average. The case study shows that the applied land footprint provides clear and meaningful information for policymakers and other stakeholders. The presented methodological approach can be applied to other countries and regions covered in the underlying database EXIOBASE. It can be adapted, also for an assessment of other ecosystem functions, such as water or soil fertility.

This research focuses on developing a Student Performance Evaluation System to enhance effectiveness and oversight in higher education. The system aims to address challenges faced by instructors in evaluating student performance using a distributed system approach. Agile methodology is employed in development, and database replication techniques are applied for data synchronization. System features include registration, assessment form creation, assessment submission, statistical visualization, and access to assessment history. The system is designed to improve efficiency and transparency in the assessment process. Key features of the system include registration process, assessment form creation, assessment submission, statistical visualization, and access to assessment history. Its goal is to enhance efficiency and transparency throughout the assessment process. The article details the system workflow and the roles played by Admins, Instructors, and students in system usage. Additionally, it discusses the back-end development model and pathways, as well as the performance testing results. Performance testing indicates that the system achieves high throughput of up to 21.55 requests per second, with an average response time of only 34 milliseconds and an error rate of zero percent. These results affirm that the system efficiently handles user loads and nearly instantaneously responds to requests, providing an optimal user experience in the context of student performance evaluation in higher education.

(1) As the craft beer industry continues to expand in Canada, understanding the regional differences between the Eastern and Western markets becomes pivotal for brewing ingredient companies seeking to penetrate or expand within these regions. This study aims to provide a comprehensive review of these two distinctive markets, focusing on economic, logistical, ingredient, and cultural differences. (2) The study primarily relies on secondary data from government, industry reports, and scholarly publications, providing a holistic view of the Canadian craft beer landscape. It further highlights the need to consider market distinctions between Canada and the United States. (3) Findings reveal that Western Canadian breweries, unlike their Eastern counterparts, operate under constrained storage space, prefer locally-sourced ingredients, and offer a distinct variety of beer styles catering to local tastes. The study further uncovers the regulatory and financial complexities of the Canadian market compared to the United States. (4) Based on these findings, this study provides strategic recommendations for brewing ingredient companies, emphasizing domestic logistics, local sourcing, product adaptation, educational support, and regulatory navigation, fostering success in the Canadian craft beer landscape.

The (benzylthio)acetic acid (HBTA) and some aminopyrimidines, namely 2-aminopyrimidine (2-AP), 5-aminopyrimidine (5-AP), 2-amino-4,6-dimethylpyrimidine (2-A-4,6-DMP) and 2,4,6-triaminopyrimidine (2,4,6-TAP), were successfully embodied as structural units into the construction of a total of four novel supramolecular organic frameworks. The received crystalline solids were inspected by single crystal X-ray diffraction (SC XRD) in order to obtain insight into the structural and supramolecular facets. The SOFs deriving from 2-AP, 5-AP as well as 2-A-4,6-DMP crystallize in the form of true co-crystals (1-3), while the one originating from 2,4,6-TAP crystallizes as salt solvate (4). The SC XRD results indicated the different contents of structural residues present in the asymmetric units of the crystals 1-4 despite the using the same molar ratio of molecular co-formers in each case. The molecular structures of co-crystals 1-3 consist of either one neutral residue of each starting components (1 and 3) or a half nonionized residue of aminopyrimidine ingredient and one neutral residue of acidic specie (2). The asymmetric unit of salt solvate 4 is composed of two ionized residues of each co-former (two 2,4,6-TAP+ cations and two BTA- anions) and one MeOH solvent molecule. The most extensive H-bonding network is observed in the crystal structure of salt solvate 4. The relevant molecular ingredients in co-crystals 1-3 are mainly hold together by the neutral Ocarboxylic–H···Npyrimidine and Namine–H···Ocarboxylic H-bonds. In the case of aggregate 4, the corresponding ionic residues are predominantly sustained by the charged-assisted Npyrimidinium–H···Ocarboxylate and Namine–H···Ocarboxylate hydrogen interactions. The MeOH solvent incorporated into the crystal lattice of adduct 4 is also involved in H-bonding by simultaneously serving as the single donor in OMeOH–H···S and the single acceptor in Namine–H···OMeOH H-bonds afforded the structural diversity within the 2,4,6-TAP+ cations and BTA- anions. Other weaker sets of additional non-covalent contacts existing in the crystal structures of analyzed conglomerates are involved in the self-assembly, stabilization and expansion of total supramolecular organic frameworks. The fact of the formation of non-covalent bonded supramolecular organic frameworks in question is also reflected in corresponding results obtained through elemental analysis (EA), Fourier transform infrared spectroscopy (FT−IR) and thermal analysis (TG/DSC).

Poly(hydroxyalkanoates) (PHAs) are currently considered competent candidates to replace traditional plastics in several market sectors. However, commercial PHA grades exhibit unsatisfactory smell that can negatively affect the quality of the final product. The cause of this typical rancid odour is attributed to oxidized cell membrane glycolipids, coming from Gram negative production strains, which remain frequently attached to PHAs granules after extraction. The aim of this research is the development of customised PHA nano-biocomposites for industrial applications containing organo-modified nanoclays with high adsorption properties able to capture volatile compounds responsible of the displeasing fragrance in PHAs. To this end, a methodology for the detection and identification of the key volatiles released due to oxidative degradation of PHAs has been established using a headspace solid-phase microextraction technique. We report the development of nine nano-biocomposite materials based on three types of commercial PHA matrices loaded with three species of nanoclays which represent a different polar behaviour. It has been demonstrated that although the reached outcome effect depends on the volatile nature, natural sepiolite (T2) might result in the most versatile candidate for all PHA matrices selected.

The importance of conflict analysis and safety considerations during work zone intervals at signalized intersections cannot be overstated, as these periods pose unique challenges and safety risks for both motorists and pedestrians. Work zones introduce temporary changes to traffic patterns, lane configurations, and signal timings, which can disrupt the flow of traffic and increase the likelihood of conflicts between vehicles and pedestrians. By conducting comprehensive conflict analysis, it is possible to identify potential conflict points, such as merging areas, pedestrian crossings, and lane transitions, and implement targeted safety measures to mitigate risks. Additionally, considering safety considerations during work zone intervals is paramount to safeguarding the well-being of road users and minimizing the occurrence of conflicts or collisions. Measures such as enhanced signage, temporary traffic control devices, and increased enforcement efforts can help promote compliance with traffic regulations and ensure the safe passage of vehicles and pedestrians through work zones. This study investigates the dynamics of traffic behavior and safety implications within work zones at signalized intersections, aiming to address key challenges and enhance intersection management strategies. Through comprehensive analysis, varying frequencies of Vehicle-to-Vehicle (V2V) conflicts were observed across different movement directions, with significant occurrences identified in specific phases of the traffic signal cycle. Specifically, 69 V2V conflicts in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8 were recorded. Furthermore, the examination of Vehicle-to-Pedestrian (V2P) conflicts underscored the importance of prioritizing pedestrian safety, with notable occurrences observed in phases associated with pedestrian crossing movements. Meanwhile, despite the closure of left-turn movements, instances of red light running persisted, with 69 red light running events in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8.To mitigate these safety concerns during work zone intervals, proactive measures are essential. Strategies include improving communication through clear signage and advanced warning systems, enhancing enforcement efforts with red light cameras and increased police presence, and investing in infrastructure improvements such as temporary pedestrian facilities. Integration of real-time data from sensors, particularly LiDAR technology, offers significant advantages in detecting conflicts and red light runners during work zone intervals. LiDAR's precise, real-time data on vehicle and pedestrian movements, combined with its 360-degree coverage and high spatial resolution, facilitates accurate detection and tracking of potential conflict scenarios, contributing to enhanced intersection safety and traffic management efficiency. This study endeavored to conduct a traffic safety analysis during work zone intervals at a signalized intersection in Baltimore City, known for its high rate of traffic crashes, utilizing LiDAR sensor technology.

Transmembrane carriers of the Slc11 family catalyze proton (H+)-dependent uptake of divalent metal-ions (Me2+) such as manganese and iron - vital elements coveted during infection. Slc11 mechanism of high affinity Me2+ cell import is selective and conserved between prokaryotic (MntH) and eukaryotic (Nramp) homologs, though processes coupling the use of the proton motive force to Me2+ uptake evolved repeatedly. Adding bacterial piracy of Nramp genes spread in distinct environmental niches suggests selective gain of function that may benefit opportunistic pathogens. To better understand Slc11 evolution Alphafold (AF2)/Colabfold (CF) 3D predictions for bacterial sequences from sister clades of eukaryotic descent (MCb and MCg) were compared using both native and mutant templates. AF2/CF model an array of native MCb intermediates spanning the transition from outward open (OO) to inward open (IO) carrier. In silico mutagenesis targeting i) a set of (evolutionary coupled) sites that may define Slc11 function (putative synapomorphy), and ii) residues from networked communities evolving during MCb transition, indicate Slc11 synapomorphy primarily instructs Me2+-selective conformation switch which unlocks carrier inner gate, and contributes to Me2+ binding site occlusion and outer gate locking. Inner gate opening apparently proceeds from interaction between transmembrane helix (h) h5, h8 and h1a. MCg1 xenologs revealed marked differences in carrier shape and plasticity, owing partly to altered intramolecular H+-network. Yet, targeting Slc11 synapomorphy also converted MCg1 IO models to OO state, apparently mobilizing the same residues to control gates. But MCg1 response to mutagenesis differed, extensive divergence within this clade correlating with MCb-like modeling properties. Notably, MCg1 divergent epistasis marks emergence of the genus Bordetella-Achromobacter. Slc11 synapomorphy localizes to the 3D areas that deviate least among MCb and MCg1 models (either IO or OO) implying it constitutes a 3D network of residues articulating Me2+-selective carrier conformation switch which is maintained in fast evolving clades at the cost of divergent epistatic interactions impacting carrier shape and dynamics.

This paper examines the importance of choice of cooking fuels in wellbeing, analysing relationships between the proportion of population with primary reliance on different types of fuels for cooking (predictor variable), and key wellbeing indices (outcome variables) - Personal Health, Social Life, Civic Engagement, Life Evaluation, Negative Experience. By combining two global datasets from Gallup and WHO, the study adds to current evidence by taking a global perspective. Controlling for demographic factors such as income per capita, age, education level, employment, etc., regression modelling of these relationships show that clean cooking fuels are influential in all of the key wellbeing indices with the exception of the Life Evaluation Index. Among the key wellbeing indices, Personal Health and Negative Experience Indices are the most strongly influenced by choice of clean cooking fuels. By adding access to electricity as an additional predictor variable, the analysis highlights the potential for integrating eCooking into national electrification plans as part of sustainable energy transitions, given that health outcomes (Personal health and Negative experience indices) appear to be as closely linked to choice of cooking fuels as to access to electricity.

Cancer is a condition in which the body's cells proliferate unchecked. Skin cancer is one of the deadliest diseases that impacts the skin on many levels. There are several different types of the disease, including melanoma, basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevus. Due to its increased prevalence, skin cancer, in particular melanoma, is be-coming a serious health problem. Early identification of skin lesions is crucial for successful treatment. Due to their resemblance, many skin lesions are misclassified, which is a severe issue. Researchers seek computer-aided diagnostic tools for early malignant tumor detection. First, a new model based on the combination of "you only look once" (YOLOv5) and "ResNet50" is proposed for melanoma detection with its degree using humans against a machine with 10,000 training images (HAM10000). Second, feature maps integrate gradient change, which allows rapid inference, boosts precision, and reduces the number of hyperparameters in the model, making it smaller. Finally, to get the desired outcomes, the current YOLOv5 model is changed by adding new classes for dermatoscopic images of typical lesions with pigmented skin. The pro-posed approach improves melanoma detection with a real-time speed of 0.4 ms non-maximum suppression (NMS) per image. The performance metrics average is 99.0%, 98.6%, 98.8%, 99.5, 98.3%, and 98.7% for precision, recall, dice similarity coefficient (DSC), accuracy, mean average precision (MAP) from 0.0 to 0.5, and MAP from 0.5 to 0.95, respectively. Compared to cur-rent melanoma detection approaches, the provided approach is more efficient using deep fea-tures.

Floating Offshore Wind Turbine (FOWT) is an innovative technology with little industry guidance for its hull design. Various FOWT floaters with different hull shapes claim to support the same turbines. Structural integrity and material expense analyses of different pontoon shapes were conducted, and it was found that some configurations, such as those with every two columns connected by both pontoon and bracing, have advantages over others. However, it is important to note that the choice of pontoon shape should be based on the wave loading conditions the floater will be exposed to. While a T-shaped pontoon provides a cost-effective solution under certain wave loading scenarios, it may not be the best option for all conditions. Specifically, ring pontoon designs with full bracing were found to be necessary for withstanding certain wave loads. Therefore, it is important to consider different Dominant Load Parameters (DLP) and ensure that a FOWT floater can withstand all applicable DLPs. An uneven hexahedral column shape, which combines the best attributes of square and round shapes, is proposed as a better alternative to cylindrical columns. It offers ease of manufacture and reasonably low drag. Bracing is found to be necessary for withstanding the wind turbine’s incurred moment and forces. The conclusion is that platform design should prioritize manufacturing costs and strength over maximizing hydrodynamic performance.

The purpose of this study is to apply the convergence-confinement method (CCM) based on the assumption of hydrostatic stress to the surrounding rock excavated in a circular tunnel whose rock mass characteristics meet the requirements of the non-linear Hoek-Brown failure criterion. Based on a consistent and rigorous method of theoretical analysis, a closed-form analytical solution for the stress/displacement of the ground reaction induced by the advancing excavation of the tunnel, especially in the elastic and plastic regions, is derived and investigated under the condition of an isotropic stress state. By taking the value of a confinement loss as an explicit analysis process in an incremental manner, not only is the computationally executable analytical solution achieved, but it can also be performed using a simple spreadsheet. The validity of the analytical solution implemented by the incremental procedure was examined with published data to study the effect of nonlinear failure criteria on the ground reaction curve; in particular the mechanical behavior at the intrados of the tunnel, and the distribution of stress/displacement on the cross-section of the tunnel. A comparison between the published results and the solution proposed in this study, which interprets the closed-form by the incremental procedure, shows consistent and favorable trends.

Centrifugal pumps are widely employed in the oil refinery industry due to their efficiency and effectiveness in fluid transfer applications. The reliability of pumps plays a pivotal role in ensuring uninterrupted plant productivity and safe operations. Analysis of failure history data shows that bearings have been identified as critical components in oil refinery pump groups. However, traditional reliability estimation theories may not apply when data is limited or subject to right censoring. This paper addresses the complexity of estimating the Weibull distribution parameters using the maximum-likelihood method under the abovementioned conditions. The likelihood equation lacks an explicit analytical solution, necessitating the use of numerical methods for resolution. The proposed approach presented in this article leverages the Expectation-Maximization (EM) algorithm for estimating the Weibull distribution parameters. This method provides more accurate estimates of failure rates and probabilities by accounting for limited and censored data. The findings are demonstrated through a case study, showcasing the practical application of the proposed approach.

In this work, we developed a highly sensitive multiplexed immunosensor for direct simultaneous detection of both N-terminal Natriuretic Peptide (NT-proBNP) and Cortisol in human artificial saliva (AS). The developed biosensor platform based on silicon nitride was composed from four gold working microelectrodes (WEs) and an integrated counter and reference electrode. Gold WEs were biofunctionalized through carboxyl diazonium (4-APA) to immobilize both anti- NT-proBNP and anti-Cortisol antibodies for simultaneous detection. The electroaddressing of the 4-APA onto the gold WE surfaces was realized by cyclic voltammetry (CV), while the interaction between antibodies and antigens in PBS was monitored by electrochemical impedance spectroscopy (EIS). The antigen detection in human AS was realized by EIS combined with the standard addition method. The immunesensor was highly sensitive and selective toward the corresponding biomarkers in both PBS and artificial human saliva as well as in the presence of other potential interfering biomarkers such as tumor necrosis factor alpha (TNF-α) and interleukin-10 (IL-10). The limit of detection (LOD) was at 0.2pg/mL for NT-proBNP within the range 0.03 to 0.9pg/mL, while the LOD for Cortisol was 0.06 ng/mL within the range 0.02 to 0.6 ng/mL for Cortisol in artificial saliva. The developed immunosensor is very promising for significant detection time reducing as it allows the simultaneous detection of various biomarkers.

In this work, we offer a novel and accurate method in order to find the solution of the linear differential equations over the intervals [0, 1) based on the generalization of Legendre wavelets. The mechanism is still upon workable implementation of the operational matrix of integration and its derivatives. This method reduces the problems into algebraic equations via the properties of generalized Legendre wavelet (GLW) together with the operational matrix of integration. The function approximation has been picked out in such a way so as to enumerate the connection coefficients in an facile manner. The proposed numerical technique, based on the GLW, has been examined on three linear problems as a consequence of this investigation. The outcomes have shown that this method, as opposed to some other existing numerical and analytical methods, is a very useful and advantageous for tackling such problems.

Developing detailed Finite Element Models related to aerospace, automobile, civil, material science etc could be a very challenging task, especially generating a mesh that could produce accurate results. This review article provides an overview of different types of structural mechanics problems which are of interest to researchers and recent developments in meshing methodology related to each type of problems. The article also describes an automatic 2D mesh generation framework leverag- ing the capabilities of commercial FEA software MSC.PATRAN

Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware is gaining increased attention due to its ability to expose plaintext passwords or key encryption files. This paper presents an enhanced classification model based on One class SVM (OCSVM) classifier that can identify any deviation from the normal memory dump file patterns and detect it as malware. The proposed model integrates OCSVM and Principal Component Analysis (PCA) for increased model sensitivity and efficiency. An up-to-date dataset known as “MALMEMANALYSIS-2022” was utilized during the evaluation phase of this study. The accuracy achieved by the traditional one-class classification (TOCC) model was 55%, compared to 99.4% in the one-class classification with PCA (OCC-PCA) model. Such results have confirmed the increased performance achieved by the proposed model.

A method is developed to identify molecular scaffolds potentially active against the Mycobacterium tuberculosis complex (MTBC). A structurally heterogeneous set of compounds active against MTBC was used to obtain a structural pattern model based on structural invariants. This model was statistically validated through a Leave-n-Out test. It successfully discriminated between active or inactive compounds over 86% in database sets and was also able to select new active chemical structures in external databases. The selection of new substituted pyrimidines, pyrimidones and triazolo[1,5-a]pyrimidines was particularly interesting because these structures could provide new scaffolds in this field. The seven selected candidates were synthesized and six of them showed activity in vitro.

The importance of measuring sustainability in tourism has been significantly advancing in recent years, following the need to manage the impact of tourism on territories and hosting communities. It was further boosted by the pandemic, where sustainability has been defined as one of the central elements to restart global tourism. The ETIS model, developed by the European Commission, is a point of reference based on self-assessment, data collection and analysis by the destinations themselves. The application of ETIS toolkit has faced many challenges, especially at sub-national level, mostly related to the lack of available and updated data to feed the model. The hypothesis explored by the authors is to solve the implementation issues, developing an indicator based on the use of the Sentiment Analysis to frame e-reputation and tourism satisfaction, and further combining it with other open data sources. The Tourism Sustainability Index (TSI) can provide a scalable and geo-referenced evaluation of tourism sustainability, measuring the four pillars and sub-components referenced to ETIS criteria, applicable to any tourism destination. Results show that the TSI can be seen as a consistent and valid tool for destinations to analyze sustainability, monitor its evolution through time periods and sub-areas, and compare it to other benchmark or competitive areas.

Ombrian curves, i.e. curves linking rainfall intensity to return period and time-scale, are well-established engineering tools, crucial to the design against storm waters and floods. Whereas at-site construction of such curves is considered a standard hydrological task, it is a rather challenging one when large regions are of interest. Regional modelling of ombrian curves is particularly complex due to the need to account for spatial dependence together with the increased variability of rainfall extremes in space. We develop a framework for the parsimonious modelling of the extreme rainfall properties at any point in a given area. This is achieved by assuming a common ombrian model structure except for a spatially varying scale parameter which is itself modelled by a spatial smoothing model for the 24 h average rainfall maxima that employs elevation as an additional explanatory variable. The fitting is performed on the pooled all-stations data using an advanced estimation procedure (K-moments) that allows both for reliable high-order moment estimation and simultaneous handling of space-dependence bias. The methodology is applied in the Thessaly region, a 13 700 km2 water district of Greece characterized by varying topography and hydrometeorological properties.

Precision medicine emphasizes fine-grained diagnostics, taking individual variability into account to enhance treatment effectiveness. Parkinson's Disease (PD) heterogeneity among individuals is a proof that disease subtypes exist, and assigning individuals to subgroups is necessary for a better understanding of disease mechanisms and designing precise treatment approaches. The purpose of this study was to identify PD subtypes using RNA-Seq data in a combined pipeline including unsupervised machine learning, bioinformatics, and network analysis. 210 post mortem brain RNA-Seq samples from PD (n = 115) and Normal Controls (NC, n = 95) were obtained with a systematic data retrieval following PRISMA statements and a fully data-driven clustering pipeline was performed to identify PD subtypes. Bioinformatics and Network analyses were performed to characterize the disease mechanisms of the identified PD subtypes and to identify target genes for drug repurposing. Two PD clusters were identified and 42 DEGs were found (p.adjusted ≤ 0.01). PD clusters had significantly different gene network structures (p &lt; 0.0001) and phenotype-specific disease mechanisms, highlighting the differential involvement of the Wnt/β-catenin pathway regulating adult neurogenesis. NEUROD1 was identified as a key regulator of gene networks and ISX9 and PD98059 were identified as NEUROD1-interacting compounds with disease-modifying potential, reducing the effects of dopaminergic neurodegeneration. This hybrid data analysis approach could enable precision medicine applications by providing insights for the identification and characterization of pathological subtypes. This workflow has proven useful on PD brain RNA-Seq, but its application to other neurodegenerative diseases is encouraged.

There are multiple factors determined causing the land subsidence (e.g. man-made and natural-climate change) which have impact on the urban built environment economic spectrum e.g. buildings, properties, infrastructures and land. This paper presents the cause-effect investigation of the causing factors which influence the direct-indirect impacting urban economic factor via multi-regression analysis using Shanghai megacity as case study. Factors are selected based on existing UUS-subsidence-economic impact (USEM) framework as well as modification and adaptation from Shanghai Masterplan 2017-2035 (SM 2035) and Sustainable Development Goals (SDGs) 2030. Data are gathered secondarily via open sources e.g. scientific journal articles and reports. The results are parallel to previous studies on the current trend for rapid and unconscious UUS exploration development including tunneling seepage and leakage as leading causes for further land subsidence in Shanghai. A further concrete multi-integrated macro-scale USEM’s awareness and knowledge is needed to avoid future costlier damage. The highly regressed causing factors include increasing population, UUS-induced subsidence, underground tunnel leakage, cumulative UUS development and subsidence whereas building prices, reconstruction area ratio, land price, green buildings, tunnel settlement, loss of arable land, number of death and government revenue are the among the most impacted. Officials in Shanghai may further consider results for future USEM masterplans to prevent further unsustainability. It is also found that developing megacity may possess different factors according to their distinct condition.

Given that the outcome of hepatic arterial infusion chemotherapy (HAIC) with cisplatin for intrahepatic advanced hepatocellular carcinoma (HCC) is unclear, we aimed to compare prognostic factors for overall survival (OS) following HAIC with cisplatin versus sorafenib for intrahepatic advanced HCC using propensity score-matched analysis. We enrolled 348 patients with intrahepatic advanced HCC who received HAIC with cisplatin (n = 97) or sorafenib (n = 251) between June 2006 and March 2020. No significant difference was observed in OS between HAIC with cisplatin and sorafenib cohorts (median survival time [MST]: 13.9 vs. 12.7 months; p = 0.0989). To reduce confounding effects, 176 patients were selected using propensity score-matched analysis (n = 88 for each treatment). HAIC with cisplatin significantly prolonged OS compared with sorafenib (MST: 16.2 vs. 12.2 months; p = 0.0060). Following stratification according to the Child–Pugh classification, for both patients with class A (MST: 24.0 vs. 15.6 months; p = 0.0097) and class B (MST: 8.5 vs. 6.9 months; p = 0.0391), HAIC with cisplatin rather than sorafenib significantly prolonged OS. Our findings suggest that HAIC with cisplatin demonstrates longer prognostic effects than sorafenib in intrahepatic advanced HCC, regardless of the hepatic reserve.

Abstract: Coal bed methane (CBM) reservoirs are complex systems whose properties differ from those of conventional reservoirs. Coal seams are dual-porosity systems that comprise the porosities of the matrix and cleat system. Gas in the coal seams can be stored as free gas in the cleat system and as adsorbed gas in the porous medium. The flow mechanisms of the natural gas through the formation include desorption, diffusion, and Darcy’s flow regimes. The permeability of CBM reservoirs is more sensitive to pressure variations than conventional gas reservoirs. To study the flow behavior of CBM reservoirs it is mandatory to use a model that considers their unique characteristics. The objective of this study was to propose a physical and mathematical model of production performance for horizontal wells in CBM reservoirs whose permeability is dependent on pressure. A solution for the model was obtained by applying Pedrosa´s transformation, perturbation theory, Laplace transformation, the point source method, and Sthefest´s algorithm. The solution to this problem was validated with previous work thoroughly. The type curves of the model were built and the pressure transient behavior of the model was analyzed and discussed. The effects of several parameters on pressure behavior were also discussed.

The energy sector plays an important role in Mexico’s development trajectory. Mexico makes an interesting case study, because it shows how difficult it is to reduce fossil energy dependence despite geographic and climatic conditions that favour renewable energy deployment and use. Resolving path dependencies and the related carbon lock-in are key to Mexico’s sustainable energy transition. This case study aims to identify and discuss how carbon lock-in affects Mexico’s sustainable energy transition. Mexico’s carbon lock-in involves oil and oil-run power plants that are costly to build but relatively inexpensive to operate. This case study identifies potential entry points for transitioning towards sustainable energy in Mexico – resources that can promote the use of clean energy despite carbon lock-in. For example, focusing on electrification – particularly of the carbon-intensive sectors – can help Mexico transit towards sustainable energy despite institutional constraints. Complementing this case study is a teaching guide with recommendations for using Mexico’s energy transition in courses on sustainability. It introduces a “learning activation framework” to identify emerging opportunities that can advance sustainable energy transitions in different cases of carbon lock-in. Finally, the framework also gives students a chance to help dismantle or cope with carbon lock-ins.

The changing of land use and land cover (LULC) are both affected by climate and human activity and affect climate, biological diversity, and human well-being. Accurate and timely information about the LULC pattern and change is crucial for land management decision-making, ecosystem monitoring, and urban planning, especially in developing economies undergoing industrialization, urbanization, and globalization. Biodiversity degradation and urban expansion in eastern China are research hot-spots. However, the influence of LULC changes on the region remains largely unexplored. Here, an object-based and multi-temporal image analysis approach was developed to detect how LULC changes during 1985-2015 in the Tiaoxi watershed (Zhejiang province, eastern China) using Landsat TM and OLI data. The main objective of this study is to improve the accuracy of unsupervised change detection from object-based and multi-temporal images. To this end, a total of seven LULC maps are generated with multi-temporal images. A random stratified sample design was used for assessing change detection accuracy. The proposed method achieved an overall accuracy of 91.86%, 92.14%, 92.00%, and 93.86% for 2000, 2005, 2010, and 2015, respectively. Nevertheless, the proposed method, in conjunction with object-oriented and multi-temporal satellite images, offers a robust and flexible approach to LULC changes mapping that helps with emergency response and government management. Urbanization and agriculture efficiency are the main reasons for LULC changes in the region. We anticipate that this freely available data will improve the modeling for surface forcing, provide evidence of changes in LULC, and inform water-management decision-making.

Advancements in deep learning and availability of medical imaging data have led to use of CNN based architectures in disease diagnostic assisted systems. In spite of the abundant use of reverse transcription-polymerase chain reaction (RT-PCR) based tests in COVID-19 diagnosis, CT images offer an applicable supplement with its high sensitivity rates. Here, we study classification of COVID-19 pneumonia (CP) and non-COVID-19 pneumonia (NCP) in chest CT scans using efficient deep learning methods to be readily implemented by any hospital. We report our deep network framework design that encompasses Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory (biLSTM) architectures. Our study achieved high specificity (CP: 98.3%, NCP: 96.2% Healthy: 89.3%) and high sensitivity (CP: 84.0%, NCP: 93.9% Healthy: 94.9%) in classifying COVID-19 pneumonia, non-COVID-19 pneumonia and healthy patients. Next, we provide visual explanations for the CNN predictions with gradient-weighted class activation mapping (Grad-CAM). The results provided a model explainability by showing that Ground Glass Opacities (GGO), indicators of COVID-19 pneumonia disease, were captured by our CNN network. Finally, we have implemented our approach in three hospitals proving its compatibility and efficiency.