Understanding the relationship between urban morphology and sustainability can inform strategies for facing the contemporary challenges of cities since urban fabrics embed information regarding the performance of different urban systems and their spatial heterogeneity. This paper assesses the variability of urban sustainability indicators for urban fabric typologies in Cuenca, Ecuador. We assessed 30 sustainability indicators for each cell in a hexagonal regular grid covering the study area and explored their spatial patterns and relationships with urban fabric typologies through spatial and statistical analysis. Results show that indicators vary significantly among urban fabrics, while built environment indicators dominate overall sustainability levels. A marked spatial heterogeneity is revealed by high sustainability for inner-core areas but much lower sustainability for fringe expansion areas – although transition areas are also identified. Spatial clusters of urban fabrics are found to be homogeneous in terms of sustainability only for urban fabrics with high/low sustainability levels, while heterogenous for fabrics in the mid-range levels. The quantitative evidence on the relation between urban morphology and sustainability unveils otherwise hidden patterns, which can support and enhance evidence-based decision-making for urban planning. Open-source data and tools developed allow full replication for further research in similar urban areas.

Waste glycerol can be subjected to various processing operations, including purification and re-fining, to obtain glycerol of appropriate purity. In addition, alternative methods of utilising waste glycerol are also being sought, e.g. by converting it into other valuable chemical products or biofuels. Therefore, various technologies are being developed to utilise this type of waste effectively and sustainably. The production of value-added products from waste glycerol is an important issue that determines the improvement of the economic viability of biofuel production and corresponds to the model of a waste-free and emission-free circular economy. This paper characterised the mechanisms and evaluated the efficiency of existing methods of microbiological utilisation of waste glycerol into liquid biofuels, including biodiesel, bioethanol and biobutanol, and identified further directions for the production of value-added products. In addition, a bibliographical analysis of publications re-lated to the production of liquid fuels and economically valuable products from glycerol was carried out, the progress of research and application work was assessed and directions for future research were identified.

A method is proposed for analyzing the tremor of the earth's surface, measured by GPS, in order to highlight prognostic effects. The method is applied to analysis of daily time series of vertical displacements in Japan. The network of 1047 stations is divided into 15 clusters. The Huang Empirical Mode Decomposition (EMD) is applied to the time series of principal components from clusters with subsequent calculation of instantaneous amplitudes using the Hilbert transform. To ensure the stability of estimates of the waveforms of the EMD decomposition, 1000 independent additive realizations of white noise of limited amplitude were averaged before the Hilbert transform. Using a parametric model of the intensities of point processes, we analyze the connections between the instants of sequences of times of the largest local maxima of instantaneous amplitudes averaged over the number of clusters and the times of earthquakes in the vicinity of the Japan with minimum magnitude thresholds of 5.5 for the time interval 2012-2023. It is shown that part of the intensity of the sequence of times of seismic events, due to the influence of the times of the most expressive local extrema of instantaneous amplitudes, significantly exceeds the reverse influence.

Jump rope is a widely-applied basic training in various sports, yet it is understudied biomechanically. This study investigates the impact of cycle-tempo-induced motor control changes in elite jump rope athletes, addressing the biomechanical gap of cyclic skill control. The hypothesis posited two accelerations per jump cycle—one in front of and one behind the body—and anticipated that increased cycle frequency would alter the distribution of acceleration time within a cycle. Using 3D motion analysis, kinematic parameters were obtained and analyzed. The results confirmed the presence of two distinct accelerations per cycle. As tempo increased, the percentage of rear acceleration time increased while front acceleration time decreased, along with peak velocities increasing significantly (p<0.01). Rope trajectory analysis indicated a consistent movement pattern across tempos, primarily in the sagittal plane. Variations in skill control showed shorter contact phases and reduced vertical range of motion for the center of gravity and feet at higher tempos (p<0.05), along with significant reductions in joint range of motion for the lower limbs (p<0.01). These findings enhance the understanding of motor control adaptations to different tempos and have practical implications for developing coaching programs aimed at optimizing performance, stability, and efficiency in jump rope training.

Natural risks encompass a range of disasters and dangers, requiring comprehensive management through assessments, anticipations, and advanced alert systems. Our specific focus is on landslides in challenging terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum approach, integrating qualitative parameters. Despite the challenges of the rugged terrain in Northern Algeria, it paradoxically hosts a dense population attracted by valuable hydro-agricultural resources. Our research is dedicated to studying landslide risks in these areas, particularly in the Mila region, with the aim of constructing a statistical model integrating considerations of both danger and vulnerability. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. The result is a valuable tool for effective risk management, strengthening safety and resilience against natural disasters. By understanding the intricate interplay of geological, environmental, and social elements, our research can contribute to the development of targeted interventions, fostering a more resilient community in the face of natural adversities.

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

In order to tackle the issue of severe rocking phenomenon under seismic conditions in structures equipped with steel spring isolation bearings, this paper investigates a novel type of anti-rocking bearing. Firstly, the structural configuration and working principle of the novel anti-rocking bearing are introduced, and a design method for bearing parameters is proposed. Secondly, a finite element analysis model is established using SAP2000 software to conduct nonlinear dynamic time history analysis under seismic loading. The analysis results show that the structural arrangement of the novel anti-rocking bearing reduces both the vertical displacement difference and the rocking angle of the isolation layer. The bearing exhibits a certain level of anti-rocking effect, but it may cause significant tensile forces in some bearings, necessitating the installation of anti-tensile reinforcement. The effectiveness of the anti-rocking effect improves as the stiffness of the steel tension rod in the bearing increases; however, it also leads to higher maximum tension in individual bearings as well as the total tension in all bearings. For structures equipped with the novel anti-rocking bearing, the acceleration amplifies under most cases, with amplification coefficients ranging from 0.82 to 1.55. The performance of the novel anti-rocking bearing varies significantly for different seismic waves. The stiffness of the steel tension rod should be determined considering the reference pressure of the bearing and the maximum designed tension of the bearing. Through the finite element simulation of the bearing, the mechanical properties of the bearing are basically the same as the theoretical analysis results. For the cylinder and the joint bearing, certain structural measures need to be taken to ensure its strength and stiffness.

The effects of environmental media on the corrosion fatigue fracture behavior of 25CrMo steel were investigated. The media include air, and a 3.5 wt.% and a 5.0 wt.% NaCl solutions. Experimental results indicate that the media induces the initiation of corrosion fatigue cracks at multiple sites. The multi-cracking sites cause the changes in the crack growth directions, the crack growth rate during the coupling action of the media and the stress amplitude. The coupling effects are important for engineering applications and research. The probability and predictions of the corrosion fatigue characteristic life can be estimated using the 3-parameter Weibull distribution function.

The geological and hydrological conditions of tunnels near reservoirs are complex, and the impact of water level changes on the stability and leakage assessment of the lining structure is not considered in the current leakage evaluation. In order to construct an evaluation model for leakage level of tunnels near reservoirs, the influence of water level changes on tunnel stability and changes in environmental conditions on the leakage of tunnels were researched. Based on the AHP and extensibility theory, a hierarchical system for leakage assessment, incorporating values from nine indexes representing three aspects: geological conditions, hydrological conditions, and tunnel engineering. Numerical simulation was used to analyze the influence of water level changes. It was found that the water level change index greatly influences the displacement and stress distribution inside the tunnel structure. The leakage evaluation model was applied to the Tiebeishan Tunnel, resulting in a rating of 3, indicating medium-level leakage. Attention should be paid to water leakage in tunnels with changes in reservoir water levels. The leakage evaluation model for tunnels near reservoirs can effectively assess leakage levels under various conditions, providing a reference for safety assessments of tunnel leakage near reservoirs.

Sentiment analysis has become an important tool for understanding public opinion across languages and domains. Recently, there has been an increase in the number of studies on sentiment analysis in low-resource languages such as Kazakh. This is important to ensure that modern text analysis technologies are accessible to all users, regardless of their language background. The aim of the study is to create a sentiment analysis model for analyzing texts in Kazakh. As part of this work, we aim to use fine-tuning techniques on our own dataset for already existing models, thus improving their accuracy and efficiency for analyzing Kazakh language texts. This paper presents a manually collected dataset "KazIntTelCom" from the city information service 2GIS, consisting of user reviews, manually annotated by the authors taking into account the polarity of sentiment (i.e. negative positive or neutral). This dataset was used to fine-tune two pre-trained multi-lingual Transformer-based sentiment analysis models taken from the HuggingFace platform. The distillBERT and XLM-RoBERTa models were used for tuning. Also, the models were tested on the dataset "KazSAnDRA". The results show that accurate tuning even on a relatively small dataset gives a significant increase in performance, which is confirmed by an increase in the accuracy index by 20%-30%. In addition, false misses and false detections are analyzed, which allows us to identify directions for further improvement of the models. The contribution of this work, in addition to the dataset, is the analysis of model errors, which will help future developers to make more accurate settings of hyperparameters of training for sentiment analysis in Kazakh. These results are important for natural language processing and their adaptation to low-resource languages, promoting more inclusive and equitable access to modern analytical tools. Thus, this study demonstrates the effectiveness of the Transformers architecture for sentiment analysis in Kazakh and opens new opportunities for further model improvement.

Groundwater flow and transport models are essential tools for assessing and quantifying the mi-gration of organic contaminants at polluted sites. Uncertainties in the hydrodynamic and transport parameters of the aquifer have a significant effect on model predictions. Uncertainties can be quantified with advanced sensitivity methods such as Sobol’s High Dimensional Model Reduction (HDMR) and Variogram Analysis of Response Surfaces (VARS). Here we present the application of VARS and HDMR to assess the global sensitivities of the outputs of a transient groundwater flow model of the Gállego alluvial aquifer which is located downstream the Sardas landfill in Huesca (Spain). The aquifer is subject to the tidal effects caused by the daily oscillations of the water level in the Sabiñánigo reservoir. Global sensitivities are analysed for hydraulic heads, aquifer/reservoir fluxes, groundwater Darcy velocity and hydraulic head calibration metrics. Input parameters in-clude aquifer hydraulic conductivities and specific storage, aquitard vertical hydraulic conductiv-ities, and boundary inflows and conductances. VARS, HDMR and graphical methods agree to identify the most influential parameters which for most of the outputs are the hydraulic conduc-tivities of the zones closest to the landfill, the vertical hydraulic conductivity of the most permeable zones of the aquitard, and the boundary inflow coming from the landfill. The sensitivity of heads and aquifer/reservoir fluxes with respect to specific storage change with time. The aquifer/reservoir flux when the reservoir level is high shows interactions between specific storage and aquitard conductivity. VARS and HDMR parameter rankings are similar for the most influential parameters. However, there are discrepancies for the less relevant parameters. The efficiency of VARS was demonstrated by achieving stable results with a relatively small number of simulations.

The amino acid proline accumulates in plants during abiotic stresses such as drought and salinity and is considered a reliable marker of environmental stress. While its accumulation is well established, its precise role in stress tolerance and its underlying molecular mechanism remain less clear. To address these issues, we performed a meta-analysis - a robust statistical technique that synthesizes results from multiple independent studies while accounting for experimental differences. We focused on 16 physiological and morphological parameters affected by drought and salt stress in transgenic plants expressing proline metabolic genes. For each parameter, we calculated the effect size as the response ratio (RR), which represents the logarithm of the mean value in the transgenic group over the mean value of the control group (lnRR). Under stress, most parameters exhibited significantly higher response ratios in the transgenic group, confirming the beneficial effects of proline during drought and salt stress. Surprisingly, under non-stressed conditions, most stress markers showed no significant differences between transgenic and non-transgenic plants, despite elevated proline levels in the former. These results suggest that the benefits of proline may be related to proline catabolism or may only become apparent during stress, possibly due to interactions with reactive oxygen species (ROS), which accumulate predominantly under stress conditions.

To understand dynamics in climate change, informing policy decisions and prompting timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of year-on-year CO2 emission changes across ten countries, considering a broad range of factors including socioeconomic, CO2-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of variations in the target. To capture emissions trend variation, we categorized countries by the change in CO2 emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO2 emissions from coal, the three-year average change in CO2 emissions, GDP, CO2 emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO2 emissions changes, contributing to global efforts to address climate change

In this paper, a novel permanent magnet-assisted synchronous reluctance machine (PMASynRM) with rare-earth PMs and ferrite magnets is proposed. The performance of PMASynRM is discussed with respected to the different magnet ratio of rare-earth PMs and ferrite magnets. Some characteristics including the flux density, output torque, cogging torque, output power, power factor, torque ripple, loss, efficiency, and demagnetization are calculated by 2-D finite element analysis (FEA). The analysis results show that the excellent performance can be obtained by using hybrid magnet of rare-earth PMs and ferrite magnets with the suitable magnet ratio, and provide some desirable cost-performance trade-off.

The microbial communities are closely related to the overall health and quality of soil, but studies on microbial ecology in apple-pear orchard soils are limited. In the current study, 28 soil samples were collected from three apple-pear orchards in northeastern China, and the composition and structure of fungal and bacterial communities were investigated by high-throughput sequencing. Molecular ecological network showed that the keystone taxa of bacterial communities were Actinobacteria, Proteobacteria, Gemmatimonadetes, Acidobacteria, Nitrospirae, and Chloroflexi, and the keystone taxa of fungal communities was Ascomycota. Mantel tests showed that soil texture and pH were important factors shaping soil bacterial and fungal communities, and soil water soluble organic carbon (WSOC) and nitrate nitrogen (NO3--N) were also closely related to soil bacterial communities. Canonical correspondence analysis (CCA) and variation partition analysis (VPA) analysis revealed that geographic distance, soil texture, pH, and other soil properties could explain 10.55%, 13.5%, and 19.03% of the overall variation of bacterial communities, and they explained 11.61%, 13.03%, and 20.26% of the overall variation of fungal communities, respectively. The key stone taxa of bacterial and fungal communities in apple-pear orchard soils and their strong correlation with soil properties could provide useful clues toward sustainable management of orchards.

In this study, we primarily assess the relationship between social safeguards and the living standards of local communities in different ecological zones of Ghana. This research aims to assist policymakers and stakeholders to make informed and appropriate decisions when developing social safeguards for emission reductions projects. Principal component analysis was employed to develop a living condition index, after which we generated a second construct based on the number of factors affecting living conditions. The results showed that access to sharecropping, literacy, extension services, financial institutions, a mobile phone network, and markets have significant positive relationships with the living conditions of local communities in Ghana. In addition, 78% of the social safeguards described in Ghana’s REDD+ Strategy, Forest and Wildlife Policy, Benefits Sharing Mechanism, and Forest Plantation Development Strategy are aligned with the determinants of local communities’ living conditions. The findings suggest a need to enforce benefit-sharing mechanism, better forest governance, and tenancy reforms in order to prevent the overexploitation of landowners and avoid elite capture. Furthermore, farmers must be linked to financial institutions in order to obtain credits. Additionally, we must seek to provide local communities with better access to markets and mobile phone networks in a way that allows them to carry out their livelihood activities effectively and efficiently. These measures help to reduce the risk of REDD+ actions and ensure sustainability.

A regional modern river-sand geochemical mapping study in the majority of the Timor-Leste territory, including the research area, has not been previously reported. This study demonstrated the potential of modern river sand geochemical mapping as an effective method for lithological mapping, particularly in areas that face challenges in conducting efficient geological fieldwork and have limited geological information. A total of 53 modern river sand samples were collected and analyzed. Before examining the samples using wavelength-dispersive X-ray fluorescence for ten major elements, the loss-on-ignition method was employed. In the Clerec drainage basin, SiO2, CaO, and K2O showed positive correlations, and CaO was positively correlated with MnO. Similarly, in the Sahe River catchment, a positive relationship was observed between CaO, MnO, and TiO2. These findings suggested the presence of quartz, manganese, secondary K-bearing minerals, and secondary Ti-bearing minerals in carbonate rocks that have undergone silicification and other alteration processes. The principal component analysis results corroborated the SiO2/Al2O3 and K2O/Na2O ratios and the ICV findings. PC1 was related to the variations in Ca-poor and Ca-rich rocks; PC2 indicated notable inputs from SiO2-rich and SiO2-poor rocks; and PC3 revealed the possibility of contributions from secondary mineral assemblages related to silicification and low-grade metamorphic processes.

The study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction methods provides the opportunity to investigate the molecular origins of most of the rare diseases reviewed in the Orpha.net database. Thus, it has been possible to analyze the topology of the missense mutations found in the 2,535 proteins involved in Mendelian rare diseases (MRD), which form the database for our structural bioinformatics study. The amino acid substitutions responsible for MRD show different mutation site distributions at different three-dimensional protein depths. We then predicted the depth-dependent effects of mutations for the 20,248 pathogenic variants that are present in our database. The results of this structural bioinformatics investigation are relevant, as they provide additional clues to mitigate the damage caused by MRD.

The study of the concentrations and effects of fine particulate matter in urban areas have been of great interest to researchers in recent times. This is due to the acknowledgment of the far-reaching impacts of fine particulate matter on public health. Remote sensing data have been used to monitor the trend of concentrations of particulate matter by deriving aerosol optical depth (AOD) from satellite images. The Center for International Earth Science Information Network (CIESIN) has released the second version of its global PM_2.5 data with improvement in spatial resolution. This paper revisits the study of spatial and temporal variations in particulate matter in Saudi Arabia by exploring the cluster analysis of the new data. Cluster analysis of the PM_2.5 values of Saudi cities is performed by using Anselin local Moran’s I statistic. Also, the analysis is carried out at the regional level by using self-organizing map (SOM). The results show an increasing trend in the concentrations of particulate matter in Saudi Arabia, especially in some selected urban areas. The eastern and south-western parts of the Kingdom have significantly clustering high values. Some of the PM2.5 values have passed the threshold indicated by the World Health Organization (WHO) standard and targets posing health risks to Saudi urban population.

The paper examines the various uncertainties encountered in high-frequency trading (HFT) environments and delves into the multiple challenges faced by HFT firms in navigating the Dodd-Frank Wall Street Reform and Consumer Protection Act (referred to as the "Dodd-Frank Act"), particularly during the initial stages of its enactment. These challenges include the ambiguity surrounding the definition of HFT, the lack of clarity regarding regulatory requirements and boundaries, inconsistencies in enforcement resulting from deviations in understanding the content, and the absence of detailed descriptions of the Act’s provisions. These hurdles significantly impact not only the daily operations of HFT firms but also pose higher demands on their long-term strategic planning and risk management. Drawing upon the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model, this study employs an innovative analytical framework. Through the comprehensive application of conceptual space, cognitive space, and semantic space, it provides a systematic methodology for identifying and analyzing the aforementioned issues. This approach not only aids firms in better comprehending and adhering to complex regulatory requirements but also enables them to explore new business opportunities and competitive advantages while ensuring compliance.

In order to study the effect of explosion load on the flexural performance of RC beams, two RC beams of the same size were carried out explosion test and bearing capacity test successively. Piezoelectric intelligent aggregate was embedded in the beams in advance to monitor the damage state of the beams, which realized the whole process of damage monitoring of the concrete inside the structure from explosion damage to compression damage.Based on wavelet packet analysis and residual bearing capacity damage evaluation criteria, the beam damage was quantitatively evaluated, and the bending bearing capacity of RC beam after explosion damage was compared and analyzed.The results show that the change of concrete strain gauge signal, the phenomenon of crack development and the increase of internal crack development index indicate that the RC beam is damaged by explosion load.After explosion damage, the damage index of the beam increases and the residual bending capacity decreases, which proves that the piezoelectric intelligent aggregate sensor can effectively monitor the bending capacity change of the component after explosion damage.

Design plays a critical role and creates a positive user experience, establishing a solid brand identity and staying competitive in the digital world. This research investigates the impact of user data collecting and analysis in improving user experience. The Data-driven User interface design can create an optimized and relevant UI with individual preferences, which leads to increasing user engagement and usability. The proposal explores methodologies to implement data collection and user research for tailoring digital products.

The calculation of the global shear capacity of steel-concrete composite downstand cellular beams with precast hollow-core units is important as it affects the span to depth ratios and the amount of material used, hence affects the embodied CO2 calculation when designers are deciding on the floor grids. This paper presents a reliable tool that can be used by designers to alter and optimise grip options during the preliminary design stages, without the need to run onerous calculations. The global shear capacity prediction formula is developed using five machine learning models. First, a finite element model database is developed. The influence of the opening diameter, web opening spacing, tee-section height, concrete topping thickness, the interaction degree, and the number of shear studs above the web opening are investigated. Reliability analysis is conducted to assess the design method and propose new partial safety factors. The Catboost Regressor algorithm presented better accuracy compared to the other algorithms. An equation to predict the shear capacity of composite cellular beams with hollow-core units is proposed by Gene Expression Programming. In general, the partial safety factor for resistance, according to the reliability analysis, varied between 1.25 and 1.26.

Dipole antennas, fundamental components in wireless communication systems, have garnered significant attention due to their versatility and efficiency across various applications. This literature review explores the performance analysis of dipole antennas, focusing on recent advancements and methodologies from 2019 to 2024. Employing a systematic approach, the review synthesizes findings from multiple databases, emphasizing peer-reviewed studies that delve into dipole antenna designs and applications. Through meticulous data extraction, key parameters influencing dipole antenna performance, such as material properties and operating frequency, are identified and analyzed. Results highlight the widespread utilization of dipole antennas in wireless communication systems, including indoor positioning, mobile communication, and antenna calibration, among others. Discussions underscore the evolution of dipole antennas in base station environments and their role in filtering applications. The review concludes by emphasizing the continued relevance and evolution of dipole antennas, showcasing their adaptability and potential across diverse wireless communication contexts.

Traditional methods for discovery and development of new drugs can be a very time-consuming and expensive process because it includes several stages such as compound identification, pre-clinical and clinical trials before the drug is approved by the US Food and Drug Administration (FDA). Therefore, drug repurposing, namely using currently FDA-approved drugs as therapeutics for other diseases than what they are originally prescribed for, is emerging to be a faster and more cost-effective alternative to current drug discovery methods. In this paper, we have described a three-step in silico protocol for analyzing transcriptomics data using online databases and bioinformatics tools for identifying potentially repurposable drugs. The efficacy of this protocol was evaluated by comparing its predictions with the findings of two case studies of recently reported repurposed drugs: HIV treating drug Zidovudine for the treatment of Dry Age-Related Macular Degeneration and the antidepressant Imipramine for Small-Cell Lung Carcinoma. The proposed protocol successfully identified the published findings, thus demonstrating the efficacy of this method. In addition, it also yielded several novel predictions that have not yet been published, including the finding that Imipramine could potentially treat Severe Acute Respiratory Syndrome (SARS), a disease that currently does not have any treatment or vaccine. Since this in-silico protocol is simple to use and does not require advanced computer skills, we believe any motivated participant with access to these databases and tools would be able to apply it to large datasets to identify other potentially repurposable drugs in the future.

This paper studies the flexural behavior of the bridge deck link slabs made with the Polyvinyl Alcohol-Strain Hardening Cementitious Composites (PVA-SHCC). Tensile and flexural properties of the PVA-SHCC with four volume fractions, i.e., 0%, 1%, 1.5%, and 2%, were evaluated first. Next, using the similarity theory, composite models with a geometric similarity ratio of 1: 5 were designed to represent the bridge deck with the link slabs. The models considered three materials for link slabs, including concrete, cement mortar and self-compacting PVA-SHCC; and two different curing ages at 7 and 56 days. Bending tests were performed to investigate the flexural behavior of the models. Based on the fractal theory, cracking characteristics of the models with different types of link slabs were compared, and the relationship between Df and the flexural behavior of the models were studied. Numerical models were built to correlate with the results from the bending tests. It is illustrated that the flexural behavior of the self-compacting PVA-SHCC link slab is better than that of concrete and cement mortar link slabs, where the crack initiation and propagation can be postponed.

The intentional damages to local Cultural Heritage sites carried out in recent months by the Islamic State have received wide coverage from the media worldwide. Earth Observation data provide important information to assess these damages in such non-accessible areas, and automated image processing techniques would be needed to speed up the analysis if a fast response is desired. This paper shows the first results of applying fast and robust change detection techniques to sensitive areas, based on the extraction of textural information and robust differences of brightness values related to pre- and post-disaster satellite images. A map highlighting potentially damaged buildings is derived, which could help experts at timely assessing the damages to the Cultural Heritage sites of interest. Encouraging results are obtained for two archaeological sites in Syria and Iraq.

Bioinformatics is a rapidly developing field enabling scientific experiments through computer models and simulations. Considering the vast databases of biological data available, it is ex-tremely important to develop efficient methods and algorithms for their processing. Sequence comparison is the best method for studying the evolutionary interaction between genes. It is based on alignment – the process of arranging two or more sequences to achieve the maximum level of identity and degree of similarity. The paper presents a new version of the algorithm for pairwise DNA sequences alignment, based on a new method called CAT, where a dependency with a previous match and the closes neighbor are taken in consideration to increase uniqueness of the CAT profile and to reduce possible collisions, i.e. two or more sequence having same CAT profiles. This makes proposed algorithm suitable for finding exact match of a concrete DNA se-quence in a big set of DNA data faster. The generation of CAT profiles is made once before data has been uploaded to the database, allowing the profiles to be used as metadata for the sequenc-es. It consists of an algorithm to calculate a CAT profile against the selected reference sequences and an algorithm to compare two sequences based on the calculated CAT profiles. Improve-ments in generation of the CAT profiles, are detailed described in the paper. Block scheme, pseudo code tables and figures were updated according to the proposed new version and ex-perimental results. Experiments have been carried out with the new version of the method and different datasets to align DNA sequences based on the CAT method. New experimental results in terms of collisions, speed, and efficiency of the proposed solutions are presented. Experiments related to the performance comparison with Needleman-Wunsch were re-executed with the new version of the algorithm to confirm that we have the same performance. An analysis of the per-formance of the proposed CAT based algorithm against Knuth–Morris–Pratt algorithm, which has a complexity of O(n) and is one of the most widely used for searching biological data, was performed. The impact of prior matching dependencies on uniqueness for generated CAT pro-files is investigated. The analysis of the experimental results obtained by sequence alignment shows a small deviation of the proposed algorithm based on the CAT method, which can be ig-nored if this deviation is acceptable at the expense of performance. The time efficiency of the CAT algorithm remains constant, regardless of the length of the sequences. Therefore, the ad-vantage of the proposed method is its fast processing in the alignment of large sequences, for which the execution of the exact algorithms takes a long time. The example code realization of the CAT Method, under the protection of the GNU General Public License v3.0, can be accessed on GitHub at: https://github.com/HristoS/CATSequenceAnalysis.

Background: Positive person-centered attributes of inpatient rehabilitation need to be identified from the patient perspective to be further developed and sustained. Purpose: To identify which attributes patients openly evoke as being great care experiences, using an open appreciative inquiry during the inpatient rehabilitation stay. Methods: Qualitative secondary analysis of appreciative patient comments during a bedside patient experience rounding, facilitated by a neutral party. Two independent analysts employed an inductive, summative form of content analysis. Results: Among 150 patients rounded, 122 provided categorizable appreciative accounts. Over two thirds of the patients (67.2%) focused on “staff attributes” in their great-experience accounts. Those attributes were mostly interpersonal such as being “attentive & caring - beyond clinical duty” and being “encouraging (but not too hard) & reassuring”. These interpersonal staff attributes were reported with words showing deep levels of personal significance or patient appreciation. Beyond staff attributes, the perceived quality of the “patient care” (31.1%) and opportunities for “leisure and social activities” (9.0%) were also frequently evoked. Amenities like food or costumer service were the least evoked, rarely so as an exclusive attribute (0.8% for each). Conclusions: The human(e) factor, especially the interpersonal qualities of staff, emerged as greatly appreciated from the patient experience perspective during inpatient rehabilitation. These experiences help identify which person-centered attributes of care might be further developed and sustained.

Understanding and improving seismic resilience in regions prone to earthquakes is crucial for mitigating the effects of natural disasters. This study zeroes in on Montenegro, a region frequently affected by seismic activity, with the goals of critically evaluating current resilience levels, pinpointing key preparedness gaps, and assessing the efficacy of existing response strategies. Employing qualitative methods, the research involved semi-structured interviews with residents from Montenegro's most vulnerable areas, including Nikšić, Podgorica, Bar, Kotor, Cetinje, Budva, Herceg Novi, and Berane. These participants, selected for their firsthand experiences with significant earthquake impacts, contributed to a deeper understanding through discussions that aligned with a broad conceptual framework, exploring everything from the nuances of citi-zen resilience to local government responses and personal preparedness. The study aimed to gather in-depth views on community resilience during earthquakes, evaluate citizens' knowledge and preparedness for such disasters, and examine local governance strategies for enhancing seismic resilience. It revealed varying resilience levels across communities, with some displaying well-structured strategies while others showed notable deficiencies. The findings underscore the urgent need for community-specific preparedness programs and improvements in both structural and educational frameworks to enhance resilience. This research highlights the necessity of a comprehensive approach that includes detailed risk assessments, tailored preparedness training, and stronger public awareness efforts. Additionally, it emphasizes strengthening local governments' capabilities to enact and sustain effective response measures to ensure ongoing community resilience against future seismic challenges.

Urban air pollution is a critical global environmental issue, necessitating an understanding of the spatiotemporal characteristics and driving factors for effective environmental protection and urban planning. This study introduced an advanced air quality assessment system that integrates environmental, socio-economic, and urban layout factors, addressing gaps in traditional models that often overlook the impact of urban spatial structures. Analyzing air quality data from Beijing's main urban area (2016-2020) alongside multi-source geographic data, the research develops a comprehensive evaluation system, incorporating 14 key driving factors. Employing Geographically Weighted Regression (GWR) and Multi-scale Geographically Weighted Regression (MGWR), the study quantitatively assessed the influence and spatial heterogeneity of these factors. Findings revealed an annual improvement in air quality, with a U-shaped seasonal pattern and significant spatial clustering (Global Moran’s I = 0.922). The MGWR model, in particular, provided a superior fit over GWR, effectively capturing the spatial variability of factors. Variables such as NDVI), economic output (GDP), and humidity space adjustment capability (HSAC) showed significant positive spatial impacts on air quality, while population density (POP), temperature (TEMP), and road density (RD) exhibited negative effects. These insights enhance the understanding of air pollution dynamics and aid in refining urban planning strategies.

Carbon capture, utilization, and storage (CCUS) considered a the key strategy for reducing the emissions of anthropogenic carbon dioxide from power generation plants, can be achieved by three main technologies: oxy-fuel combustion, pre-combustion, and post-combustion capture. Post-combustion carbon capture (PCC), where CO2 is removed after the fuel burning, is a crucial solution for reducing greenhouse gas emissions from natural gas power plants (NGPPs). However, high costs and energy penalties associated with PCC technologies hinder their widespread adoption. Recent advancements in hybrid PCC configurations have shown promise in improving efficiency and reducing costs. In effect, six PCC hybrid configurations below were identified as feasible process routes: • 2S-AB +AD: Two-stage Absorption + Adsorption hybrid • 2S-AB +MB: Two-stage Absorption + Membrane hybrid • 2S-AD +AB: Two-stage Adsorption + Absorption hybrid • 2S-AD +MB: Two-stage Adsorption + Membrane hybrid • 2S-MB +AB: Two-stage Membrane + Absorption hybrid • 2S-MB +AD: Two-stage Membrane + Adsorption hybrid Each hybrid has its own technical and economic challenges that need to be investigated in order to identify the best technique for carbon capture. In this paper, we performed Aspen Hysys design simulation of the six hybrids PCC configurations and also their economic evaluations using parameters like investment costs, operating costs, net present value, and rate of return, culminating in the use of three assessment parameters namely, levelized cost of electricity (LCOE), carbon emission intensity (CEI) and cost of carbon avoidance (COA), to evaluate the six hybrids PCC configurations and to determine the most viable option. Overall, it was found by dimensional analysis that the post combustion carbon capture using 2S-MB +AB: Two-stage Membrane + Absorption hybrid is the most viable for capturing CO2 from power generation plants and is hereby recommended. However, the choice of materials (membranes and absorbents) needs to be evaluated so as determined the best optimal configuration for commercialization.

This study aims to provide an understanding of the preferences of student tourists regarding service attributes related to domestic accommodation. To clarify how lodging firms formulate policies for the student travel market, a “Relative to an Identified Distribution” (RIDIT) analysis in which responses are treated as ordinal data instead of equally spaced data is performed to examine the Likert-scale data obtained from 296 university students in Taiwan. In our RIDIT analytical results, respondents’ preferences are sorted in a descending importance. Of the examined attributes, “Cleanliness of rooms” is the attribute most prioritized by the students, followed by “Located in a safe neighborhood/feeling safe in the room,” and “Close to scenic area for meeting the trip requirement.” The attribute least prioritized by the students is “Interaction with other guests.” This analysis provides conclusions that differ from those obtained through commonly used techniques including the arithmetic average and exploratory factor analysis. The preference rankings indicate that managers should focus on the most prioritized service attributes and implement the necessary measures to enhance their service quality and maximize customer satisfaction.

This paper presents the five-parameter Exponentiated Weibull Weibull (EWW) distribution with Weibull base failure rate parameterization, developed to model the dropout times of students in an online class module. Parameters of the model were estimated using both Maximum Likelihood and Bayesian estimation procedures to determine the most effective estimation method. Simulation results indicate that, across various hazard types and censoring levels, Bayesian estimates are more accurate and precise than Maximum Likelihood estimates. Real-life data analysis of online students module presentation supports these findings, showing significantly lower standard deviations for Bayesian estimates compared to Maximum Likelihood estimates, highlighting the efficiency of Bayesian estimation for the EWW distribution. Additionally, a comparative goodness-of-fit analysis demonstrated that the EWW distribution better fits the student dropout times dataset than Weibull Weibull (WW), Weibull Exponential (WE), and Exponential Weibull (EW) distributions. The median dropout time estimated using the EWW distribution is 24 days, suggesting that instructors should closely monitor student attendance around this period.

Rebar cages form the skeletal framework of reinforced concrete structural members. They comprise longitudinal and transverse reinforcing bars, usually connected by tie wires. Past studies have identified tie-wire connections as the "weak links" in large, prefabricated rebar cages, leading to their failure during site handling and construction stages. Recent research has explored the use of mechanical U-bolt connectors to bolster stability and safety of rebar cages while reducing their vulnerability and risk of failure. This paper presents a comprehensive experimental and numerical investigation of a new type of mechanical connector, referred to as "Cage Clamp", to enhance rebar cage stability and safety. Component-level experimental tests are conducted to determine the force-displacement response of Cage Clamp connectors for crossbar connections in different degrees of freedom. The results highlight the remarkable strength and stiffness of Cage Clamp connectors, significantly outperforming traditional tie-wire connections. Furthermore, full-scale experimental tests are performed on a circular underground pile-shaft rebar cage with Cage Clamp connectors to assess its behavior under various site handling conditions. The data obtained from these experiments are used to construct and refine detailed 3D finite element models of the rebar cages, capable of capturing the complex behavior of rebar cages with Cage Clamp connectors. Using the numerical model, a sensitivity analysis is performed to assess the effect of the number of Cage Clamps on the deflection and stability of the rebar cage. The findings show that using cage clamps to connect pick-up rebar with lateral reinforcement at every 8 to 10 feet is sufficient to ensure the stability of the cage during typical site handling conditions. The results of this study serve as a basis for establishing analysis, design, fabrication, and handling guidelines for rebar cages, contributing to safe and efficient construction practices.

Grasping the spatial and temporal patterns of forest fires, along with the key factors that drive them and the ability to forecast such events accurately, is essential for the successful management of forests. In China's southern region, forest fires significantly endanger the ecological system, public safety, and economic well-being. Through the application of Geographic Information Systems (GIS) and LightGBM (Light Gradient Boosting Machine) model, this study investigates the determinants of fire incidents and formulates a predictive model for forest fire occurrences, alongside a zoning strategy, within the Central-South area. The results indicate: (i) Spatially, fire points exhibit significant clustering and autocorrelation characteristics; (ii) The Central-South Forest Fire Prediction Model shows exceptional accuracy, reliability, and predictive power, with high performance metrics across training and validation sets, including over 85% accuracy, precision, recall, and F1 scores, along with AUC values above 89%, underscoring its efficacy in forecasting forest fires and distinguishing between fire events; (iii) Throughout the year, forest fire risks in the Central-South region of China vary by region and season, with risk spikes from March to May in Guangdong, Guangxi, Hunan, Hubei, and Hainan. From June to August, localized risks are observed in Heyuan and Huangshi, while from September to November, an increase in risk is noted in Guangdong (Meizhou, Heyuan, Shaoguan), Guangxi (Nanning, Hezhou, Yulin), and Hunan (Binzhou, Yongzhou, Hengyang) due to cooler, drier conditions and leaf litter accumulation. From December to February, the risk continues in specific areas across Guangdong, Guangxi, Hunan, and Hubei.

The presented work offers a novel process scheme for valorizing historical Pb-Zn slag, which involves crushing, grinding, and then using separation techniques to concentrate valuable components (nonferrous metals). This methodology could have a significant impact on the global beneficiation of metallurgical slags since, in theory, it is significantly more simple, environmentally friendly, and cost-effective than standard pyrometallurgical or hydrometallurgical procedures. According to previous physicochemical and mineralogical studies, Pb-Zn slag from "Topilnica" Veles is a valuable secondary raw material. This inhomogeneous technogenic resource contains substantial amounts of nonferrous metals (Pb, Zn, Cu, and Ag). However, laboratory tests have indicated that the Pb-Zn slag contains a very uneven valuable metals amounts, ranging from several g/ton to several tens of g/ton. The main issue is that traditional metallurgical procedures for releasing beneficial elements are not commercially viable since the valuable elements are "trapped" within amorphous aluminosilicates or intergrowths of alloy grains and glassy phases. Gravity concentration (Wilfley 13 shaking table) and magnetic separation (Davis separator and disk separator) were used to obtain the final concentrate following comminution and grindability testing. The gravity concentration proved more effective. This streamlined approach of valorizing Pb-Zn slag can reduce the need for hazardous chemicals used in hydrometallurgical refinement operations, as well as the extremely high temperatures required for pyrometallurgical processing. This is the first investigation into the feasibility of this novel approach, which includes direct investigations of the Pb-Zn slag feed using these alternative technologies. After extracting the valuable metals, the amorphous aluminosilicate part of the Pb-Zn slag can be reapplied as an alternative raw material in the building sector, adding to the circularity of the suggested approach..

This study sought to investigate steel frames’ performance with dual lateral loader system (Frame bending + bracings of divergent and convergent) in near and far filed to fault. In order to this, four categories of steel frame with dual system with 8, 10 and 12 story are designed with average formation based on existing seismic regulation in 2800 standard of Iran and tenth chapter of national regulations of construction (planning and performing steel construction). Time history non-linear dynamic analysis under the effect of near and far field earthquakes has been done on plan’s models. Then the maximum of floors’ dislocation, floors’ drift, roof dislocation, base shear and energy curves of frames are shown and compared with each other. All non-linear time history analyses have been accomplished using PERFORM 3D software.

Purpose: Investigation for the co-occurrence of two unrelated genetic disorders of muscular dystrophy and Prader-Willi syndrome (PWS) (OMIM # 176270) using joint whole genome sequencing (WGS). Methods: Trio WGS joint analysis was performed to investigate the genetic etiology in a proband with PWS, prolonged muscular hypotonia associated hyperCKemia, and early-onset obesity. The parents were unaffected. Results: Results showed maternal isodisomy uniparental disomy (UPD) in chromosome 15 expanding from 15q11.2 to 15q22.2, including PWS regions at 15q11.2-15q13. Maternal heterodisomy was detected from 15q22.2 to 15q26.3. A pathogenic variant, NM_000070.3(CAPN3):c.550del (p.Thr184fs), was identified at 15q15.1 in a heterozygous state in the mother that was homozygous in the proband due to maternal isodisomy. Conclusion: This is the first study of the concurrent molecular etiology of PWS and calpainopathy (OMIM # 253600) in the same patient. This report highlights the utility of joint analysis and need for the assessment of autosomal recessive disease in regions of isodisomy in patients with complex and unexplained phenotype.

Investigation of oomycete diversity in rice paddies of Fars Province in Iran led to the identification of two new Pythium sensu lato (s.l.) species as Globisporangium izadpanahii sp. nov. and Pythium banihashemianum sp. nov. The identification was based on morphological and physiological features as well as the phylogenetic analysis of nuclear (ITS and βtub) and mitochondrial (cox1 and cox2) loci using Bayesian inference and Maximum Likelihood. A major characteristic of G. izadpanahii was the production of globose hyphal swellings; this species did not produce vesicles and zoospores. The present paper describes formally these two new species and defines their phylogenetic relationships with other congeneric species. According to multiple gene genealogy analysis, G. izadpanahii grouped with other species of Globisporangium in the former clade G of Pythium s.l. and was closely related to both G. nagaii and the recently described G. coniferarum. The second species, designated P. banihashemianum, grouped with other species of Pythium sensu stricto in the former clade B of Pythium s.l. and according to the phylogenetic analysis shared an ancestor with P. plurisporium. In pathogenicity tests on rice seedlings, P. banihashemianum isolates were highly pathogenic causing severe root and crown rot, while G. izadpanahii isolates were not pathogenic.

Medical image segmentation is a critical step in various healthcare applications, aiding in diagnosis, treatment planning, and disease monitoring. In this study, we investigate the efficacy of a segmentation approach based on the 2D wavelet transform. Leveraging a dataset comprising 10 diverse medical images, we evaluate the performance of our segmentation method using three key metrics: accuracy, precision, and recall. Our findings demonstrate that the proposed approach enhances segmentation accuracy, offering promising results compared to existing methods. By harnessing the multi-resolution feature extraction capabilities of the 2D wavelet transform, our method achieves improved delineation of medical image structures, paving the way for more accurate and efficient healthcare interventions.

Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. By leveraging computer vision technologies to extract key traffic parameters from video streams, the approach demonstrated a notable improvement in aligning the generated data from the calibrated simulation model with car sensor data, achieving an average improvement of over 50% compared to the uncalibrated macroscopic model. Moreover, there was a substantial reduction in data drift for the machine learning model integrated into the virtual transport space using vehicle-to-everything technology, resulting in a more than fourfold decrease in the average absolute error of the model.

The main purpose of our research is the evaluation of the efficacity of solvent polarity on phenolic composition, as well as the antibacterial, and antioxidant activities of extract of Moroccan Mentha aquatica L., leaves. To attempt these objectives, the HPLC-DAD was used to identify the biomolecules. The total amount of phenolic and flavonoid content was determined using the Folin-Ciocalteu and aluminum trichloride processes as well as the DPPH, RP, and TAC tests were used to evaluate the antioxidant capabilities. In addition, by employing both in vitro and in silico analysis through the microdilution method, the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the extracts were assessed against multidrug-resistant bacteria. Furthermore, the High-Performance Liquid Chromatography (HPLC) analysis indicated that all extracts of M. aquatica were abundant in noteworthy phytochemical compounds, especially gallic acid, vanillic acid, chlorobenzoic acid, quercetin, and kaempferol. Furthermore, the results indicated that the total phenolic content was higher in hydroethanolic extract (62.19 ± 1.19 mg GAE/g DW) in contrast to the acetonic and aqueous extracts (22.22 ± 0.64, and 22.44± 0.57 mg GAE/g DW) respectively. Similarly, the aqueous ethanol extract contained the greatest flavonoid concentration, with values of 29.15± 0.09 mg QE/g DW when compared with the other extracts. The seam extract poses excellent antioxidant power with an IC50 of 0.06 ± 0.00 mg/mL for the DPPH test, and lower RP with EC50 of 80± 0.00 µg/ml when compared with acetonic and aqueous extract (370± 0.00 and 460± 0.01 µg/mL) respectively. Moreover, the hydroethanolic extract has a high level of TAC 75.09± 2.00 mg EAA/g DW, followed by aqueous extract carrying a value of 28.53 ± 1.36 mg EAA/g DW. The antibacterial capacity is ranged between 0.78 ± 0.05 mg/mL and 12.60 ± 0.00 mg/L. The results of the in-silico study demonstrated that quercetin, vanillic acid, and naringenin components contribute to the antioxidant capability. While gallic acid, kaempferol, and quercetin components are attributed to the antibacterial capacity.

Wastewater-based epidemiology (WBE) has emerged as a key method for continuous monitoring of COVID-19 prevalence including circulating SARS-CoV-2 lineages. WBE addresses limitations of traditional clinical COVID-19 surveillance such as clinical test availability, fluctuating testing rates and increased reliance on rapid antigen tests. Our study in Perth, Western Australia, found a significant positive correlation between SARS-CoV-2 concentrations in wastewater and clinical PCR positivity rates (rs = 0.772; p < 0.001) over an 18-month period that included four successive COVID-19 waves. A strong positive correlation was apparent between the proportions of SARS-CoV-2 lineages in wastewater and clinical cases within the same region (rs = 0.728; p < 0.001), including earlier detection of Omicron and recombinant lineages in wastewater before clinical case confirmation. The successful integration of WBE with healthcare data underscores its critical role in enhancing public health decision-making and pandemic management. This approach not only demonstrates the value of WBE in current global health surveillance efforts but also highlights the potential of WBE to address future public health challenges, as a comprehensive disease monitoring and response approach.

Liver fibrosis, a consequence of chronic liver damage or inflammation, is characterized by the excessive buildup of extracellular matrix components. This progressive condition significantly raises the risk of severe liver diseases like cirrhosis and hepatocellular carcinoma. The lack of approved therapeutics underscores the urgent need for novel anti-fibrotic drugs. Hepatic stellate cells (HSCs), key players in fibrogenesis, are promising targets for drug discovery. This study investigated the anti-fibrotic potential of Citrus hystrix DC. (KL) and its bioactive compound, β-citronellol (β-CIT), in a human HSC cell line (LX-2). Cells exposed to TGF-β1 to induce fibrogenesis were co-treated with crude KL extract and β-CIT. Gene expression was analyzed by Real-Time qRT-PCR to assess fibrosis-associated genes (ACTA2, COL1A1, TIMP1, SMAD2). The releasing of matrix metalloproteinase 9 (MMP-9) was measured by ELISA. Proteomic analysis and molecular docking identified potential signaling proteins and modeled protein-ligand interactions. Results showed that both crude KL extract and β-CIT suppressed HSC activation genes and MMP-9 levels. The MAPK signaling pathway emerged as a potential target of β-CIT. This study demonstrates the ability of KL extract and β-CIT to inhibit HSC activation during TGF-β1-induced fibrogenesis, suggesting a promising role of β-CIT in anti-hepatic fibrosis therapies.

The United Nations’ Agenda 2030 has established a series of Sustainable Development Goals to address global challenges, including poverty, food insecurity, access to education, and social inequality. In this context, charitable organizations such as Caritas play a crucial role in mitigating the negative effects of these challenges and promoting fair and sustainable development. This study aims to analyze prevalent needs among individuals seeking assistance from Caritas in Campania and examine how the organization contributes to achieving the Agenda 2030 Goals in the region. The statistical investigation techniques considered include tandem analysis [], which considers a dimension-reduction technique, such as multiple factor analysis, and, then cluster analysis to identify similar groups of individuals. These exploratory data analysis methods have allowed for the identification of common needs, including food assistance, support for education, employment, and housing assistance. Subsequently, Caritas programs and initiatives aimed at meeting these needs and promoting sustainable development are explored. The results indicate that Caritas plays a significant role in addressing the urgent needs of the vulnerable population in Campania and contributes to the goals of Agenda 2030, particularly those related to poverty alleviation, immigration, health promotion, education, employment, and reduction of social inequalities. This study provides an important perspective on the relevance and effectiveness of Caritas’ work in the context of Agenda 2030.

The railway industry focus in the past years was to research, find and develop methods to mitigate noise and vibration resulted from wheel/rail contact along track infrastructure. This resulted in a wide range of abatement measures that are available for the professionals of the industry today. However, although there are many options in the market, their practical implementations depend upon general constraints that affect most technological application in the engineering world. The progression of these technologies have facilitated the selection of more adequate methods for each best case scenario, but further studies are ought to be made to proper assess if each one is fit for their purpose. Every method implementation must be analyzed through budget and timeframe limitations, which includes building, maintenance and inspection costs and time allocation, while also aiming to meet different benefits, such as environmental impact control and wear of the whole infrastructure. There are several situations and facilities in a railway project design that need noise and vibration mitigation methods and each design allocates different priorities for each one of them. Traditionally the disturbance caused by railways to the community are generated by wheel/rail contact sound radiation that expresses in different ways, depending on the movement of the rolling stock and track alignment, such as rolling noise, impact noise and curve noise. More specifically, in special trackworks such as turnouts, the main area of this study, there are two noises types that must be evaluated: impact noise and screeching noise. With respect to the second, it is similar to curve squeals and, being such, its mitigation methods are to be assigned as if it was to abate curve squeal in turnouts and crossings. The impact noise on the other hand, emerges from the sound made by the rolling stock moving through joints and discontinuities (i.e. gaps) that composes these special components of a railway track. A life cycle analysis is therefore substantial for this reality and in this case will be applied to Squeal and Impact Noise on Special Trackwork. The evaluation is based on a valid literature review and the total costs were assumed by industry reports to maintain coherency. The period for a life cycle analysis is usually of 50 years, hence it was the value assumed. As for the general parameters, an area with high density of people was considered to estimate the values for a community with very strict limits for noise and vibration.

Understanding gene expression changes in chicks after vaccination against Newcastle Disease (ND) can reveal vaccine biomarkers. There is limited data on chicks’ early immune response after ND vaccination. Two trials aimed into this knowledge gap. In experiment one, forty-two 13 days-old specific pathogen free (SPF) chicks were used. Harderian glands (Hg) and tracheas (Tc) from five birds per group were sampled at 12, 24, and 48 hours post-vaccination (hpv) to evaluate the gene transcription levels by RNA sequencing (RNA-seq) and RT-qPCR. Results of RNA-seq were compared by glmFTest, while results of RT-qPCR were compared by t-test. With RNA-seq, significant up-regulation of interferon related genes along with JAK-STAT signaling pathways regulation was observed in the Hg at 24- hpv. None of the DEGs identified by RNA-seq were positive for RT-qPCR. Experiment 2 used one-hundred and twelve SPF and commercial chickens at 1 day-old and 14 days-old. Only the commercial birds had maternal antibodies for NDV. By RNA-seq, twenty core DEGs associated with innate immunity and viral genome replication inhibition were identified. Genes previously unlinked to NDV response, such as USP41, were identified. This research present genes with potential as immunity biomarkers for vaccines, yet further investigation is needed to correlate the core gene expression with viral shedding post-vaccination.

The viticulture sector, spanning over 7.3 million hectares worldwide, plays a crucial economic role but faces challenges such as labor shortages, rising production costs, market competition, and climate change stresses. Addressing these issues requires monitoring the spatial and temporal variability in vineyard productivity. Traditional ground-based observations, though accurate, are time-consuming and limited in scope. Precision viticulture, leveraging technologies like GPS, remote sensing, and artificial intelligence, offers efficient, non-destructive monitoring tools. This study aims to develop and test a technological workflow using the mobile app Digivit, integrated with the AgroSat platform, to estimate yield variability in vineyards. The workflow was performed during the 2023 growing season in a series of vineyards near Siena (Italy) and involved two main steps: spatial variability characterization using AgroSat and yield estimation using the mobile Digivit app. AgroSat processed Sentinel-2 NDVI data to identify homogeneous zones, while Digivit used smartphone images of grape clusters to estimate yields. The field campaign, performed three weeks before harvest, involved monitoring representative vines within identified zones and uploading georeferenced images for cluster segmentation analysis. Yield estimates were validated against measured weights, showing a strong linear correlation (R²=0.85, RMSE=43.74g). Excluding one outlier improved correlation to R²=0.95, RMSE=14.58g. Vineyard-level yield predictions were compared with harvest data, achieving an overall error of approximately 7%, in agreement with other methods in the literature. The study demonstrates that the Digivit app provides accurate, timely yield prediction, supporting farmers in harvest planning and winemaking management. The user-friendly mobile app approach facilitates a broader adoption of precision viticulture technologies among farmers, overcoming barriers related to technical expertise.

Exoskeleton technology has been shown to be useful for individuals with spinal cord injury (SCI), potentially facilitating their recovery of motor function and social reintegration. However, studies on the safety and usability of exoskeletons are limited. This study aimed to design a powered exoskeleton for SCI and evaluate its safety and usability. Ten healthy adults participated in this study. The prototype exoskeleton featured an adjustable frame, computer-controlled hip and knee joint actuators, and a gait-trigger sensor. The experimental actions included walking, standing, and sitting, with and without the prototype. Safety outcomes included adverse events, vital signs, numerical rating scale (NRS) scores for pain, discomfort, and fatigue, and prototype-human body clearance. Usability outcomes included donning and doffing times, subjective ratings, gait speed, stride length, and kinematic parameters of hip and knee joint angles. Safety outcomes showed no serious adverse events, stable vital signs, minimal NRS scores, and acceptable clearance. Usability outcomes showed high efficiency and received positive ratings. Kinematic parameters showed significant positive correlations between normal walking and walking with the prototype for both hip and knee joint angles, confirming its effectiveness in assisting walking. However, further device improvements and gait parameter tuning are necessary for future clinical application.

Simple summary This reassessment of the findings from our Phase I/II study seeks to evaluate the effectiveness of SBRT for treating local intermediate-high risk prostate cancer in the absence of androgen-deprivation therapy due to patient’s refusal, with a median follow-up of 6.5 years. Our primary focus is on analysing the treated areas compared to recurrence patterns in the seven patients who experienced local relapse. Additionally, we provide updated data on late toxicities of grade 2 or higher, both genitourinary and gastrointestinal. Abstract Objectives: We investigated spatial patterns between primary and recurrent tumor sites and assessed long-term toxicity after dose escalation stereotactic body radiation therapy (SBRT) to the dominant intraprostatic nodule (DIN). Materials and methods: In 33 patients with intermediate-high risk prostate cancer (PCa), doses up to 50 Gy were administered to the DIN. Recurrence sites were determined and compared to the original tumor development sites through multiparametric MRI and 68Ga-labelled prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (68Ga-PSMA-PET/CT) images. Overlap rates, categorized as 75% or higher for full overlap, and 25-74% for partial overlap, were assessed. Long-term toxicity is reported. Results: All patients completed treatment, with only one receiving concomitant androgen deprivation therapy (ADT). Recurrences were diagnosed after a median of 33 months (range: 17-76 months), affecting 13 out of 33 patients (39.4%). Intraprostatic recurrences occurred in 7 patients (21%), with ≥ 75% overlap in two, a partial overlap in another two, and no overlap in the remaining three patient. Notably, 5 patients with intra-prostatic recurrences had synchronous bone and/or lymph node metastases, while six patients had isolated bone or lymph node metastasis without intraprostatic recurrences. Extended follow-up revealed grade 2 or higher late GU and GI toxicity in 18% (n=6) and 6% (n=2) of the patients. Conclusions: Among patients with intermediate-high risk PCa undergoing focal dose-escalated SBRT without ADT, DIN recurrences were infrequent. When present, these recurrences were typically located at the original site or adjacent to the initial tumor. Conversely, relapses beyond the DIN and in extra-prostatic (metastatic) sites were prevalent, underscoring the significance of systemic ADT in managing this patient population. Advances in knowledge: focal dose escalated prostate SBRT prevented recurrences in the dominant nodule, however extra-prostatic recurrence sites were frequent.

During public health events, Active Mobility (walking and cycling) plays an important role in securing residents' travel. To enhance the resilience of urban transportation, this article integrates the travel environment elements for walking and cycling and constructs a travel environment for Active Mobility evaluation index system. This article uses survey data from the main urban area of Harbin in the context of the COVID-19 pandemic in May 2022, and combines the Gradient Boosting Decision Tree (GBDT) and Impact-Asymmetry Analysis (IAA) methods to analyze the satisfaction of the travel environment for Active Mobility. It proposes strategies for optimizing the travel environment for Active Mobility, ensuring residents' travel during public health events, guiding them to adopt Active Mobility, and improving urban transportation resilience. This research shows that Active Mobility can enhance urban transportation resilience. In addition, there are differences in resident demand for walking and cycling environments in the travel environment for Active Mobility. Residents are more concerned about the comfort of the walking environment, while they prioritize the convenience of the cycling environment.

The excavations carried out by the Ephorate of Antiquities of Piraeus and Islands at the cemetery in the Phaleron Delta, Attica (late 8th - 4th century B.C.), have uncovered in total 1961 burials of various types, among which the Burial 923 is of particular importance as it is the only case of secondary cremation inside a copper alloy lebes. The use of such a valuable bronze vessel, symbolizing prestige, social status and wealth, as a cinerary urn indicates the importance of this find for the interpretation of issues related to the use of the cemetery and the ritual customs of the period and makes imperative the need to preserve the object despite its significant erosion. In this paper, we will present issues related to the lifting and conservation of the bronze vessel, that was found buried within a coastal and sandy depositional environment. Also, the condition of the vessel was scientifically investigated using optical microscopy (OM), portable X-ray fluorescence (p-XRF) and X-ray diffraction (XRD) techniques. The results showed a completely mineralized bronze artefact with tin corrosion products in the core, which indicate decuprification of the copper alloy.

Research in complex network analysis, including tasks such as information dissemination and link prediction, has traditionally advanced by examining groups of nodes with similar characteristics. In multilayer complex networks, these analyses are performed layer-wise, with each layer representing a distinct feature. However, this approach often neglects interlayer interactions, leading to limited predictive power, underestimation of network resilience, and incomplete identification of influential nodes. This article introduces a novel block modeling technique that integrates dominant features from multiple layers, addressing these shortcomings. Our approach introduces a new centrality metric that combines layer weight and PageRank centrality to identify influential nodes within multilayer networks. Nodes are aggregated into blocks centered around these influencers, accounting for both intra- and interlayer connections. Empirical evaluation of various datasets demonstrates that our method effectively identifies influential nodes, thereby enhancing information dissemination and link prediction across diverse multilayer network structures. This technique offers significant potential for improving decision-making processes in various fields, including social network analysis, transportation systems, and biological networks.

This study develops a hybrid machine learning (ML) algorithm integrated with IoT technology to improve the accuracy and efficiency of soil monitoring and tomato crop disease prediction in Anakapalle, a south Indian station. An IoT device collected one-minute and critical soil parameters—humidity, temperature, pH values, nitrogen (N), phosphorus (P), and potassium (K)—essential for assessing soil health and optimizing crop growth. Kendall's correlations were computed to rank these parameters for utilization in hybrid ML techniques. Various ML algorithms including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Tree (DT), Random Forest (RF), and Logistic Regression (LR) were evaluated. A novel hybrid algorithm, 'Bayesian Optimization with KNN', was introduced to combine multiple ML techniques and enhance predictive performance. The hybrid algorithm demonstrated superior results with 95% accuracy, precision, and recall, and an F1 score of 94%, while individual ML algorithms achieved varying results: KNN (80% accuracy), SVM (82%), DT (77%), RF (80%), and LR (81%) with differing precision, recall, and F1 scores. This hybrid ML approach proved highly effective in predicting tomato crop diseases in natural environments, underscoring the synergistic benefits of IoT and advanced ML techniques in optimizing agricultural practices.

In review, the operating principles of the most common bio sensing devices, detection methods and the identification sensitivity of analyzed molecules were shown. The central focus was done on hybridization analysis of nucleic acids (NA), which are considered to be one of the most important analytes in terms of diagnostic point of view. Constructions enabling to transfer the fact of formation of nucleotide probe/target complex in to detectable signal by optical, electrochemical or micromechanical (piezoelectric) sensors were presented in this review.

In the ambitiously evolving construction industry of Kuwait, characterized by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. This research paper introduces a groundbreaking deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector’s significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This meticulous analysis allows for the detection of deviations that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning’s pivotal role in construction and building management. Additionally, our findings proactively sketch the future market trajectory and facilitate anomaly detection, thereby contributing substantially to the academic discussion on integrating machine learning into construction management. This research offers valuable insights for professionals within Kuwait, aiming to harness deep learning techniques to boost operational efficiency and strategic foresight in the construction sector.

Statistical denoising techniques have been well studied, however many challenges still remain. This 9 article focuses on determining the least necessary number of significant samples in a statistical pop-10 ulation sampling, while minimizing the impact on the statistical detection of the fiber Bragg grating 11 (FBG) power spectra. This is achieved by using Bayesian decision making, small population statis-12 tics while excluding false detection. First, we investigate the impact of two-sided sliding window 13 technique applied near the cell under the test. The window size varies from 8 to 60 samples. The 14 considered maximum size of population sampling is connected with the number of wavelength 15 samples within the bandwidth of the symmetrical FBG power spectra peaks. Regarding the sym-16 metry of FBG power spectra peaks and the possible reduction of computational demands, we inves-17 tigate the impact of a one-sided sliding window. This enables to achieve less processing latency for 18 real time applications thus brings benefits to FBG-based fiber optical sensing. Both, two- and one-19 sided small population sampling techniques are then experimentally verified. Detection results are 20 analyzed in depth via calculating detection thresholds by using the small size window sliding along-21 side the waveband. From results achieved, it was found that the normality three-sigma rule does 22 not need to be followed while using the small population sampling. Experimental demonstrations 23 and analyses showed that the proposed concept of denoising and statistical threshold detection 24 based on the sliding window does not depend on the prior knowledge of probability distribution 25 functions of the FBG power spectra peaks level and background noise. We have also found that the 26 thresholds’ adaptability mainly depends on the statistical numerical characteristics of the above ad-27 ditive mixture of the FBG power spectra peaks signals and background noise.

The European Union Water Framework Directive (2000/60/EC; WFD) aims to achieve a good ecological and chemical status of all bodies of surface water by 2027. The development of an integrated guidance on surface water chemical monitoring (e.g. WFD Guidance Document No. 7/19) has been transferred into national German law (Ordinance for the Protection of Surface Waters, OGewV). For the majority of compounds, this act requires a monthly sampling frequency to assess the chemical water quality status of a surface water body. To evaluate the representativeness of the sampling strategy under the OGewV, high-frequency online monitoring data is investigated under different sampling scenarios and compared with current, monthly grab sampling strategy. About 23 million data points were analyzed for this study, three chemical parameters (dissolved oxygen, nitrate-nitrogen, chloride concentration) and discharge data were selected from four catchment of different sizes ranging from 51391 km² to 84 km² (Elbe, Vereinigte Mulde, Neiße and two stations at Lockwitzbach). In this paper we proposed short-term online-monitoring (STOM) as a sampling alternative. STOM considers the placement of online sensors over a limited duration and return interval. In general we: (I) compare the results of conventional grab sampling with STOM, (II) investigate the different performance of STOM and grab sampling using discharge data as proxy for analyzing event-mobilized pollutants and (III) investigate the related uncertainties and costs of both sampling methods. Results show, that STOM outperforms grab sampling for parameters where minimum/maximum concentrations are required by law as the probability to catch a single extreme value is higher with STOM. Furthermore, parameters showing a pronounced diurnal pattern, like dissolved oxygen, are also captured considerably better. The performance of STOM showed no substantial improvements for parameters with small concentration variability, as nitrogen-nitrate The analysis of discharge events as a proxy parameter for event-mobilized pollutants proved that the probability of capturing samples during events is significantly increased by STOM.

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

The assessment of Photovoltaic (PV) systems, a key component of sustainable energy solutions, is of paramount importance in the pursuit of a greener future. This abstract provides an overview of environmental assessments associated with PV systems, considering their life cycle, energy production, and ecological impact. The study explores various methodologies and tools employed in the evaluation of PV systems’ environmental performance, addressing issues such as material sourcing, manufacturing, installation, and end-of-life disposal. Additionally, it delves into the trade-offs and challenges in achieving sustainability objectives in the solar energy sector. By critically examining the environmental implications of PV systems, this research contributes to informed decision-making and the sustainable integration of solar power into our energy landscape.

In this study, a new diphosphate compound, Ag0.2Na0.6FeP2O7, was synthesized using a solid-state reaction method and extensively studied using various techniques. The compound's structure was confirmed through X-ray diffraction analysis, revealing a single-phase monoclinic structure with the P21/C space group. The electrical behavior of the compound was investigated, and its conductivity followed Jonscher's universal law. The correlated barrier hopping (CBH) model explained the temperature-dependent ac conductivity and parameter "s" well. The study also explored the contribution of single-polaron and bipolaron hopping to the a.c. conductivity and discussed the ionic conductivity in relation to the structural characteristics of the sample. Overall, this investigation significantly enhances our comprehension of the distinctive electrical properties of Ag0.2Na0.6FeP2O7 and its potential practical applications.

Based on the traditional Chinese cultural belief of "male breadwinner, female homemaker," as well as the systemic and interactive characteristics of families, this study aims to explore the influence of maternal gatekeeping behavior on the quality and quantity of paternal parenting, as well as adolescent aggressive behavior. A total of 483 seventh-grade students completed questionnaires on maternal gatekeeping behavior, paternal involvement, parenting styles and aggressive behavior. Latent profile analysis identified four parenting combinations: positive, negative, mixed, and neglectful. Adolescents under negative parenting exhibited the highest aggression, while those under positive and neglectful parenting showed the least aggression. Maternal gatekeeping behavior correlated with paternal negative parenting and adolescent aggression. Paternal negative parenting mediated the relationship between maternal gatekeeping and aggression, while paternal involvement moderated this relationship. These findings highlight the role of parental interaction in adolescent behavior and support family-based interventions.

Pain is a multifaceted multisystem disorder that adversely affects neuro-psychological processes. This study compares the effectiveness of central-stimulation (tDCS) and peripheral-stimulation (TENS) in pain-inhibition during a cognitive-task in healthy volunteers and to observe potential neuro-cognitive improvements. Healthy eighty volunteers went through a complete experimental protocol comprising pain-induction (CPT), administration of tDCS/TENS, EEG-data acquisition, and cognitive assessments. The protocol for this study is categorized into 04-groups: G1(Control), G2(TENS), G3(Anodal-tDCS), and G4(Cathodal-tDCS). Paired t-tests (p

In recent years, online review system attracts attention to assess seller-customer relations in the world of e-commerce. To address the quality concern of online review, especially incentivized ones, this study evaluates credibility and consistency based on reviews’ volume, length, and content to distinguish the impact of incentives on customer review behavior, improving review quality, and purchase decision-making. Software product reviews collected from software review websites, including Capterra, Software Advice, and GetApp undergo Exploratory Data Analysis (EDA) to reveal critical features such as cost, support, usability, and product features. The indirect impact of companies’ size, direct impact of users’ experience, and different impacts of changing situations during years on the incentive reviews’ volume are major findings of sentiment analysis. A/B testing results show a minimal to no impact of such reviews on purchasing decisions, highlighting discrepancies in credibility and consistency in volume, length, and content. Employing advanced techniques like Sentence-BERT (SBERT) and TF-IDF, the study explores semantic differences in reviews to improve recommendation systems for a more customized shopping experience. This approach seeks to establish a framework that discerns between review types and their effect on customer behavior. The findings contribute to developing more sophisticated and consistent e-commerce solutions, emphasizing the importance of authentic and reliable online reviews in influencing consumer choices.

Background This study aimed to identify the prominent risk factors that may trigger danger signals of early onset of vascular deregulation in T1D. The risk factors we have identified may serve as dual-purpose biomarkers of rapid diagnosis that offer prospective tailor-made resolution therapeutics. Methods The healthy control (HC) was compared with individuals diagnosed with T1D for the risk factors of vascular deregulation in published research studies from year 2013 to 2023. The PubMed, Web of Science and Google Scholar databases were searched from 1/1/2013 to 1/9/2023. The risk of bias was assessed with the Cochrane (ROBINS-I ) tool, which is relevant to clinical subjects. A random effect model for continuous data was followed and analysed by RevMan 5.4 and GraphPad Prism software. Results 80 relevant case-control studies having 5293 HC and 7492 T1D patients were included. The age and sex-matched HC consisted of persons free of disease and not under any medication while clinical subjects of all age groups were included. 28 risk factors were grouped into six primary outcome models, all of which favoured the T1D synonymous with a high risk of CVD. Conclusions Our findings have strong implications for improving the quality of life and health economics related to vascular disease in T1D. HbA1c% is the most effective biomarker, followed by FBG, LDL-C, and AI% which the clinicians could evaluate using a simple blood test or non-invasive techniques. (Protocol registered at https://www.crd.york.ac.uk/prospero/ CRD42022384636).

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

Anxiety disorders affect patient quality of life (QoL). This study aimed to examine the associations between improved QoL and reduced clinical symptoms and Korean medicine (KM) in patients with anxiety disorders. Medical records of patients with anxiety who were treated with KM (acupuncture, Anxiety disorders affect patient quality of life (QoL). This study aimed to examine the associations between improved QoL and reduced clinical symptoms and Korean medicine (KM) in patients with anxiety disorders. Medical records of patients with anxiety who were treated with KM (ac-upuncture, psychotherapy, Chuna therapy, aromatherapy, or herbal medicine) for at least 4 weeks were retrospectively analyzed. Clinical, QoL, and cost outcomes were measured at baseline and at 4 and 12 weeks (Anxiety: State-Trait Anxiety Inventory[STAI X-1(state), X-2(trait)], Beck Anxiety Inventory[BAI]; anger: State-Trait Anger Expression Inventory State[STAXI-S(state), T(trait)], Anger Expression Inventory[AXI-K-I(anger-in), AXI-K-O(anger-out), AXI-K-C(anger-control); depression: Beck Depression Inventory-II[BDI II], QoL: QoL-related instruments Euro Quality of Life 5 Dimensions utility score[EQ-5D], Euro QoL Visual Analog Scale[EQ-VAS]). The quali-ty-adjusted life years (QALYs) and average cost-effectiveness ratio of KM for anxiety were esti-mated. The medical records for 67 patients were evaluated. KM treatments were found to be as-sociated with decreased anxiety (STAI X-1; STAI X-2; BAI, p

An HFMD outbreak spread over the city of Hải Phòng from Summer 2011 to Autumn 2012. This epidemic was chosen because it was the very first HFMD epidemic in North Vietnam, eliminating thus interferences with previous outbreaks. This epidemic displayed 3 separate waves. A complete dataset was collected for more than 9500 patients during this period, which enabled us to analyze this epidemic at different scales. Access to the healthcare system was crucial during this period, with a reorganization of the system in February-March 2012. An analysis at commune level enables us to track the epidemic along certain communication routes. The three-waves structure reveals a wide disparity at district level. We develop a mathematical model showing high accuracy at the adjustment of data for both the total number of cases and for the number of cases per week. As a consequence, the model was able to accurately determine the dates of beginning and end of each wave and to show that they overlapped. Using mathematical functions associated with this model, it was possible to calculate the probability for a patient to belong to a specific wave.

In response to the escalating global waste production rate, sustainable waste management be-comes indispensable. In parallel, the transformation of energy system is vital in order to ensure affordable, decentralized, and sustainable energy for the global community, based on economic, environmental, and social criteria. Waste-to-Energy (WtE) is an effective option in order to put a stepping stone to address both issues provided that multiple views and criteria are considered. This paper puts forward a generic methodological framework to holistically assess WtE technologies based on the PROMETHEE approach. In addition to environmental, economic, and social aspects, demonstration of applicability of the proposed methodology is realized at national level the case of Greece. Anaerobic Digestion seems to be the most preferable choice, recognized for its cost-effectiveness and lower environmental burden to other WtE technologies (i.e. gasification, pyrolysis, incineration). Insightful expert opinions and public preference are of paramount importance to the adopted approach, revealing technology-specific concerns and preferences. In addition, the material presented underscores a crucial gap in Life Cycle Assessment studies for large-scale WtE, emphasizing the need for future scientific attention and exploration. Public consensus indicates a positive overall attitude towards waste valorization in the context of energy production.

The article presents the results of the technical-economical study regarding the optimization of the FDM parameters (Lh – the height of the layer deposited in one pass and Id – the filling percentage) for the manufacture of PETG and ASA parts. To carry out the technical-economical study, we used the fundamental principle of value analysis, which consists in maximizing the ratio between Vi/Cp, where Vi - represents the mechanical characteristic, and Cp - represents the production cost. The results of the study show that for the tensile specimens made of PETG, the parameter that significantly influences the results of the Vi/Cp ratios is Lh, and in the case of the compression specimens made of PETG, the parameter that significantly influences the results of the Vi/Cp ratios is Id. In the case of specimens manufactured by FDM from ASA, the parameter that decisively influences the results of the Vi/Cp ratios of the tensile and compression specimens is Id – the filling percentage. By performing optimization of the process parameters with multiple responses, we identified the optimal parameters for FDM manufacturing of parts from PETG and ASA: Lh = 0.20 mm and Id = 100%.

Background. The Stanford Health Assessment Questionnaire-Disability Index (HAQ) is widely used to measure functional ability in persons with Rheumatoid Arthritis (RA). The instrument was developed with limited involvement from persons with RA, and their perception of the instrument has not been studied in depth. The aim of this study was to explore how persons with RA experience the use of the HAQ in care. Methods. Forty persons with RA were purposefully recruited to participate in semi-structured interviews. The interviews were then analyzed qualitatively using thematic analysis. Results. The participants questioned the relevance of the HAQ but nevertheless experienced that the instrument had a profound effect on their understanding of health and how care is delivered. The analysis resulted in three themes: Problems with individual items, meaning of the summative score, and effects on care and health perceptions. Conclusions. To make the HAQ relevant to persons with RA, it needs to be revised or to include an option to select items most meaningful to the respondent. To ensure relevance, the HAQ update should preferably be co-created by researchers, clinicians and persons with RA.

This article investigates the effectiveness of feature extraction and selection techniques for enhancing the performance of classifiers accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data by employing three Feature Extraction (FE) methods are used namely, Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection. The performance of seven classification techniques such as Non-Linear Regression - NLR, Linear Regression - LR, Gaussian Mixture Model - GMM, Expectation Maximization - EM, Logistic Regression - LoR, Softmax Discriminant Classifier - SDC, and Support Vector Machine with Radial Basis Function kernel - SVM-RBF are evaluated with and without feature selection. The analysis reveals that the combination of PCC with SVM-RBF achieved a promising accuracy of 92.85% even without feature selection. Notably, employing BESO with PCC and SVM-RBF maintained this high accuracy. However, the highest overall accuracy of 97.14% was achieved when RDO was used for feature selection alongside PCC and SVM-RBF. These findings highlight the potential of feature extraction and selection techniques, particularly RDO with PCC, in improving the accuracy of DM detection using microarray gene data.

Several series of new polymers were synthesized: binary copolyesters of vanillic (VA) and hydroxybiphenylcarboxylic (HBCA) acids, as well as ternary copolyesters additionally containing hydroxybenzoic acid (HBA) and obtained via three different ways (in solution, in melt and in solid state). The high values of logarithmic intrinsic viscosities and insolubility of several samples proved its high molecular weights. It has been shown that the use of vanillic acid leads to the production of copolyesters with a relatively high glass transition temperature (about 130°C). Thermogravimetric analysis revealed onset of weight loss temperatures of ternary copolyesters occurred at 330-350 °C, the temperature of 5% mass loss is in the range of 390-410 °C. Two-stage thermal destruction is observed for all aromatic copolyester of vanillic acid: decomposition begins with VA units at 420-480 °C, and then decomposition of more heat-resistant units demonstrated above 520 °C. The copolyesters are thermotropic and exhibit a typical nematic type of liquid crystalline order. Mechanical characteristics of copolyesters are close to those for semi-aromatic copolyesters, but are much lower than typical values for fully aromatic thermotropic polymers. Thus, vanillic acid is a mesogenic monomer suitable for the synthesis of thermotropic fully aromatic and semi-aromatic copolyester, but resulting processing temperature must not exceed 280°C.

The aim of this paper is to investigate whether multifractal analysis can be applied to study choroidal blood vessels and help ophthalmologists for the early diagnosis of retinitis pigmentosa (RP). In a case study, we use Spectral Domain Optical Coherence Tomography (SDOCT), which is a non-invasive and highly sensitive imaging technique of the retina and choroid. The image of a choroidal branching pattern can be regarded as a multifractal. Therefore, we calculate generalized Renyi point-centered dimensions, which is considered a measure of inhomogeneity of data, to prove that it increases in the patients with RP with respect to the control group.

Serine peptidases (SPs) of the chymotrypsin S1A subfamily are an extensive group of enzymes found in all animal organisms, including insects. Here we provide analysis of SPs in the yellow mealworm Tenebrio molitor transcriptomes and genomes datasets and profile their expression pattern at various stages of ontogeny. A total of 269 SPs were identified, including 137 with con-served catalytic triad residues, while others 125 lacking conservation were proposed as non-active serine peptidase homologs (SPHs). Seven deduced sequences exhibit a complex do-main organization with two or three peptidase units (domains), predicted both as active or non-active. The largest group of 84 SP and 102 SPH had no regulatory domains in the propep-tide, and the majority of them were expressed only in the feeding life stages, larvae and adults, presumably playing an important role in digestion. The remaining 53 SP and 23 SPH had differ-ent regulatory domains, showed constitutive or upregulated expression at eggs or/and pupae stages, participating in regulation of various physiological processes. The majority of polypep-tidases was mainly expressed at the pupal and adult stages. The data obtained expand our knowledge on SPs/SPHs and provide a foundation for further research on the functions of this gene family in T. molitor.

HIV-associated neurocognitive disorders (HAND) persist under antiretroviral therapy as a complex pathology that has been difficult to study in cellular and animal models. Therefore, we generated an ex vivo human brain slice model of HIV-1 infection from surgically resected adult brain tissue. Brain slice cultures processed for flow cytometry showed >90% viability of dissoci-ated cells within the first three weeks in vitro, with parallel detection of astrocyte, myeloid, and neuronal populations. Neurons within brain slices showed stable dendritic spine density and mature spine morphologies in the first weeks in culture, and they generated detectable activity in multi-electrode arrays. We infected cultured brain slices using patient-matched CD4+ T-cells or monocyte-derived macrophages (MDMs) that were exposed to a GFP-expressing R5-tropic HIV-1 in vitro. Infected slice cultures expressed viral RNA and developed a spreading infection up to 9-days post-infection, which were significantly decreased by antiretrovirals. We also detected infected myeloid cells and astrocytes within slices and observed minimal effect on cellular via-bility over time. Overall, this human-centered model offers a promising resource to study the cel-lular mechanisms contributing to HAND (including antiretroviral toxicity, substance use, and aging), infection of resident brain cells, and new neuroprotective therapeutics.

Long COVID is a persistent symptom that appears in many COVID-19 patients, regardless of whether they are asymptomatic, mild, or severe. At least 10% worldwide are suffering from long COVID. The anti-inflammatory effect was evaluated in LPS-treated RAW264.7 cells using six Korean national insurance-covered herbal prescriptions with therapeutic potential for mitigating inflammation in the respiratory tract, and additional network pharmacology analysis was performed. Soshihotang (SST) significantly downregulated iNOS and COX2 expression, reduced the production of NO (nitric oxide) and inflammatory cytokines, such as TNFα, IL-1β, and IL-6, and inhibited IκBα phosphorylation and reduced the nuclear translocation of NF-κB in LPS-treated RAW264.7 cells. Network pharmacological analysis showed that SST was closely related to the NF-κB signaling pathway. Further study on the potential of herbal prescriptions will be needed before they can be used clinically by long COVID patients.

Safety signal detection, an integral component of Pharmacovigilance (PhV), aims to identify new or known adverse events (AEs) resulting from the use of pharmacotherapeutic products. Post-marketing spontaneous reports from different sources are commonly utilized as a data source for detecting these signals but there are underlying challenges arising from data complexity. This paper investigates the implementation of the Apriori algorithm, a popular method in association rule mining, to identify frequently co-occurring drugs and AEs within safety data. We discuss previous applications of the Apriori algorithm for safety signal detection and conduct a detailed study of an improved method specifically tailored for this purpose. This enhanced approach refines the classical Apriori method to effectively reveal potential associations between drugs/vaccines and AEs from post-marketing safety monitoring datasets, especially when AEs are rare. Detailed comparative simulation studies across varied settings, coupled with the application of the method to vaccine safety data from the Vaccine Adverse Event Reporting System (VAERS), demonstrate the efficacy of the improved approach. In conclusion, the improved Apriori algorithm is shown to be a useful screening tool for detecting rarely occurring potential safety signals from the use of drugs/vaccines using post-marketing safety data.

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

Intense climate change and rapid urbanization have increased the risk of urban flooding, seriously affecting urban economic and social stability. Enhancing the urban flood resilience (UFR) has required a new solution to cope with urban flood disasters. In this study, taking Yingtan city as an example, a system of indicators for evaluating UFR was constructed, with 17 representative indicators, comprising three subsystems: socio-economic, ecological and infrastructural. A hybrid (FAHP-EWM)-TOPSIS model was applied, to develop an index-based measurement to compare and evaluate UFR, and Gray Relational Analysis (GRA) was used to discover the main factors affecting UFR. In addition, the natural discontinuous method was innovatively used to divide the UFR grade interval into levels, and the grade change was evaluated based on the TOPSIS method. The results showed that (1) from 2010 to 2022, the UFR in Yingtan City increased by 80.69%, and the factors affecting UFR are highly correlated with urban infrastructure development. However, the ecological resilience in the subsystem showed a fluctuating downward trend, because of the influence of the surface area of lakes and rivers. (2) The grades of UFR for Yingtan City increased from Level III (2010 and 2016) to Level IV (2022), with local financial expenditures and the age structure of the population being the main factors currently limiting the development of UFR. The study provides a theoretical basis for the construction of an indicator system for assessing the UFR of Yingtan and proposes practical improvement directions for UFR.

Sensory descriptive analysis was used to evaluate the aged Feng-flavored baijiu. The results indicated that there are notable differences in the samples of different ages. The samples with shorter storage times and fresh distilled Baijiu present bran flavor, and fresh green flavor. with the increase of storage time, the fragrance of honey, sweetness, flowers, and aging gradually increases. The samples of Feng-flavored baijiu were dected by Headspace solid-phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC–TOFMS) , in total, 496 sustances were identified in all samples, mainly containing 14 types of substances, such as esters, aldehydes etc., of which 42 substances were found in Feng-flavored baijiu for the first time. Chemometrics was used to analyze the key differential compounds. 143 differential compounds closely related to aging were preliminarily screened, and principal component analysis revealed that these samples were separated by year. 65 differential compounds were selected out by partial least squares discriminant analysis(PLS-DA). Furthermore, 43 key differential compounds were selected from the aged Feng-flavored baijiu combined with variable importance in projection and Pearson correlation coefficients. Partial least squares regression (PLSR) was used to research the correlation of sensory properties and key differential compounds, and the results indicated that most compounds were closely related to the aged Baijiu . This study afford theoretical basis and reference for flavor research of Feng-flavored baijiu.

Recently, there appeared in this journal (Beh and Lombardo \textbf{2022}, {\it Symmetry}, 14, 1103) a paper that showed how to perform a correspondence analysis on a two-way contingency table where Bowker's statistic lies at the numerical heart of this analysis. Thus, we showed how this statistic can be used to visually identify departures from perfect symmetry. Interestingly, Bowker's statistic is a special case of the symmetry-version of the Cressie-Read family of divergence statistics. Therefore, this paper presents a new framework for visually assessing departures from perfect symmetry using a second-order Taylor series approximation of the Cressie-Read family of divergence statistics.

This work continues the review and illustrative application to energy systems of the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, in-cluding fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters, in the combined phase-space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21976 im-precisely known parameters. This benchmark is representative of “large-scale computations” such as those involved in the modeling of energy systems. The result (“response”) of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for “high-precision” parameters (2% standard deviations) and “medium-precision” parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st- through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems mod-eled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, MC). At least second-order sensitivities should be retained in order to obtain reliable predictions.

This study employs Principal Component Analysis to examine cranial measurements obtained from both extant and fossil specimens spanning 38 species and comprising over 300 individuals within the subfamily Cervinae. Our findings elucidate craniometric distinctions among cervids characterized by varying body sizes and certain evolutionary adaptations. While our results generally corroborate previous assertions regarding the limited craniometric variability among plesiometacarpal deer, they also unveil specific cranial adaptations within certain genera and species. The Principal Component Analysis of craniometric data revealed that giant and large-sized deer display significantly broader ecomorphological diversity in cranial shape compared to small-sized deer. Secondly, small-sized deer exhibit greater uniformity in their cranial shape, appearing densely clustered on the factorial map. Thus, we conclude that body size imposes ecological constraints, limiting the available niches due to eco-physiological factors. The study demonstrates that endemic insular deer do not evolve consistent craniometric features attributable to insular isolation, while the cranial proportions of medium-to-small sized deer delineate a ubiquitous morphological archetype characteristic of numerous cervid taxa spanning diverse phylogenetic lineages and sharing comparable body sizes. This group of "Dama-like" deer, characterized by similar body size, metabolic rates, ecological needs, and cranial morphometry, is referred to here as the fundamental eco-physiological type, typical of warm regions within the Palearctic and Oriental biogeographic realms..

Toll plaza optimization is essential for improving traffic flow and easing congestion on road networks. This study aims to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer facilitation. Different resources are used for data collection, like the National Highway Authority of Pakistan. Physical data is collected from the established Toll Plaza of Peshawar, along with the development of each line. The goal of this study is to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer happiness. The analysis of present traffic patterns, the detection of bottlenecks, and the creation of plans to ease congestion and enhance overall traffic flow are all part of the optimization process. The approaches used in this study include data analysis, simulation, and traffic modeling. PTV VISSIM is software for transportation planning, design, and analysis. The simulations consider various conditions, such as variable traffic volumes, toll collection methods, and lane configurations. When comparing the different optimization strategies, performance indicators like trip time, latency, and queue length are utilized to determine which approach is practical. Waiting time Vs. Existing each lane, etc, is explained. In the end, comparing queue lengths and throughput across different lane scenarios shows that maximizing throughput with existing lanes yields up to 45% improvement with E-tag lanes, 40% with existing E-tag lanes, 38% with E-tag plus additional toll and cash lanes, 32% with three cash lanes, and 28% with two E-tag lanes. Failure to regularly monitor and adjust the existing lane pattern scenario based on traffic patterns can lead to queues being 20% longer. Without proactive evaluation and optimization, there is a potential 63% opportunity for queue length improvement

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.

The existing PDC(Polycrystalline Diamond Compacts) -cone hybrid bit bearing adopts a unilateral support structure, which is prone to stress concentration in the journal area, resulting in fracture and wear failure of the bearing, thus reducing the service life of the hybrid bit. In this paper, a new type of double supported bearing hybrid bit is proposed. The static strength analysis of unilateral and bilateral support bearing structures is carried out by finite element simulation, and the stress and strain distribution of the two structures under the load of 20KN-100KN is obtained. Experimental devices for unilateral and bilateral support bearing structures are designed and manufactured to complete 50KN-100KN static pressure loading experiments. The results showed that the stress and strain of unilateral and bilateral support bearing increased linearly with the increase of load. Compared with unilateral bearing, when the load was 100KN, the maximum Mises stress of bilateral bearing decreased from 358.80MPa to 211.10MPa, with a decrease of 41.16%. The maximum contact stress decreased from 415.20MPa to 378.10MPa, decreased by 8.94%, and the maximum principal strain decreased from 11.01e-4 to 9.71e-4, decreased by 11.81%. The axial strain in the danger zone was reduced by 14.68% and 17.35%, respectively. It is found that the contact stress of the simulation data is highly correlated with the bearing life, and the service life of the bilateral bearing bit is increased by 8.94%. The simulation data and experimental results provide data support for the production of hybrid bits with bilateral bearing support.

Objective: To employ Latent Class Analysis (LCA) to investigate dietary intake patterns among Sudanese children aged 0 to 2 years and to examine the association of these patterns with sociodemographic factors. Methods: This study leveraged the Sudan Multiple Indicator Cluster Survey (MICS) 2014 data to uncover dietary intake patterns among 7,362 children using latent class analysis (LCA). We investigated class memberships concerning demographic and socioeconomic factors. The model's adequacy was determined using several fit indices, including BIC, AIC, entropy, CAIC, and SABIC, providing a holistic evaluation of the model's accuracy in capturing dietary behaviors. Results: Three latent classes were identified: Class 1 (55%) with an average nutrition composition, Class 2 (28%) with limited nutrition composition, and Class 3 (17%) with good nutrition composition. Significant associations were found between latent class membership and sociodemographic factors, particularly mother's education level and household wealth. The three-class solution provided the best balance between model fit and class distinction. Conclusions: The LCA revealed distinct dietary intake patterns and underscored the influence of sociodemographic factors on child nutrition. The findings suggest that targeted nutritional interventions should be developed according to the specific needs of different latent classes. The study also highlights the utility of LCA as a robust statistical and machine learning tool in public health research, capable of informing tailored interventions and policies for improving child nutrition. Implications: The study emphasizes the importance of maternal education and socio-economic status in shaping dietary behaviors of children in Sudan. It implies the need for policies that address educational disparities, food security, and economic development as part of comprehensive nutritional interventions.

In the era of mounting water scarcity, particularly in arid regions, the agricultural sector is striving to optimize the water resource management practices. Treated wastewater (TWW) has emerged as a viable alternative to freshwater for irrigation, offering a sustainable solution to water conservation. However, ensuring the safety and quality of TWW is paramount to safeguard for both crop health and environmental integrity. Electrical conductivity (EC), a critical factor in assessing TWW suitability for irrigation, can significantly impact crop yields and soil health since it is directly linked to the concentration of dissolved salts and ions. Therefore, this study delves into the development of advanced predictive models for EC assessment by employing supervised machine learning (ML) techniques like the Support Vector Regression (SVR), Adaptive Network-based Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). The TWW samples collected from Al-Qatif area, Saudi Arabia were assessed in the lab through a sensor-based real-time monitoring system integrated with Internet of Things (IoT) network and a multiprobe device. The key water quality parameters assessed involved, temperature (T), pH, Oxidation Reduction Potential (ORP), Resistivity (RES), Total Dissolved Solids (TDS), Salinity and Turbidity which served as the foundation for these models, given their profound influence on and their anticipated impact on EC levels. Following feature selection, two distinct model combinations were identified for the SVR, ANFIS and MLR models. Furthermore, to evaluate prediction performance, four statistical criteria, including coefficient of determination (R²), correlation coefficient (R), mean square error (MSE), and root mean square error (RMSE), were employed. Overall, the comparative performance results proved that ANFIS-M2 indicated the strongest predictive capability with the R² value of 0.99572 and 0.75145 and the RMSE values of 0.00965 and 0.01134, respectively in both calibration and verification stages.

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

Primary ciliary dyskinesia (PCD) is an inherited disorder that impairs motile cilia, essential for respiratory health, with a reported prevalence of 1 in 16,309 within Hispanic populations. Despite having the RSPH4A [c.921+3_921+6del (intronic)] founder mutation in 70% of Puerto Rican patients, the characterization of the ciliary dysfunction remains unidentified due to the unavailability of advanced diagnostic modalities like High-Speed Video Microscopy Analysis (HSVA). Our study implemented HSVA for the first time on the island as a tool to better diagnose and characterize the RSPH4A [c.921+3_921+6del (intronic)] founder mutation in Puerto Rican patients. By applying HSVA, we analyzed the ciliary beat frequency (CBF) and pattern (CBP) in native Puerto Rican patients with PCD. Our results showed decreased CBF and a rotational CBP linked to the RSPH4A founder mutation in Puerto Ricans, presenting a novel diagnostic marker that could be implemented as an axillary test into the PCD diagnosis algorithm in Puerto Rico. The integration of HSVA technology in Puerto Rico substantially enhances the framework for PCD evaluation and diagnosis, facilitating prompt detection and early intervention for improved disease management. This initiative, demonstrating the potential of HSVA as an adjunctive test within the PCD diagnostic algorithm, could serve as a blueprint for analogous developments throughout Latin America.

Teaching, learning, research, and development are all supported in a favorable atmosphere at the University of Lagos (UNILAG). The wetland systems made up of freshwater that are extremely biodiverse makeup about 10% of the total land area. Flooding, deforestation, excessive sedimentation, blockages to streams, abuse, overuse, and invasion of wetlands are some of the recent environmental challenges that the university faced. Urban heat island impacts, contaminants in the air, wetland degradation, social issues including stress, and a detachment from nature are all major concerns. With the green corridor behind the faculties of law and management science serving as a model supported by sustainability as a true foundation for UNILAG's campus landscapes, this article investigates the sustainable restoration of wetlands within the boundaries of the university. The use of qualitative techniques was employed, utilizing the Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis. Additionally, a geospatial mapping of the wetlands and tributaries that connect the Lagos Lagoon was conducted to evaluate the intrinsic ecological goods and services through a series of analytical steps, beginning with an analysis of the land area, current biodiversity, hydrology, and its potential through landscape mapping and analysis. The biophysical setup, landscape features, climatic data analysis, the ecological goods and services derived from the wetlands, and the records of flooding and rainfall are all included in the geospatial evaluation and analysis. The educational institution's site planning for the remediation of its wetlands takes into account the actual evolution of the area's drainage network. Throughout the wetlands, low carbon emissions are generated by off-road bike lanes, trails for walking, and raised hardwood platforms. Combining native plant species and park furniture, the academic landscape atmosphere will be improved in each of the wetlands and aesthetic values. the country's "University of First Choice" and its biophysically restored wetlands will help illuminate the landscape while the green corridors have an innate ability to attract human affinity within the academic community. All of these elements are implemented following an environmentally friendly landscape planning, management, design model, and enforcement process.

This paper investigates women’s empowerment and their active involvement in governance aimed at promoting sustainable development. The study focuses on examining the roles and processes of co-creation of nature-based solutions (NBS) for water management in Milan. Three research directions were explored: 1) to determine gender-based roles within local governance; 2) to detect semantic groups of NBS based on stakeholders' gender; c) to analyze gender-based linguistic and conceptual patterns of NBS. To achieve our objectives, we converted a sample of audio-video interviews of local stakeholders into textual data along with metadata indicating speakers' roles and gender. Descriptive statistics were applied to explore the governance's roles. We employed a combination of textual analysis and network analysis to identify thematic groups of NBS, and applied a word embedding approach to uncover linguistic patterns indicative of semantic sub-structures. The findings revealed significant demographic diversity. Male stakeholders perceive sustainable development as a process driven by the circular economy and the coordination of territorial authorities, while female stakeholders envision a transformative approach rooted in science, culture, and collaboration. The semantic sub-structure analysis highlighted a tendency among male governance to emphasize specialized skills, whereas female governance placed greater emphasis on problem definition rather than resolution.

Integrated crop-livestock production (ILP) is an interesting alternative for more sustainable soil use. However, more studies are needed to analyze soil pore properties under ILP at the micrometer scale. Thus, this study proposes a detailed analysis of soil pore architecture at the micrometer scale in three dimensions. For this purpose, samples of an Oxisol under ILP subjected to minimum tillage (MT) and no-tillage (NT) with ryegrass as cover crop (C) and silage (S) were investigated. The soil micromorphological properties of the soil were analyzed using X-ray microtomography. The MT(C) system showed the highest values of porosity (c. 20.4%), connectivity (c. 32.8×103), volume (c. 26%), and number of pores (c. 32%) in a rod-like shape. On the other hand, the MT(S), NT(C), and NT(S) systems showed higher tortuosities (c. 2.2, c. 2.0, and c. 2.1) and lower pore connectivities (c. 8.3×103, c. 6.9×103, and c. 6.2×103), especially in S use. Ellipsoidal and rod-shaped pores predominated over spheroidal and disc-shaped pores in all treatments. The results of this study show that the use of ryegrass as a cover crop improves soil physical properties, especially in MT. For S use, the type of soil management (MT or NT) did not show any differences.

The seismic events of 2015 in Nepal exposed the challenges associated with constructing earthquake-resistant 2-story self-built stone masonry structures in high-elevation rural regions, especially post-disaster. The Department of Urban Development and Building Construction (DUDBC) provided designs that failed to preserve the local architectural landscape and craftsmanship while adding unfamiliar knowledge and technology. To address the challenges related to architecture, structure, and socio-economic concerns in designing and implementing post-earthquake houses, a need-based building development framework is required for standardized practice. This study presents a novel framework to assess, design, and implement a self-built house after a disaster, consisting of five stages: 1) post-disaster problem assessment, 2) need identification, 3) material selection, 4) design development, and 5) validation and implementation. Based on this framework, we proposed a novel gabions building construction technology for two-story stone masonry structures that effectively mitigate post-disaster challenges such as logistic, resilience, and socioeconomic aspects while improving disaster resiliency specifically in the high-elevation rural areas of Nepal. The proposed two-story gabion building preserves architectural values, enhances structural integrity, and provides cost-effectiveness when compared with its DUDBC peer models while providing much-needed relief to the vulnerable community. The proposed resilient house, G- 2.1, utilizes locally sourced materials and craftsmanship, and innovative gabion technology that ensures affordability and facilitates knowledge transfer. The contribution of this study includes a development framework and a two-story gabions house that is most suitable for self-built post-disaster reconstruction and resilient to local architecture and socio-economic conditions while providing structural integrity and safety.

The application of the latest technological innovations has been promoted worldwide to increase farm productivity, including in salt farming. This research aims to determine the determinants of adoption decisions for salt production technology called geomembrane and estimate the adoption impact on technical efficiency. The data in this study is cross-sectional from 215 small-scale salt farmers on Madura Island, East Java, Indonesia. The data was analyzed using logistic regression to identify which factors influenced farmers’ decisions to use geomembrane. The influence of adoption on farmers' technical efficiency was then assessed using propensity score matching (PSM) and data envelopment analysis (DEA). The findings indicate that age and the dummy variables of gender, land ownership, profit-sharing involvement, and membership in the People's Salt Business Group (KUGAR) all had a significant impact on adoption rates. The findings of controlling matched samples using the PSM process reveal that geomembrane application improves and greatly increases farmers' technical efficiency. Those who used geomembranes displayed greater technical efficiency than those who did not. These findings imply that salt production technology should be promoted more to increase productivity, especially geomembrane adoption, through outreach and dissemination of information, including for landowners involved in the profit-sharing system.

The present research focuses on the endogenous development theory and investigates the relationship between economic growth (dependent variable) and renewable energy consumption, technological innovation, and export diversification (independent variables) in seven emerging economies known as the E-7. Previous studies have examined these factors individually but have not explored their combined impact on the E-7 economies. Therefore, this study contributes to the existing literature on the effects of renewable energy consumption, technological advancement, and export diversification on economic development. The study analyses the dynamic connection among these variables in seven selected emerging countries: Brazil, China, Indonesia, India, Mexico, Russia, and Turkey. Panel data from 1990 to 2022 is utilized, and various methodologies, including panel cointegration, the PMG-ARDL estimator, and robustness tests such as FMOLS and DOLS, are employed. Empirical inferences are drawn using the Dumitrescu-Hurlin Panel Causality (DHC) test, and the long-run relationships among the variables are validated using the Westerlund residual cointegration tests. The results from the PMG-ARDL estimator show that renewable energy consumption, technological advancement, and export diversification have a significant and positive impact on economic expansion, confirming the validity of the endogenous growth model in the E-7 countries. The control variable of the financial sector has a positive but insignificant effect on economic growth, while trade openness has a negative and significant effect. The DHC test results indicate a neutral feedback effect of renewable energy consumption on economic growth. The findings also reveal a unidirectional causal relationship between technological innovation and economic growth. Overall, these findings provide valuable insights for economic policymakers in the E-7 countries. By removing barriers to renewable energy consumption, technological innovation, and export diversification, policymakers can promote sustainable economic development.

A partial mitochondrial DNA Cytochrome Oxidase subunit I (mtCOI) gene haplotype variant of the coconut rhinoceros beetle (CRB) Oryctes rhinoceros classed as ‘CRB-G (clade I)’ has been the focus of much research since 2007 with reports of invasions into new Pacific Island locations (e.g., Guam, Hawaii, Solomons Islands). For numerous invasive species, inference of invasion biology via whole genome is superior to assessments via the partial mtCOI gene. Here, we explore CRB draft mitochondrial genomes (mitogenomes) from historical and recent collections, with assessment focused on individuals associated within the CRB-G (clade I) classification. We found that all Guam CRB individuals possessed the same mitogenome across all 13 protein coding genes and differed from individuals collected elsewhere, including ‘non-Guam’ individuals designated as CRB-G (clade I) by partial mtCOI assessment. Two alternative ATP6 and COIII partial gene primer sets were developed to enable distinction between CRB (clade I) that invaded Guam and CRB-G (Clade I) individuals collected elsewhere. Phylogenetic analyses based on concatenated ATP6-COIII genes showed that only Guam CRB-G (clade I) individuals clustered together, and therefore Guam was not the source of the CRB that invaded the other locations in the Pacific assessed in this study. The use of the mtCOI and/or mtCOIII genes for initial molecular diagnosis of CRB remained crucial, and assessment of more native CRB populations will further advance our ability to identify the provenance of CRB invasions being reported within the Pacific and elsewhere

In volleyball, the jump serve is a fundamental and crucial skill that is one of the most commonly used serving techniques. This study aimed to examine the kinematic differences between straight line (SL) and diagonal line (DL) in volleyball jump serves by focusing on torso and attack arm joint motions. Three-dimensional coordinate data were captured using a motion capture system (200 Hz) in 14 right-handed male professional volleyball players who performed SL and DL jump serves. This result suggests that the pelvis and torso backward rotation angles during the arm-clocking phase were smaller when performing DL serves. During ball contact, the servers exhibited greater forward rotation angles of the pelvis and torso to control the upper body facing the diagonal direction of the target zone. In addition, lowering the forward tilt angle of the trunk helped the servers maintain body balance in the air and increased their wrist flexion angle, preventing the ball from flying out of bounds when performing DL. These findings highlight the importance of controlling pelvic and torso rotations in the transverse plane when adjusting for different serving lines. These insights can guide volleyball athletes and coaches in refining their serving techniques.