In multi-objective optimization problems, the optimization target is to obtain a set of non-dominated solutions. Comparing solution sets is crucial in evaluating the performances of different optimization algorithms. The use of performance indicators is common in comparing those sets and, subsequently, optimization algorithms. A good solution set must be close to the Pareto-optimal front, well-distributed, maximally extended and fully filled. Therefore, an effective performance indicator must encompass these features as a whole and must be Pareto dominance compliant. Unfortunately, some of the known indicators often fail to properly reflect the quality of a solution set or cost a lot to compute. This paper demonstrates that the Degree of Approximation (DOA) quality indicator, is a weakly Pareto compliant unary indicator that gives a good estimation of the match between the approximated front and the Pareto-optimal front. Moreover, DOA computation is easy and fast.

We generalize the point information gain (PIG) and derived quantities, i.e., point information gain entropy (PIE) and point information gain entropy density (PIED), for the case of the Rényi entropy and simulate the behavior of PIG for typical distributions. We also use these methods for the analysis of multidimensional datasets. We demonstrate the main properties of PIE/PIED spectra for the real data on the example of several images, and discuss further possible utilizations in other fields of data processing.

Snow crab replaced groundfish fisheries as the primary focus of the Newfoundland and Labrador (NL) fishing industry in the mid-1990s. Over the past three decades, management philosophies in this male-only fishery have shifted from promoting industry expansion to promoting industry rationalization to implementing Precautionary Approach (PA) management. Until the mid-2010s, there were regionalized management strategies characterized by higher exploitation rates in northern than southern sub-stock units along the NL marine shelves. However, in the late 2010s, exploitation rates were permitted to elevate to high levels across the entire stock range in association with a large resource decline and evidence of biological harm through fishing emerged in chronically depleted areas. In 2019, when stock and fishery productivity were near historic lows, a multi-indicator PA system was informally introduced into management of the resource. This event coincided with anticipated improvements in stock and fishery performance. This paper examines the extent to which the multi-indicator PA management system, focused on promotion of both biological protection and maximization of fishing efficiency, may have contributed to recent improvements in stock and fishery status. A suite of indicators from areas implementing PA guidance into management in 2019 prior to formal adoption of the PA system in 2023 suggest the system has been highly beneficial in promoting rapid recovery, improving stock and fishery performance metrics to levels matching or exceeding historic levels under similar conditions. We discuss capacity of the system to better safeguard biological aspects of resource and fisheries productivity moving forward under a scenario of an expectant resource decline.

Globalization, knowledge economy, information technology advancements, and the rise of learning societies contribute to the concept of a learning city. The study examines the economic indicators of Yazd city, Iran, in the context of learning city development. A descriptive-analytical approach with a survey method and cluster sampling was employed. Data analysis involved Kolmogorov-Smirnov normality test, single-sample T-test, simple linear regression, and Kendall's W test. The normality test confirmed data normality. The T-test indicated that experts perceived the current state of most variables in Yazd as undesirable. Regression analysis revealed a significant positive association between the economic index and independent variables. Kendall's W test prioritized areas based on economic indicators, with the top 7 and bottom 5 areas identified for focused interventions. The study emphasizes the need to prioritize areas within Yazd based on their economic indicators. This prioritization informs strategies for enhancing social and legal aspects of Yazd's transformation into a learning city. Further research should explore effective methods to cultivate a full-fledged learning environment in the city.

This study unveils verapamil's compelling cytoprotective and proliferative effects on pancreatic β-cells amidst diabetic stressors, spotlighting its unforeseen role in augmenting cholecystokinin (CCK) expression. Through rigorous investigations employing MIN6 β-cells and zebrafish models under type 1 and type 2 diabetic conditions, we demonstrate verapamil's capacity to significantly boost β-cell proliferation, enhance glucose-stimulated insulin secretion, and fortify cellular resil-ience. A pivotal revelation of our research is verapamil's induction of CCK, a peptide hormone known for its role in nutrient digestion and insulin secretion, which signifies a novel pathway through which verapamil exerts its therapeutic effects. Furthermore, our mechanistic insights reveal that verapamil orchestrates a broad spectrum of gene and protein expressions pivotal for β-cell survival and adaptation to immune-metabolic challenges. In vivo validation in a zebrafish larvae model confirms verapamil's efficacy in fostering β-cell recovery post-metronidazole inflic-tion. Collectively, our findings advocate for verapamil's reevaluation as a multifaceted agent in diabetes therapy, highlighting its novel function in CCK upregulation alongside enhancing β-cell proliferation, glucose sensing, and oxidative respiration. This research enriches the therapeutic landscape, proposing verapamil not only as a cytoprotector but also as a promoter of β-cell re-generation, thereby offering fresh avenues for diabetes management strategies aimed at pre-serving and augmenting β-cell functionality.

The composition simplex (N, P, K, Ca, and Mg) of the leaf is the main scores used by different approaches like the Diagnosis and Recommendation Integrated System and Compositional Nutrient Diagnosis to study the nutrient interactions and balance in plant leaves. However, the application and validation of these concepts to grain composition remains unexplored. Contrary to foliar analysis's early intervention for nutrient deficiency detection and correction, applying this approach to seeds assesses diverse cultivars' potential, enabling anticipation of their adaptation to climate conditions and informed selection for future crops. In the present study, a collected database of more than 924 scores including the grain yield (kg ha-1) and the nutrient composition (mg kg-1) of different corn varieties is used to develop a novel nutrient-based diagnostic approach to identify reliable markers of nutrient imbalance. A 'nutrient signature' model is proposed based on the impact of the environ-mental conditions on the nutrient indices and composition (N, P, K, Ca, and Mg) of the corn grains. The yield threshold used to differentiate between low and high-yielding subpopulations is established at 12.000 kg ha-1and the global nutrient imbalance index (GNII) of 2.2 is determined using the chi-square distribution function and vali-dated by the Cate-Nelson partitioning method, which correlated yield data distribution with the GNII. Therefore, the nutrient compositions were classified into highly balanced (GNII ≤ 1.6), balanced (1.6 < GNII ≤ 2.2), and imbalanced (GNII > 2.2). In addition, we found that the Xgboost model's predictive accuracy for GNII is signif-icantly affected by soil pH, organic matter, and rainfall. These results pave the way for adapted agricultural practices by providing insights into the nutrient dynamics of corn grains under varying environmental conditions.

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

Zinc is a redox-inert trace element that is second only to iron in abundance in biological systems. In cells, zinc is typically buffered and bound to metalloproteins, but may also exist as a labile or chelatable (free ion) form. Zinc plays a critical role in prokaryotes and eukaryotes ranging from structural to catalytic to replication to demise. This review discusses the influential properties of zinc on various mechanisms of bacterial proliferation and synergistic action as anti-microbial element. We also touch upon the significance of zinc among eukaryotic cells and how it may modulate their survival and death through its inhibitory or modulatory effect on certain receptors, enzymes, and signaling proteins. A brief discussion on zinc chelators is also presented and chelating agents may be used with or against zinc to affect therapeutics against human diseases. Overall, the multidimensional effects of zinc in cells attest to the growing numbers of scientific research that reveal the consequential prominence of this remarkable transition metal in human health and disease.

Global attention to climate change has surged since the advent of the Paris Agreement, intensifying the importance of measuring and managing carbon productivity indicators on a national level. Nevertheless, concerns persist regarding the reliability of such measurements because of inherent discrepancies in implementing and operating national-level carbon productivity indicators, coupled with their inherent uncertainty. This study proposes a multiple regression model to address these issues aimed at refining national-level carbon productivity indicators metrics, accounting for factors such as the gross domestic product and total greenhouse gas emissions by sectors. The objective was to offer insights into enhancing and effectively utilizing current indicators, enabling a more nuanced interpretation of the variation in the carbon productivity indicators across diverse industrial landscapes. This study showed that adjustments of the carbon productivity metrics reflect disparities in emissions across industrial structures, with countries characterized by high emissions from non-service industries showing improving trends. In addition, this paper proposes an auxiliary indicator estimating method for carbon productivity, emphasizing its utility in interpreting productivity indicators within the context of varying industrial compositions across OECD countries. This study underscores the inadequacies of the current national productivity estimating method, pinpointing areas requiring refinement. Specifically, the method for estimating the auxiliary indicator for carbon productivity guarantees enhanced rationality when integrated with current methodologies. Moreover, by elucidating the nuances of industrial structures, this study advocates for more sophisticated approaches to interpreting and managing the productivity indicators tailored to each unique economic landscape of each country. Nevertheless, the limitations stemming from data availability underscore the need for further research, particularly in refining the national-level carbon resource productivity indicators analyses and exploring the thematic productivity variations in greater depth. By addressing these gaps, future studies will contribute to a more comprehensive understanding of national-level carbon resource productivity indicators dynamics and reveal targeted strategies for sustainable development.

Today's city needs more than ever methodological devices that ensure a specific urbanism, in keeping with the complexity of relationships that occur in the urban space. Networks or systems of diverse nature must be activated to achieve a certain success of the city, even more so in its fragile central fabrics. The study is focused on the historic neighborhoods of the city of Jaén (Spain), which have a high vulnerability index. The aim is to establish a strategy to revitalize their urban and social capital and improve their articulation with the rest of the urban and territorial structure. A methodological proposal for the analysis of vulnerability factors and an urban and cartographic analysis of the public space is addressed through an interweaving of a triple network: environmental, social, and urban. For this purpose, the configuration of this overlapping network of analysis and formulation of criteria is presented, which considers the urban multiplicity and promotes new dynamics for the integrated urban regeneration of the area and for the activation of public and collective urban spaces.

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

Convolutional neural networks (CNNs) have proven to be a very efficient class of machine learning (ML) architectures for handling multidimensional data by maintaining data locality, especially in the field of computer vision. Data pooling, a major component of CNNs, plays a crucial role for extracting important features of the input data and downsampling its dimensionality. Multidimensional pooling, however, is not efficiently implemented in existing ML algorithms. In particular, quantum machine learning (QML) algorithms have a tendency to ignore data locality for higher dimensions by representing/flattening multidimensional data as simple one-dimensional data. In this work, we propose using the quantum Haar transform (QHT) and quantum partial measurement for performing generalized pooling operations on multidimensional data. We present the corresponding decoherence-optimized quantum circuits for the proposed techniques along with their theoretical circuit depth analysis. Our experimental work was conducted using multidimensional data, ranging from 1-D audio data, to 2-D image data, to 3-D hyperspectral data, to demonstrate the scalability of the proposed methods. In our experiments, we utilized both noisy and noise-free quantum simulations on a state-of-the-art quantum simulator from IBM Quantum. We also show the efficiency of our proposed techniques for multidimensional data by reporting the fidelity of results.

In option pricing, we often deal with options whose payoffs depend on multiple factors such as foreign exchange rates, stocks, etc. Usually, it leads to a knowledge of the joint distributions and complicated integration procedures. The paper develops an alternative approach that converts the option pricing problem to a dual one and presents a solution to the problem in the optional semimartingale setting. The paper contains several examples which illustrate results in terms of the parameters of models and options.

The global use of alcohol based hand sanitizers (ABHS) as a means of controlling the transmission of infectious disease increased dramatically in 2020 as governments and public health agencies across the world advocated hand hygiene as a preventative measure during the COVID-19 pandemic. Although the performance of these products is most commonly defined as a function of their alcohol concentration, they are multifaceted products in which an interplay of several factors is important in determining efficacy. The hand sanitizer tetrahedron, is a novel concept that considers both ABHS formulation factors and product performance factors from a multi-dimensional perspective. The four faces of the tetrahedron represent input/formulation factors: 1) the type and amount of alcohol, 2) inactive ingredients, 3) the type of formulation/delivery system and 4) manufacturing practices. The four corners of the tetrahedron represent output/product performance factors: 1) efficacy, 2) sensory characteristics, 3) usage, usability and compliance and 4) product safety/adverse effects. All factors are of importance to ensuring the effectiveness and utility of these products.

With the rapid development of intelligent logistics and network environment, it has become an urgent problem to efficiently and accurately handle and analyse the huge amount of uncertain decision information. Traditional decision making methods often fail to make the best use of complex and incomplete information, especially in the field of cyber defence. To address these problems, this paper introduces a new mathematical tool, discrete multidimensional Z-numbers (MZs), for expressing and dealing with uncertainty and reliability in network defence decisions. In this paper, we first introduce the synthesis method of discrete multidimensional Z-numbers, which allows us to consider the uncertainty and reliability of multi-source information in network defence strategy. Then, the hidden probability of MZs is calculated by using the hidden probability model, and the multidimensional Z+−number is proposed, which improves the expressiveness of the model in dealing with information uncertainty. Based on MZs, we define a variety of utility functions and build a multi-attribute group decision framework for network defence around these functions. The MZs provides a novel perspective for analysing and designing response strategies, especially against highly adaptive and covert attacks. The method in this paper is verified by a case of network security assessment of an intelligent logistics company. The results show that this method can significantly improve the accuracy and efficiency of decision making, demonstrating its unique advantages and wide application potential in network defence. This integrated discrete multidimensional Z-number group decision method is not only suitable for intelligent logistics decision making, but also provides an efficient decision support tool for network defence, which helps to improve the intelligence and adaptability of network security defence.

Globally, bridges fail mostly due to hydrological causes such as scour or flooding. Therefore, under a hydrological approach, this study proposes a methodology that contributes to prioritize the intervention of bridges to prevent their collapse. Through an exhaustive literature review, an evaluation matrix subdivided into 4 dimensions was developed and a total of 18 evaluation parameters were considered, distributed as follows: 4 environmental, 6 technical, 4 social and 4 economic. This matrix was applied to eight bridges with a history of hydrological problems in the same river and validated through semi-structured interviews with specialists. Data were collected through field visits, journalistic information, review of the gauged basin historical hydrological flow rates and consultations with the population. Then, the modeling, which considered the influence of gullies that discharge additional flow, was carried out using HEC-HMS and HEC-RAS and subsequently calibrated. The application of the matrix revealed that five bridges have a high vulnerability, and three bridges have a medium vulnerability. The multidimensional approach can be adapted for studies of other riverine bridges.

Generally, when cognitive radio users are in Underlay mode, through the analysis of the electromagnetic spectrum in time, frequency, space, and field of multiple dimensional data accounts for judging the inherent correlation of the radio spectrum used/idle state, this is the realization of cognitive radio users (secondary users, SUs) efficient access to the foundation of the limited spectrum resources. Therefore, how to efficiently use of spectrum status of each SU implementation of reception multidimensional combination forecasting is the core of this paper addressing the problem. In this paper, we propose a deep-learning hybrid model called TensorGCN-LSTM based on the tensor data structure. The model treats SUs deployed at different spatial locations under the same frequency and the spectrum status of SUs themselves under different frequencies in the task area as nodes and constructs two types of graph structures. Graph convolutional operations are used to sequentially extract corresponding spatial-domain and frequency-domain features from the two types of graph structures. Then, the long short-term memory (LSTM) model is used to fuse the spatial, frequency, and temporal features of the cognitive radio environment data. Finally, the prediction task of the spectrum distribution situation is accomplished through fully connected layers. Specifically, the model constructs a tensor graph based on the spatial similarity of SUs' locations and the frequency correlation between different frequency signals received by SUs, which describes the electromagnetic wave's dependency relationship in spatial and frequency domains. LSTM is used to capture the electromagnetic wave's dependency relationship in the temporal domain. To evaluate the effectiveness of the model, we conducted ablation experiments on LSTM, GCN, GC-LSTM, and TensorGCN-LSTM models using simulated data. The experimental results show that our model achieved better prediction performance in RMSE, and the correlation coefficient R2 of 0.8753 also confirmed the feasibility of the model.

In this article, we investigate limitations of importing methods based on algorithmic information theory from monoplex networks into multidimensional networks (such as multilayer networks) that have a large number of extra dimensions (i.e., aspects). In the worst-case scenario, it has been previously shown that node-aligned multidimensional networks with non-uniform multidimensional spaces can display exponentially larger algorithmic information (or lossless compressibility) distortions with respect to their isomorphic monoplex networks, so that these distortions grow at least linearly with the number of extra dimensions. In the present article, we demonstrate that node-unaligned multidimensional networks, either with uniform or non-uniform multidimensional spaces, can also display exponentially larger algorithmic information distortions with respect to their isomorphic monoplex networks. However, unlike the node-aligned non-uniform case studied in previous work, these distortions in the node-unaligned case grow at least exponentially with the number of extra dimensions. On the other hand, for node-aligned multidimensional networks with uniform multidimensional spaces, we demonstrate that any distortion can only grow up to a logarithmic order of the number of extra dimensions. Thus, these results establish that isomorphisms between finite multidimensional networks and finite monoplex networks do not preserve algorithmic information in general and highlight that the algorithmic information of the multidimensional space itself needs to be taken into account in multidimensional network complexity analysis.

This article presents a concise survey of basic discrete and semi-discrete nonlinear models which produce two- and three-dimensional (2D and 3D) solitons, and a summary of main theoretical and experimental results obtained for such solitons. The models are based on the discrete nonlinear Schrödinger (DNLS) equations and their generalizations, such as a system of discrete Gross-Pitaevskii (GP) equations with the Lee-Huang-Yang corrections, the 2D Salerno model (SM), DNLS equations with long-range dipole-dipole and quadrupole-quadrupole interactions, a system of coupled discrete equations for the second-harmonic generation with the quadratic (χ⁽²⁾) nonlinearity, a 2D DNLS equation with a superlattice modulation opening mini-gaps, a discretized NLS equation with rotation, a DNLS coupler and its PT-symmetric version, a system of DNLS equations for the spin-orbit-coupled (SOC) binary Bose-Einstein condensate, and others. The article presents a review of basic species of multidimensional discrete modes, including fundamental and vortex solitons, their bound states, gap solitons populating mini-gaps, symmetric and asymmetric solitons in the conservative and PT-symmetric couplers, cuspons in the 2D SM, discrete SOC solitons of the semi-vortex and mixed-mode types, 3D discrete skyrmions, and some others.

Sulfate, the main dissolved contaminant in acid mine drainage (AMD), is ubiquitous in watersheds affected by coal and metal mining operations worldwide. Engineered passive bioremediation systems (PBS) are low-cost technologies that remediate sulfate contamination by promoting (1) precipitation of sulfate-bearing compounds, such as schwertmannite and gypsum; and (2) microbially-mediated sulfate reduction (BSR) to sulfide with subsequent precipitation of sulfide minerals. In this study, chemical and sulfur isotopic data are used to infer multiple pathways for sulfate sequestration in the Tab-Simco PBS. By simultaneously monitoring sulfate concentrations and δ34SSO4 values at four sampling points across the PBS, we (1) identified that the organic layer within the bioreactor was the primary site of BSR processes contributing to sulfate sequestration; (2) observed seasonal variations of BSR processes; (3) estimated that initially the BSR processes contributed up to 30% to sulfate sequestration in the Tab-Simco bioreactor; and (4) determined that BSR contribution to sulfate sequestration continuously declined over the PBS operational lifetime. Together, our results highlight the utility of combining geochemical and microbial fingerprinting techniques to decipher complementary processes involved in sulfur cycling in a PBS as well as the value of adding the sulfur isotope approach as an essential tool to help understand, predict, prevent and mitigate sulfate contamination in AMD-impacted systems.

The implementation of grey and green infrastructure is an effective mean to address urban flooding and nonpoint source pollution, but due to the complexity of the process and the diver-sity of benefits, there is a lack of measurement for their comprehensive benefits. Adopting a typ-ical university in Beijing as an example, this paper simulated the multidimensional benefits of the water quantity, water quality, and ecology of grey and green facility renovation by coupling the storm water management model (SWMM) and InfoWorks Integrated Catchment Manage-ment (ICM). Monetization methods and economical means were employed to characterize the comprehensive benefits. The results showed that grey and green infrastructure retrofitting re-duces the number of severe overflow nodes in the study area by 54.35%, the total overflow vol-ume by 22.17%, and the nonpoint source pollution level by approximately 80% under the heavy rain scenario and 60% under the rainstorm scenario. The annual benefits of grey and green infra-structure renovation reached ¥765,600/year, of this amount, ¥83,100/year is from hydrological regulation, ¥454,100/year is from nonpoint source pollution reduction, and ¥228,300/year is from ecological improvement. The benefits of green facilities were higher than those of grey facilities, and the combined benefits were negatively correlated with the rainfall level, with a total bene-fit–cost ratio of 1.19. The results provide methodological and data support for grey and green in-frastructure retrofitting within the context of sponge cities.

We present a tutorial to realize a multidimensional fitting procedure capable of extracting all the relevant information contained in a sequence of charged particle images acquired as a function of time in femtosecond pump-probe experiments. The images are reproduced using a 3D fitting method, which provides the velocity (or center-of-mass kinetic energy) and angular distributions contained in the images and their time evolution. A detailed example of the method is shown through the analysis of the time-resolved predissociation dynamics of CH₃I on the B-band origin [Gitzinger et al., J. Chem. Phys. 133, 234313 (2010)]. We show that the multidimensional approach is essential for the analysis of complex images that contain several overlapping contributions where reduced dimensionality analyses cannot provide a reliable description of the features present in the image sequence. This methodology can be generalized to many types of multidimensional data analysis.

The study aims to investigate the impacts of climate change on butterfly populations Thimphu district of Bhutan. Butterflies are indicators of environmental factors and can act as an early warning of impending changes in local flora and fauna. The study will involve citizen science with participants using iNaturalist and Seek applications to capture butterfly data in various habitat areas in the Thimphu district. The data collected will be analyzed using Statistics Kit for Social Sciences (SPSS), Nvivo, and Geographical Information System (GIS) programs. The results of the research will be disseminated through awareness campaigns to increase public understanding of the value of butterfly protection in the conservation of healthy environments. The study's findings will be used to develop conservation initiatives to maintain biodiversity and assess the impacts of climate change.

Trends focused on the Global Innovation Index (GII) as a measure for progress of nanochemistry. This paper provides projections of recent developments in the word in nanochemistry based on the Global Innovation Index as a predictor for certain Arab countries. The GII is an annual ranking of countries by its ability and performance in innovation and is calculated on a basic average from five and two pillars in two sub-indexes, the Innovation Input Index and the Innovation Output Index. Each pillar represents a trait of creativity and consists of up to five measures, with a weighted average formula for measuring their ranking. In 2008, the GII rose to 36.3 in 2016 from 0.5. The GII is smaller than the GII in Arabic countries worldwide. During the years 2013-2016, the worldwide GII was increasing while for the same period, for Arabic countries, this decline could be explained by economic and industrial wars in the Arab region.

The present days can be considered a crossroad in the history our word because the economic, social and ecological needs don't agree one another. The result is a continuous growth of poverty and an increase of the ecological degradation. This has generated the present difficult socio-economic state, and it seems very difficult to escape. A new viewpoint must be introduced, but it cannot based on the usual economic indicators. So, new indicators must be introduced. They must allow us to consider the technological level, the environmental impact and the socio-economic conditions. In this paper we suggest three indicators based on an engineering approach of irreversibility. Three applications are shown in order to highlight the possible interest from different scientists and researchers in engineering, economy, etc, in order to develop sustainable approaches and policies for decision makers.

This study investigates the intricate relationships between happiness, sustainability, and cultural values across 74 countries. Utilizing a multidimensional scaling (MDS) and K-means clustering approach, we analyze six happiness indices alongside the Sustainable Development Goals Index (SDGI). Our findings indicate a positive correlation between economic development, social progress, and elevated levels of both happiness and sustainability. Nevertheless, instances like the United States highlight the necessity of alternative methodologies, highlining classification of nonlinear models. The MDS analysis identifies four distinct clusters of countries, each demonstrating similar profiles in terms of happiness, sustainability, and cultural values, as per Inglehart's framework. This clustering underscores the necessity for a comprehensive perspective that integrates economic, social, and cultural dimensions to produce robust results and mitigate disparities or contradictions in subjective measures like the World Happiness Score. Future research should explore the impact of institutional frameworks, governance models, and social capital. Additionally, longitudinal studies are needed to examine how these relationships evolve in response to global challenges. We acknowledge the limitations related to data heterogeneity, the subjectivity of cluster analysis, and the exclusion of outliers, emphasizing the need for further research to enhance the generalizability and robustness of these findings.

The main objective of this paper is to develop a readiness index model that can serve as an analytical tool for exploring the achievements of electrification of transportation systems. We have applied this readiness index model to evaluate the readiness positioning of China, Norway, and Sweden to-wards transport electrification. We have chosen these three countries as they represent diversity among countries that are in the process of adopting electric transport system solutions. Our developed readiness index model has four key dimensions, technological readiness, political readiness, societal readiness, and economic readiness. The embeddedness of all four dimensions in one model provides a multi-perspective way of analyzing and evaluating the readiness levels of countries moving towards transforming the transportation system. Therefore, we named the model a “multidimensional readiness index.” Our main conclusions are that the political processes and political decisiveness involved are the most important factors followed by the societal needs and economic ability, with the current technology available as the fourth. Without the participation of dedicated and determined political decision-makers being involved, the other three factors are challenging to obtain. Political decision-makers need to facilitate the use of economic means to support the transformation in the society and affected industries to balance the initial economic disadvantages of the electrically powered systems until they pass the cost disadvantage turning point. The development of the relevant technology is no longer a great barrier as it was at the beginning of this transformation, about 20 years ago. The technology for electrically powered transportation systems and devices is widely available now, although it is continuously evolving and being improved. Associated industries cannot be expected to initiate, finance, take the risk, and take the lead in this global societal transformation without clear and strong political support.

The study of vegetation community and structural change has been central to ecology for over a century, yet how disturbances reshape the physical structure of forest canopies remains relatively unknown. Moderate severity disturbance including fire, ice storms, insect and pathogen outbreaks, affects different canopy strata and plant species, which may give rise to variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits with known linkages to ecosystem functioning. We used lidar-derived canopy structural measured within a machine learning framework to detect and differentiate among various disturbance agents, including moderate severity fire, ice storm damage, age-related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that disturbance agents such as fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, pathogen and insect infestation, and senescence altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, indicating that this commonly quantified canopy feature is insufficient to characterize many moderate severity disturbances. Rather, measures of canopy structure, including those that describe multidimensional change, are needed to characterize disturbance at moderate severities because structural changes from these events are spatially and quantitatively variable. Our findings suggest that standard disturbance detection methods, such as optical based remote sensing platforms, may currently be limited in their ability to detect, differentiate, and characterize disturbance. Further, we conclude that a more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structure change will improve the modeling, detection, and prediction of functional implications of moderate severity disturbance.

INTRODUCTION: The purpose of the study was to determine (a) the overall preclinical character; (b) the cumulative cutoff values and the risk ratio, and (c) the factors associated with severity by a unidimensional and multidimensional analysis on 2173 Sars-Cov2 patients. METHODS: The machine learning study population consisted of 2173 patients (1587 mild and non symptoms patients, 377 moderate patients, 209 severe patients). The status of the patients was recorded from September 2021 to March 2022. RESULTS: The Covid19 Severity directly links with a significant correlation to Age, Score index of the chest X-ray, percentage and quantity of neutrophils, Albumin, C reactive protein, and ratio of Lymphocytes. Their important cut off values (from regression analysis) respectively are: 77.56 years old (the mild-moderate group), 5.53 (the mild-moderate group) and 10.51 (the moderate-severe group), 84.80% (the mild-moderate group) and 87.74%(the moderate-severe group), 11.77G/L (the moderate-severe group), 29.73g/L (the moderate-severe group), 7.46mg/dL (the mild-moderate group), 6.32% (the moderate-severe group). Their significant (p<0.0001) R score correlation with the severity of Covid19, are: 0.44, 0.52 and 0.52, 0.33 and 0.44, 0.42, -0.43, 0.40, -0.41. Their significant risk ratio (p<0.00001) from the meta-analysis, respectively are: 4.19 [3.58-4.95], 3.29 [2.76-3.92] and 3.03 [2.4023;3.8314], 3.18 [2.73-3.70] and 3.32 [2.6480;4.1529], 3.15 [2.6153;3.8025], 3.4[2.91-3.97], 0.46 [0.3650;0.5752] (p<0.00001), 0.34 [0.2743;0.4210]. The pair ALT – Leucocytes and Transferrin – Anion Chloride get the most important correlation shift. ALT – Leucocytes show the important negative link (R=-1, p<0.00001) in the mild group to the significant positive correlation in the moderate group (R=1, p<0.00001). Transferrin–anion Chloride has an important positive association (R=1, p<0.00001) in the mild group with a significant negative correlation in the moderate group (R=-0.59, p<0.00001). The network map and HCA show that in the mild-moderate group, the closest neighbors with the Covid19 severity are ferritins, Age. Then there is C-reactive protein, SI of X-ray, Albumin, and Lactate dehydrogenase, which are the next close neighbors of these three factors. In the moderate-severe group, the closest neighbors with the Covid19 severity are Ferritin, Fibrinogen, Albumin, the quantity of Lymphocytes, SI of X-ray, white blood cells count, Lactate dehydrogenase, and quantity of neutrophils. CONCLUSIONS: Complete multidimensional study in 2173 Covid19 patients in Vietnam shows the whole picture of all the preclinical factors, which may become the clinical reference marker for surveillance and diagnostic management

Background: and objectives: Covid-19 is a global pandemic that requires a culturally integrated response in all Latin American government systems. In this study, we investigate the multidimensional associations of actions in Latin American indigenous peoples against Covid-19 Methods: We performed a data mining analysis of scientific literature records from 19 academic journals, using the topics Covid-19, mortality, indigenous peoples, and future measures, rights and actions. The literature is analyzed in a multidimensional way, either isolated or combined. It uses switchable statistical methods, the R Studio, worddj, Gephi, and Iramuteq for both textual and multidimensional analysis. Results and Conclusions: In this analysis, the literature is classified into 4 linked groups which are the indigenous people, the rights of territorial protection, the indigenous health culture, the Covid-19 pandemic with its protection efforts. Taking the indigenous peoples as an axis, we observe that the covid pandemic and the protection of their territories do not have the same force in the current context, the indigenous people are more linked to the protection of their territory. Indigenous peoples have a link of cultural strength in traditional health independent of Covid treatment. It should be noted that government agencies are taking measures regarding the covid pandemic with an effort not marked within the health culture of indigenous peoples.

In this paper, we propose a simple tool to help the energy management of a large buildings stock defining clusters of buildings with the same function, setting alert thresholds for each cluster, and easily recognizing outliers. The objective is to enable a building management system to be used for detection of abnormal energy use. First, we framed the issue of energy performance indicators, and how they feed into data visualization (Data Viz) tools for a large building stock, especially for university campuses. Both for Data Viz and clustering algorithm processes, we discussed two possible approaches to choose the right number of clusters and the identification of alert thresholds and outliers, after a brief presentation of the University of Turin's building stock case study. Different Data Viz tools have been studied to apply a specific clustering algorithm, the k-means one. An explorative analysis based on the general Multidimensional detective approach by Inselberg has been performed. Two multidimensional analysis tools, the Scatter Plot Matrix and the Parallel coordinates method have been used. Secondly, the k-means clustering algorithm has been applied on the same dataset in order to test the hypothesis made during the explorative analysis. Data Viz techniques developed in this study revealed to be very useful to explore quickly and simply a large buildings' stock, identifying the worst efficient buildings and clustering them according to their distinct functions.

Heat index (HI) is a biometeorological indicator that combines temperature and relative humidity. This study aimed to investigate the relationship between heat index and daily counts of diarrhoea hospitalisation in Dhaka, Bangladesh. Data on daily diarrhoea hospitalisations and meteorological variables from 1981–2010 were collected. We categorised heat index of >94.3°F, >100.7°F and >105°F as high, very high and extremely high heat index, respectively. We applied time-series adjusted generalised linear model (GLM) with negative binomial distribution to investigate the effects of heat index and extreme heat index on hospitalisations for diarrhoea. Effects were assessed for all ages, <5 children and by gender. A unit higher HI, and high, very high and extremely high HI were associated with 0.8%, 8%, 7% and 9% increase in diarrhoea hospitalisation in all ages, respectively. The effects varied slightly by gender and were most pronounced in <5 children with a rise of 1°F in high, very high and extremely high HI associated with a 14.1% (95% CI: 11.3% – 17.0%), 18.3% (95% CI: 13.4% – 23.5%) and 18.1% (95% CI: 8.4% – 28.6%) increase of diarrhoea, respectively. This suggest that heat index may serve as an alternative indicator for measuring the combined effects temperature and humidity on diarrhoea.

Background and Objectives: To investigate the relationship between the SII index and the micro-macro complications and death that develop in the first year and the following 3 years, following the calculation of the index in type 2 diabetic retinopathy patients. Materials and Methods: This study was performed retrospectively with 523 type 2 diabetic retinopathy patients admitted to the endocrinology outpatient clinic of our hospital between January 2019 and December 2019. The demographic, and clinical characteristics of the patients were evaluated using descriptive statistics. Shapiro-Wilk test was used for the normal distribution of quantitative data; Mann-Whitney U, Mc Nemar Chi-square, and Cochran's Q tests were used to analyze SII values between groups and complication rates by years; sensitivity and specificity were determined by ROC analysis and considered statistically significant with a significance level of P

Background Indicators to evaluate progress towards timely access to safe surgical, anaesthesia, and obstetric (SAO) care were proposed in 2015 by the Lancet Commission on Global Surgery. Despite being rapidly taken up by practitioners, datapoints from which to derive them were not defined, limiting comparability across time or settings. We convened global experts to evaluate and explicitly define - for the first time - the indicators to improve comparability and support achievement of 2030 goals to improve access to safe affordable surgical and anaesthesia care. Methods and findings The Utstein process for developing and reporting guidelines through a consensus building process was followed. In-person discussions at a two day meeting were followed by an iterative process conducted by email and virtual group meetings until consensus was reached. Participants consisted of experts in surgery, anaesthesia, and obstetric care, data science, and health indicators from high, middle, and low income countries. Considering each of the six indicators in turn, we refined overarching descriptions and agreed upon data points needed for construction of each indicator at current time (basic data points), and as each evolves over 2-5 (intermediate) and >5 year (full) timeframes. We removed one of the original six indicators (one of two financial risk protection indicators was eliminated) and refined descriptions and defined data points required to construct the 5 remaining indicators: geospatial access, workforce, surgical volume, perioperative mortality, and catastrophic expenditure. Conclusions To track global progress toward timely access to quality SAO care, these indicators – at the basic level - should be implemented universally. Intermediate and full evolutions will assist in developing national surgical plans, and collecting data for research studies.

Until recently, the classic approach to mineral exploration studies is to bring the field samples/drill cores collected during field studies to the laboratory followed by laborious analysis procedures to generate the analytical data. This is very expensive, time consuming and difficult for exploring vast areas. But rapid technological advances in field portable analytical instruments such as portable ultraviolet–visible and near-infrared spectrophotometers, gamma ray spectrometer, pXRF, pXRD, pLIBS, and µRaman spectrometer have changed this scenario completely and increased their on-site applications in mineral exploration studies. These instruments are currently providing direct, rapid, on-site, real-time, non-destructive, cost-effective identification, and determination of target elements, indicator minerals and pathfinder elements in rock, soil, and sediment samples. These portable analytical instruments are currently helping to obtain accurate chemical and mineralogical information directly in field with minimal or no sample preparation, and providing decision-making support during field work as well as during drilling operations in several successful mineral exploration programs. In this article, the developments in these portable devices, and their contributions in the platinum group elements (PGE), rare earth elements (REE), gold, base metals, and lithium exploration studies both on land and on ocean bed have been summarized with examples.

The aerospace industry develops prognosis and health management algorithms to ensure better safety on board. Particularly for in-flight controls where jamming is dreaded. For that, vibration signals are monitored to predict future defect occurrences. However, time series are not labeled according to severity level, and the user can only assess the system health from the data mining procedure. To that extent, we developed a clustering algorithm using a deep neural network core. We encoded the time series into pictures to be feed into an artificially trained neural network: U-NET. From the segmented output, one-dimensional information on cluster frontiers is extracted and filtered without any parameter selection. Then, a kernel density estimation finally transforms the signal into an empirical density. Ultimately, a Gaussian mixture model extracts the latter independent components. The method empowered us to reveal different degrees of severity faults in the studied data, with their respective likelihood without prior knowledge. We compared it to state-of-the-art machine learning algorithms. However, internal clustering results evaluation for time series is an open question. As state-of-the-art indexes were not producing relevant results, we built a new indicator to fulfill this task. We applied the whole method to an actuator consisting of an induction machine linked to a ball screw. This study lays the groundwork for future training of diagnosis and prognosis structures in the health management framework.

ProAKAP4, a precursor of AKAP4 (A-kinase anchor protein) found in the flagellum of mammalian and non-mammalian spermatozoa, serves as a structural protein with established correlations to motility parameters across diverse species. This study aimed to determine the proAKAP4 levels evolution in thawed stallion semen over a 3-hour period, examining its correlation with motility descriptors and mitochondrial membrane potential. Utilizing sixteen ejaculates from four French warmblood stallions, the study involved maintaining thawed samples at 37°C for 3 hours, conducting proAKAP4 enzyme-linked immunosorbent assay (ELISA), computer-assisted sperm analysis (CASA), and mitochondrial membrane potential by JC-1 probe and flow cytometry at 0, 1, and 3 hours post-thawing. The findings indicate significant positive correlations (p≤0.05) between proAKAP4 levels and sperm total or progressive motility at all time points analyzed. Spermatozoa velocity descriptors (VAP, VCL, VSL) and spermatozoa lateral head displacement (ALH) display positive correlations (p≤0.05) with ProAKAP4 at the 0-hour post-thawing. ProAKAP4 concentration exhibits no discernible difference between batches with or without cryoprotectant. Notably, proAKAP4 consumption remains insignificant within the initial hour after thawing but becomes significant (p≤0.05) between 1 and 3 hours post-thawing. In summary, proAKAP4 demonstrates positive correlations with total and progressive motility in stallion semen for up to 3 hours after thawing, albeit showing a noticeable decrease starting from the first hour post-thawing, indicating a progressive consumption as a result of spermatozoa motile activity.

In the last few years, researchers have paid increasing attention to singing voice evaluations.In their studies, they observed changes in the vibrations of the vocal folds during the transi-tion of registers. Additionally, they also found that these changes are less visible and audiblein the case of skilled singers. In order to confirm this theory we defined a new parameter,the Passaggio Peak Coefficient (PPC), obtained from an EGG signal to analyse pitch andopen quotient jump characteristics during the transition of vocal registers among 21 femaleand male choir members with different singing skills. The Kruskal-Wallis test proved thatit is possible to distinguish vocal skills, based on the ability to smoothen transitions amongfemale singers at a 5% significance level.

The Estimation of systemic risk in India is still in its infancy stage. There are several methods which are available but none of the methods are fully compatible to forecast the systemic risk since under different circumstances the factors responsible for the risk differs. In this paper the systemic risk estimation in India being carried out based on spread in daily stock market price(Difference between the bid and ask price of a share) of the top 100 firms in India according to market capitalization for the period of July2007 to March 2016. The results were compared with the Financial Stability Report published by Reserve Bank Of India for the period of March 2010 to June 2016.The results clearly indicates that there exits relationship between market illiquidity represented by spread and risks associated with the Financial System. In most of the cases the Z score (deviation from the mean/Standard Deviation) of the spread has become negative which provides the spread which is farther from the mean, also a good indicator of volatility in market and risk to financial system. It is also seen that the Systemic Risk Survey conducted by Reserve Bank of India which started during October 2011 has supported the results.

Scattering has been always considered a problem in most of the imaging and holography systems. In this project, a 3D imaging system has been developed based on scattering against common beliefs. The 3D imaging system consists of only two components namely a scattering lens, fabricated by grinding the surface of a convex lens using sandpaper and a web camera. The point spread function (PSF) in the form of speckle distribution was recorded using a laser source in the first step. A synthetic object was selected which was convolved with the PSF in a computer to generate the object intensity distribution. The image of the object was reconstructed by processing the PSF and object intensity distribution using a computational reconstruction method called non-linear reconstruction. The recorded PSF was scaled and the process was repeated for a different synthetic object. The concept was extended to 3D by summing the object intensity distributions generated using PSFs with different scaling factors. The image at different planes can be reconstructed using the PSFs corresponding to that plane.

The sonification of data to communicate information to a user is a relatively new approach that established itself around the 1990s. To date many researchers design their individual sonification from scratch. There are no standards in sonification design and evaluation. But researchers and practitioners have formulated several requirements and established several methods. There is wide consensus that psychoaocustics could play an important role in the sonification design and evaluation phase. But this requires an adaption of psychoacoustic methods to the signal types and the requirements of sonification. In this method paper we present PAMPAS, a PsychoAcoustical Method for the Perceptual Analysis of multidimensional Sonification. A well-defined and well-established, efficient, reliable and replicable Just Noticeable Difference experiment using the Maximum Likelihood Procedure serves as the basis to achieve linearity of parameter mapping during the sonification design stage and to identify and quantify perceptual effects during the sonification evaluation stage, namely the perceptual resolution, hysteresis effects and perceptual interferences. The experiment results are universal scores from a standardized data space and a standardized procedure. These scores can serve to compare multiple sonification designs of a single researcher, or even between different research groups. The method can supplement other sonification design and evaluation methods from a perceptual viewpoint.

In this work, a colorimetric indicator based on gold nanoparticles (AuNP) and a biodegradable and eco-friendly polymer (sodium alginate, Alg.), has been developed, for the real-time detection of fish spoilage products. AuNP and colorimetric indicator were characterized by: UV-Vis, FTIR spectroscopies, TGA, DSC, XRD, TEM, and colorimetry. The UV-Vis spectrum and TEM showed the successful synthesis, the spherical shape, and the size of AuNPs. Results indicated color changes of the indicator in packaged fish on day 9 of storage at refrigerated temperature (5ºC). These results showed the successful application of colorimetric indicator in the detection of TVB-N in packaged fish.

The prediction of system degradation is very important as it serves as an important basis for the formulation of condition-based maintenance strategies. An effective health indicator (HI) plays a key role in the prediction of system degradation as it enables vital information for critical tasks ranging from fault diagnosis to remaining useful life prediction. To address this issue, a method for monitoring data fusion and health indicator construction based on autoencoder (AE) and long short-term memory (LSTM) network is proposed to improve the predictability and effectiveness of health indicator in this study. Firstly, an unsupervised method and overall framework for HI construction is built based on deep autoencoder and LSTM neural network. The neural network is trained fully based on the normal operating monitoring data and then the construction error of the AE model is adopted as the health indicator of the system. Secondly, we propose related machine learning techniques for monitoring data processing to overcome the issue of data fusion, like mutual information for sensor selection and t-distributed stochastic neighbor embedding (T-SNE) for operating condition identification, etc. Thirdly, in order to verify the performance of the proposed method, experiments are conducted based on CMAPSS dataset and results are compared with algorithms of principal component analysis (PCA) and vanilla autoencoder model. Result shows that LSTM-AE model outperforms the PCA and Vanilla-AE model in metrics of monotonicity, trendability, prognosability and fitness. Fourthly, in order to analyze the impact of the time step of the LSMT-AE model on HI construction, we construct and analyze the system HI curve under different time steps of 5, 10, 15, 20, and 25 cycles. Finally, the results demonstrate that the proposed method for HI construction can effectively characterize the health state of system, which is helpful for further failure prognostics and converting the scheduled maintenance into condition-based maintenance.

The analysis and measurement of poverty is a crucial issue in the field of social science. Poverty is a multidimensional notion that can be measured using composite indicators relevant to synthesizing statistical indicators. Subjective choices could, however, affect these indicators. We propose interval-based composite indicators to avoid the problem, enabling us in this context to obtain robust and reliable measures. Based on a relevant conceptual model of poverty we have identified, we will consider all the various factors identified. Then, considering a different random configuration of the various factors, we will compute a different composite indicator. We can obtain a different interval for each region based on the distinct factor choices on the different assumptions for constructing the composite indicator. So we will create an interval-based composite indicator based on the results obtained by the Monte-Carlo simulation of all the different assumptions. The different intervals can be compared, and various rankings for poverty can be obtained. For their parameters, such as center, minimum, maximum, and range, the poverty interval composite indicator can be considered and compared. The results demonstrate a relevant and consistent measurement of the indicator and the shadow sector's relevant impact on the final measures.

Citizen science has the potential to support the delivery of the United Nations Sustainable Development Goals (SDGs) through its integration into national monitoring schemes. In this study, we explore the opportunities and biases of citizen science (CS) data when used either as a primary or secondary source for SDG 6.3.2 reporting. We use data from waterbodies that have both CS and regulatory monitoring in England and Zambia to explore their biases and complementarity. A comparative analysis of regulatory and CS data provided key information on appropriate sampling frequency, site selection and measurement parameters, necessary for more robust SDG reporting. The results show elevated agreement for pass/fail ratios and indicator scores for English waterbodies (80%) and demonstrate CS data can improve granularity and spatial coverage for SDG indicator scoring, even when extensive statutory monitoring programmes are present. In Zambia, management authorities are actively using citizen science projects to increase spatial and temporal coverage for SDG reporting. Our results indicate that design considerations for SDG focused citizen science can address local needs as well as provide a more representative indicator of the state of a nation’s freshwater ecosystems for international reporting requirements.

A lightweight image encryption algorithm based on chaos induction via a 5-dimensional hyperjerk oscillator (5DHO) is presented. First, the dynamics of our 5DHO network is investigated and shown to exhibit up to five coexisting hidden attractors in the state space that depend exclusively on the system’s initial values. Further, a simple implementation of the circuit was used to validate its ability to exhibit chaotic dynamical properties. Second, an Arduino UNO platform is used to confirm the usability of our oscillator in digital implementation of the system. Finally, an efficient image encryption application is executed using the proposed chaotic networks based on the use of permutation-substitution sequences. The superior qualities of the proposed strategy are traced to the dynamic set of keys used in the substitution process which heralds the generation of the final ciphered image. Based on the results obtained from the entropy analysis (7.9976), NPCR values (99.62), UACI tests (33.69) and encryption execution time for 512x512 images (0.1179 sec), the proposed algorithm is adjudged to be fast and robust to differential and statistical attacks relative to similar approaches.

Ukraine is an associate member of the European Union and in the coming years it is expected that all the data and services already used by European Union countries will become available for Ukraine. An important program, which is the basis for building European monitoring services for Smart Cities, is the Copernicus program. The two most important services of this program are Copernicus Land Monitoring Service (CLMS) and Copernicus Atmosphere Monitoring Service (CAMS). CLMS provides important information on Land Use in Europe. In the context of Smart Cities, the most valuable one is the Urban Atlas service, which is related to local CLMS services and provides a detailed digital city plan in vector form, which is segmented into small functional areas classified by the CORIN nomenclature. The Urban Atlas is a geospatial layer with high-resolution, which is built for all European cities with a population of more than 100,000 that combines high-resolution sat-ellite data, city segmentation by blocks and functional areas, important city infrastructure, etc. This product is used as a basis for city planning and obtaining analytics on the most important indicators of city development including air quality monitoring. For Ukraine, such geospatial products are not provided under the Copernicus program. It is important to start work on its development and implementation as early as possible, so that when the first city atlas appears, Ukraine will be ready to work with it together with the European community. This requires preparing the basis for na-tional research and training national stakeholders and users to use this product. To make this happen it’s necessary to have national geospatial product, which can be used as an analogue of the city atlas. In this article authors analyzed the existing methods of air quality assessment and assessment of the SDG indicator 11.6.2 achieving for European cities, based on which the indicator 11.6.2 for Ukraine for 5 years was evaluated for the first time. The obtained results are analyzed and the values of indicator 11.6.2 for Ukraine are compared with European countries.

Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. We suggest a new health indicator based on vibration data measurements and compare its performance with state-of-the-art health indicators regarding different criteria. This evaluation is based on different data sets from robot test rigs. We find that the proposed health indicator can detect different faults, has low temperature sensitivity and works in instationary velocity regimes. A discussion of the validity of the results rounds up our contribution.

The objective of this research was to create a Spanish-adapted version of the 30-item Multidimensional Ethics Scale (MES-30), analyze its structural validity, and create a short, valid, and culturally-adapted version. A total of 594 employees from organizations operating in different industries, primarily located in Ecuador, participated in this study. The comparison of different models using Confirmatory Factor Analysis (CFA) showed that the bifactor model proved better fit in all scenarios. The high common variance explained by the general factor (&gt; 80%) and the high values of ωh (&gt; .90) led to the conclusion that the explanatory model of the adapted MES-30 is essentially one-dimensional. In order to obtain a more valid and reliable measurement of the substantive common factor, a short version (Spanish MES-15) has been proposed, which contains items from all philosophical approaches and three non-redundant scenarios. The one-dimensional model for the Spanish MES-15 showed excellent goodness of fit in all scenarios. Regarding the metric equivalence between the original instrument (adapted MES-30) and the short one, the correlation between both measurements was greater than .90, the correlation of the short version with the social desirability scale was lower, and both instruments similarly predicted the intention of executing the actions described in the scenarios, the probability of executing the actions attributed to peers and supervisors, and the general ethical evaluation of those actions.

The intricate relationship between physical and social environments within organizations plays a pivotal role in shaping sustainable innovation endeavors. This paper presents a comprehensive analysis of two key dynamics that have garnered substantial scholarly attention for promoting sustainable innovation: the physical environment and the organizational climate. To elucidate the intricate interplay between these dynamics, we propose a novel three-dimensional framework that guides the modeling of the intricate processes through which the physical environment and innovation climate synergistically influence innovation outcomes. Building on this framework, we delve into an in-depth literature review concerning physical environments that exert a significant impact on innovation. Through this review, we identify and elaborate on four pivotal elements of the physical environment: communality, personalization, comfort, and healthiness. Leveraging insights gleaned from this review, we outline two promising avenues for future research in the realm of the physical environment's interaction with the innovation climate. Furthermore, we underscore the critical importance of adopting an interdisciplinary approach that seamlessly integrates insights from both the physical and social domains to comprehensively understand the sustainable innovation landscape.

There is great importance and need of improving existing carbon materials fabrication methods. As such, this work proposes to discuss, interrogate, and propose viable hydrothermal, solvothermal, and other advanced carbon materials synthetic methods. The advanced carbon materials to be interrogated will include the synthesis of carbon dots, carbon nanotubes, nitrogen/titania-doped carbons, graphene quantum dots, and their nanocomposites with solid/polymeric/metal oxide supports. This will be done with special mind to microwave-assisted solvothermal and hydrothermal synthesis due to their favourable properties such as rapidity, low cost, and green/environmentally-friendliness. Thus, these methods are important during the current and future synthesis and modification of advanced carbon materials for application in energy, gas separation, sensing, and water treatment. Simultaneously, the work will pay special cognizance to methods reducing the fabrication costs and environmental impact while enhancing the properties as a direct result of the synthesis methods. As a direct result, the expectation is to impart a significant contribution to the scientific body of work regarding the improvement of the said fabrication methods.

Construction projects are usually operating in a complex and dynamic environment in which the accumulation of many interrelated factors causes high uncertainty. Construction projects are complex and frequently involve substantial uncertainties including process complicatedness, intricate organization structure, dynamic environment, and financial strain. The study aims to categorize the influencing factors into three groups, namely construction project system, economic-market climate, and external environment. It attempts to adopt a novel analysis tool to examine the relationship between the project cost and multiple influencing factors by using Bayesian SEM. While the Bayesian SEM method has been receiving increasing attention in exploring the relationship between latent variables, construction studies still heavily rely on the covariance-based SEM approach. This study introduces several advantages of Bayesian SEM that make it more flexible and powerful than covariance-based SEM and provides the foundation of Bayesian SEM estimation and inference by illustrating this method in a project cost application.

Construction projects are usually operating in a complex and dynamic environment in which the accumulation of many interrelated factors causes high uncertainty. Construction projects are complex and frequently involve substantial uncertainties including process complicatedness, intricate organization structure, dynamic environment, and financial strain. The study aims to categorize the influencing factors into three groups, namely construction project system, economic-market climate, and external environment. It attempts to adopt a novel analysis tool to examine the relationship between the project cost and multiple influencing factors by using Bayesian SEM. While the Bayesian SEM method has been receiving increasing attention in exploring the relationship between latent variables, construction studies still heavily rely on the covariance-based SEM approach. This study introduces several advantages of Bayesian SEM that make it more flexible and powerful than covariance-based SEM and provides the foundation of Bayesian SEM estimation and inference by illustrating this method in a project cost application.

3D printing is commonly used to create complex objects at a low cost. It has also been applied in the fabrication of metamaterials and metasurfaces, which alter the propagation path of electromagnetic waves. These structures have diverse applications, including enhancing beam direction, improving antenna gain and signal-to-noise ratio, and creating filters and reflectors. Metasurfaces are cheap to fabricate and their use is expected to boost antenna efficiency in 5G telecommunications and beyond. However, accurately characterizing their profile remains a significant challenge. This paper presents a low-cost fully automated 4-axis instrument for microwave metasurface charac-terization, overcoming limitations of manual methods and offering an efficient and accurate approach. This research represents a significant advancement in microwave metasurface characterization, enabling sophisticated techniques to advance wireless communication technolo-gies, as it is the case of the future 5G and millimeter-wave communication systems. The equipment has an approximate cost of $1550 USD and was able to characterize a metalens with a focal distance of 20 cm designed for signals at 30 GHz. It was possible to create 2D and 3D profiles of the intensity distribution focused by the metalens and extract parameters such as the gain (8.05 dB), 3dB depth of focus (11 cm) and full width at half maximum (2.17 cm). The comparison of the measurements with the diffraction calculations of the metalens showed a very good agreement between the in-tensity distributions and the parameters of the metalens.

In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions which includes time, frequency, and space. Accordingly, recent literature uses tensor-based methods to exploit the multidimensional spectrum correlation. However, these methods share two main drawbacks. First, they are computationally complex. Second, they need to re-train the overall model when no information is received from any base station for any reason. Different than the existing works, this paper proposes a method for dividing the multidimensional correlation exploitation problem into a set of smaller sub-problems. This division is achieved through composite two-dimensional (2D)-long short-term memory (LSTM) models. Extensive experimental results reveal a high detection performance with more robustness and less complexity attained by the proposed method. The real-world measurements provided by one of the leading mobile network operators in Turkey validate these results.

Estimating the remaining useful life (RUL) of components is a crucial task to enhance the reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator ( ) to infer the current condition of the component, and modelling the degradation process, to estimate the future behavior. Although many signal processing and data-driven based methods were proposed to construct the , most of the existing methods are based on manual feature extraction techniques, and need the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the . For this purpose, the continuous wavelet transform (CWT) technique is used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model is trained by the healthy operation dataset. Finally, the Mahalanobis Distance (MD) between the healthy and failure stages is measured as the . The proposed method is tested on a benchmark bearing dataset and compared with several other traditional construction models. Experimental results indicate that the constructed exhibits a monotonically increasing degradation trend and has a good performance to detect incipient faults.

Scholars of the English Literature unanimously say that J.R.R. Tolkien and C.S. Lewis influenced each other writings. For the first time we have investigated this issue mathematically by using an original multi–dimensional analysis of linguistic parameters, based on surface deep–language variables and linguistic channels. To set our investigation in the framework of the English Liter-ature, we have considered some novels written by earlier authors, such as C. Dickens, G. Mac-Donald and others. The deep–language variables and the linguistic channels, discussed in the paper, are likely due to writers’ unconscious design and have revealed connections between texts far beyond writers’ awareness. In summary, the capacity of the extended short–term memory required to readers, the universal readability index of texts, the geometrical representation of texts and the fine tuning of linguistic channels within texts – all tools largely discussed in the paper – have revealed strong connections between The Lord of the Rings (Tolkien), The Chronicles of Narnia, The Space Trilogy (Lewis) and novels by MacDonald, therefore in agreement with what scholars of the English Literature say.

The human antibody response to influenza virus infection or vaccination is as complicated as it is essential for protection against flu. The constant antigenic changes of the virus to escape human herd immunity hinder the yearly selection of vaccine strains since it is hard to predict which virus strains will circulate for the coming flu season. A "universal" influenza vaccine that could induce broad cross-influenza subtype protection would help to alleviate this burden. However, the human antibody response is intricate and often obscure, with factors like antigenic seniority or original antigenic sin "OAS", and back-boosting ensuring that each person mounts a unique immune response to infection or vaccination with any new influenza virus strain. Notably, the effects of existing antibodies on cross-protective immunity after repeated vaccinations are unclear. More research is needed to characterize the mechanisms at play, but traditional assays such as hemagglutinin inhibition (HAI) and microneutralization (MN) are excessively limited in scope and too resource-intensive to effectively meet this challenge. In the past ten years, new multiple dimensional assays (MDAs) have been developed to help overcome these problems by simultaneously measuring antibodies against a large panel of influenza hemagglutinin (HA) proteins with a minimal amount of sample in a high throughput way. MDAs will likely be a powerful tool for accelerating the study of the humoral immune response to influenza vaccination and the development of a universal influenza vaccine.

Food security (FS) during infancy is associated with lifelong outcomes. New Zealand is a developed economy that reports poor childhood nutrition-related health statistics, particularly among minority children, yet has no measure of FS applicable to infancy. The objective was to develop a FS index for New Zealand infants and examine its association with demographic covariates and health outcomes. Within a large (n=6853) nationally representative cohort, variables describing infant food consumption, breastfeeding, and maternal food-related coping methods were collected from mothers during late infancy. A FS index was derived using confirmatory factor analysis. Associations were assessed by logistic regressions and described using odds ratios (OR) and ≥95% confidence intervals (CI). 15% of the cohort was highly FS, 43% tenuously food insecure (FIS), and 16% highly food insecure (FIS). Infants from minority ethnic groups had lower odds of being food secure, as did those born to the youngest mothers, mothers who smoked, or lived in low-income households. FIS infants had higher odds of morbidity. Interventions to improve infant FS should focus on improving dietary quality, and give particular consideration to minority infants. We identified that FIS shows wide ethnic and socioeconomic inequity, and is associated with poorer health. The most important driving factors of FIS included poor quality weaning diets, and poverty and its proxies. Any interventions to improve infant FS should focus on increasing fruit and vegetable consumption to recommended intake levels, and give particular consideration to minority infants.

A “watch and wait” strategy, delaying treatment until active disease manifests, is adopted for most CLL cases; however prognostic models incorporating biomarkers have shown to be useful to predict treatment requirement. In our prospective O-CLL1 study including 224 patients, we investigated the predictive role of 513 microRNAs (miRNAs) on time to first treatment (TTFT). In the context of this study six well-established variables (i.e., Rai stage, beta-2-microglobulin levels, IGVH mutational status, del11q, del17p, and NOTCH1 mutations) maintained significant associations with TTFT in a basic multivariable model, collectively yielding a Harrell's C-index of 75% and explaining 45.4% of the variance in the prediction of TTFT. Concerning miRNAs, 73 out of 513 were significantly associated with TTFT in a univariable model; of these, 16 retained an in-dependent relationship with the outcome in a multivariable analysis. For 8 of these (i.e., miR-582-3p, miR-33a-3p, miR-516a-5p, miR-99a-5p, and miR-296-3p, miR-502-5p, miR-625-5p, and miR-29c-3p) a lower expression correlated with a shorter TTFT, whereas in the remaining eight (i.e., miR-150-5p, miR-148a-3p, miR-28-5p, miR-144-5p, miR-671-5p, miR-1-3p, miR-193a-3p, and miR-124-3p) the higher expression was associated with shorter TTFT. Integrating these miRNAs into the basic model significantly enhanced predictive accuracy, raising the Harrell’C index to 81.1% and the explained variation in TTFT to 63.3%. Moreover, the inclusion of the miRNA scores enhanced the Integrated Discrimination Improvement (IDI) and the Net Reclassification Index (NRI), underscoring the potential of miRNAs to refine CLL prognostic models and providing insights for clinical decision-making. In silico analyses on the differently expressed miRNAs revealed their potential regulatory functions of several pathways, including those involved in the therapeutic responses. To add a bio-logical context to the clinical evidence, a miRNA-mRNA correlation analysis revealed at least one significant negative correlation between 15 of the identified miRNAs and a set of 50 Artificial Intelligence (AI) selected genes, previously identified by us as relevant for TTFT prediction in the same cohort of CLL patients. In conclusion, the identification of specific miRNAs as predictors of TTFT holds promise for enhancing risk stratification in CLL to predict therapeutic needs. However, further validation studies and in-depth functional analyses are required to confirm the robustness of these observations and to facilitate their translation into meaningful clinical utility.

Differential Evolution (DE) is an evolutionary algorithm that has become increasingly popular due to its effectiveness and efficiency. However, its adaptation for multi-objective optimization requires further investigation. To address this issue, a novel algorithm called ILSDEMO has been proposed, which incorporates indicator-based selection and local search into a self-adaptive DE. ILSDEMO employs an archive population to store non-dominated solutions, generating an initial population uniformly distributed over the feasible solution space using orthogonal design. Additionally, two variants of DE are used to expedite convergence, while q-Gaussian mutation is utilized to exploit better trial individuals. The k-nearest neighbor rule is used to eliminate crowded solutions, and indicator-based selection is employed to generate a new parent population without diversity preservation. The performance of ILSDEMO was investigated on the test instances from the ZDT series and DTLZ series in terms of the selected indicators. The results suggest that ILSDEMO accurately and evenly approximates the true Pareto front compared to NSGAII, IBEA, and DEMO.

Fluorescent labels, especially FRET probes, are promising tools for studying a number of bio-chemical processes. In this paper, we promoted 2 important applications of FRET probes (MUTMAC-R6G and FITC-R6G): i) the formation of micelles from surfactants of various natures and polymers (chitosan – fatty acid), as well as ii) monitoring of enzymatic activity with improved parameters (increased analytical signal and improved selectivity due to shift to the long-wavelength region). The formation of micelles is accompanied by the convergence of fluorophores in the hydrophobic micelle core by a distance closer than in the buffer solution, thus r/R0 (where R0 – Foerster radius) is chosen as an analytical signal of micelle formation, including critical micelle concentration (CMC) and critical pre-micelle concentration (CPMC). The CMC values calculated using FRET probes are in a good agreement with literature data. At the same time, the r/R0 function provides valuable information about the nature and mechanism of micelle formation. With the second analytical application of FRET probes, we considered the optimization of techniques for studying enzymatic activity. The enzyme catalyses the reaction with the release of a fluorescent product, the signal from which may not be enough for detection, or it may be quenched, for example, in the reverse micelles (AOT-octane). Here we have proposed a solution – a FRET probe containing a rhodamine 6G (R6G) acceptor, which allows us to monitor the enzymatic reaction selectively (in the red region, 550-600 nm), and obtain a significantly higher fluorescence yield (potentially from 10 to 250 times). Thus, we have demonstrated a high potential for the FRET probes application as indicators of micelle formation as well as for the study of the enzyme catalytic activity. In the future, the method developed has prospects for application in the visualization of the enzyme functioning in cells due to the shift of the fluorescence signal to the long-wavelength region with an increase in the signal selectivity to suppress autofluorescence.

The work is devoted to the search for effective solutions to the applied problem of early diagnostics of plant stress in the conditions of smart farming and based on modern explicable artificial intelligence (XAI). The study mostly oriented on the theory and practice of XAI, focused on the use of hyperspectral imagery (HSI) and Thermal Infra-Red (TIR) sensor data at the input of a neural network. The first our goal is to build an XAI neural network, explainable due to its structure, the input of which is a datascientist oriented HSI 'explanator', and the output is a biologist oriented TIR 'explanator'. In the middle is SLP-regressor which solves the universal problem of training HSI pixels to temperatures of plants, needed for early plant stress diagnostic. The result can be considered as prototype of a special XAI explanator which is assigned to transform explanator specialized on area 1 onto explanator specialized on area 2. Using this HSI-TIR explanator we ensured the follows: extend HSI data by TIR attribute; providing TIR data for early diagnostic of plant stress; reducing dimensionality HSI needed for TIR training 25 times (from 204 to 8) preserving the same accuracy of temperature prediction (RMSE=0.2-0.3C). This reducing was achieved without using PCA methods. The constructed model is computationally efficient in training: the average training time is significantly less then 1 min (Intel Core i3-8130U, 2.2 GHz, 4 cores, 4 GB). One of the 8 channels, 820 nm, is the leader in correlation with TIR, what allows building local linear temperature prediction functions.

The provenance of the aminoacyl-tRNA synthetases (aaRS) poses challenging questions because of their role in the emergence and evolution of genetic coding. We investigate evidence about their ancestry from curated structure-based multiple sequence alignments of a structurally invariant “scaffold” shared by all 10 canonical Class I aaRS. Three uncorrelated phylogenetic metrics—residue-by-residue conservation, its variance, and row-by-row cladistic congruence—imply that the Class I scaffold is a mosaic assembled from distinct, successive genetic sources. These data are especially significant in light of: (i) experimental fragmentations of the Class I scaffold into three partitions that retain catalytic activities in proportion to their length; and (ii) evidence that two of these partitions arose from an ancestral Class I aaRS gene encoding a Class II ancestor in frame on the opposite strand. Phylogenetic metrics of different modules vary in accordance with their presumed functionality. A 46-residue Class I “protozyme” roots the Class I molecular tree prior to the adaptive radiation of the Rossmann dinucleotide binding fold that refined substrate discrimination. Such rooting is consistent with near simultaneous emergence of genetic coding and the origin of the proteome, resolving a conundrum posed by previous inferences that Class I aaRS evolved long after the genetic code had been implemented in an RNA world. Further, pinpointing discontinuous enhancements of aaRS fidelity establishes a timeline for the growth of coding from a binary amino acid alphabet.

Gliomas are the most common primary brain tumors in adults. Despite multidisciplinary treatment approaches, the survival rates for patients with malignant glioma have only improved marginally, and few prognostic biomarkers have been identified. Peroxisome proliferator-activated receptor γ (PPARγ) coactivator-1α (PGC-1α) is a crucial regulator of cancer metabolism, playing a vital role in cancer cell adaptation to fluctuating energy demands. In this study, the clinicopathological roles of PGC-1α in gliomas were evaluated. Employing immunohistochemistry, cell culture, siRNA transfection, cell viability assays, western blot analyses, and in vitro and in vivo invasion and migration assays, we explored the functions of PGC-1α in glioma progression. High PGC-1α expression was significantly associated with an advanced pathological stage in patients with glioma and with poorer overall survival. The downregulation of PGC-1α inhibited glioma cell proliferation, invasion, and migration and altered the expression of oncogenic markers. These results conclusively demonstrated that PGC-1α plays a critical role in maintaining the malignant phenotype of glioma cells and indicated that targeting PGC-1α could be an effective strategy to curb glioma progression and improve patient survival outcomes.

In this work, we explore the extreme searching of multidimensional functions by natural gradient descent based on Dirichlet and generalized Dirichlet distributions. The natural gradient is based on describing multidimensional surface with probability distributions, which allows us to reduce changing the accuracy of gradient and step-size. In this article, we propose an algorithm of natural gradient descent based on Dirichlet and generalized Dirichlet distributions. We demonstrate that the natural gradient descent with step-size adaptation with Dirichlet and generalized Dirichlet distributions has higher accuracy and does not take a large number of iterations for minimizing test functions than gradient descent and Adam.

Live-streaming commerce (LSC) is a new shopping method that combines the characteristics of social commerce and e-commerce. Since the global coronavirus disease 2019 (COVID-19) outbreak, the number of branded platforms is growing rapidly, and their competition is fiercer than ever. Understanding consumer needs and improving service quality become the key issue for survival. This study aims to develop and empirically validate a multi-dimensional hierarchical model for measuring service quality on LSC platforms. A hierarchical reflective construct was proposed to capture dimensions based on literature on e-retail and social commerce service quality. The pro-posed model was rigorously tested using two waves of survey data through the partial least squares method. Results showed that the service quality of LSC is a third-order, reflective con-struct and includes five primary dimensions (the streamer’s interaction quality, physical envi-ronment, website quality, outcome quality and ordering process) and twelve sub-dimensions (trustworthiness, expertise, responsiveness, telepresence, consumption scenarios, information quality, system operation quality, fulfillment and refund/compensation, privacy/security, contact and ease of use). Findings also supported the hypothesis that service quality has a significant impact on customers’ satisfaction and their behavioral intentions. Furthermore, we tested an al-ternative model, and the results showed that the relationship between dimensions and overall assessment is reflective rather than formative. We offered directions for further research on LSC service quality and discussed managerial implications stemming from the empirical findings.

We aimed to assess whether play behavior can be used as a welfare indicator for farmed spotted pacas (Cuniculus paca) by examining its association with other positive welfare markers including affiliative behavior and low-amplitude vocalizations. We submitted six groups of spotted pacas (one male/two females per group) (N=18) to an ABA experimental design (A1/A2: without ball; B: with three boomer balls). Play behavior occurred only during phase B (mean=35.5s, SE=6.4). Spotted pacas spent more time in affiliative and exploratory behaviors, while they spent less time engaging in agonistic interactions during phase B than in both control phases (A1 and A2) (P<0.05). Moreover, spotted pacas emitted more low-amplitude bark vocalizations during phase B than during either control phase (P<0.05), which have previously been shown to indicate a positive affective state and low arousal level. Because the expression of play was associated with a decrease in aggression, an increase in affiliative behavior, and an increase in low-amplitude barking, indicating thus a positive state, we suggest that play behavior can be used as a non-invasive indicator of positive welfare in this species.

Candidatus Accumulibacter belongs to phosphate-accumulating organisms (PAO) which exhibit cyclic metabolism and are capable of intracellular polyphosphate accumulation and their hydrolysis under feast-famine anaerobic-aerobic cycling. In consortia of activated sludge microorganisms, these bacteria are responsible for enhanced biological phosphorus removal (EBPR). The spectrum of the substrates used by Ca. Accumulibacter remains insufficiently studied. It was investigated by measuring the oxygen uptake rates (OUR) of Ca. Accumulibacter-enriched culture supplemented with 17 different organic substrates. The highest oxygen uptake rates values were observed in the presence of tryptone, volatile fatty acids (acetate, propionate, and butyrate), succinate, pyruvate, and amino acids (aspartate and glutamate). Phosphate dynamics in the medium under shifts from anaerobic to aerobic cultivation in batch experiments was studied for these compounds (except for tryptone). All tested substrates were shown to cause phosphate cycling (release in the anaerobic phase and uptake in the aerobic one), with OURs for the substrates correlating with the amount of phosphates consumed during the aerobic phase. It was concluded that OUR may be used as an indicator of the monosubstrates used by Ca. Accumulibacter in the anaerobic/aerobic cycle. The possible pathways for substrate transport and metabolism by Ca. Accumulibacter are discussed using the stoichiometric data and the results of metagenomic analysis.

Few assessments and indicators on cultural sustainability have been applied to national and local development policies in Indonesia. Said policies have had the notion of space solely defined through its physical aspect. This study aims to establish the cultural aspect of space as a workable indicator for sustainability in development planning. The study grounds itself in the coastal area of Kalibaru Subdistrict, North Jakarta, a region characterized by dense and culturally diverse residential areas located in between heavy industrial development. Primary data are gathered from 10 informants of varying occupations with residence in the area for more than 20 years. In-depth interviews and Spatial Cognitive Mapping were conducted to articulate the cultural spaces of their everyday activities. Prevalent themes of spatial practice were identified and described in 3 categories of 1. core cultural spaces, 2. tactical cultural spaces, and 3. spaces of cultural conflict. The participatory procedures to identify cultural spaces as described in the study were concluded to be operable within existing indicator or index frameworks for sustainability policy. The availability of cultural spaces and its enumeration may be incorporated into national surveys for effective data collection and use.

Biodiversity loss has been identified as one of the environmental impacts where humankind has been tres-passing planetary boundaries most significantly. Going beyond the pressures causing damages (calling them ‘direct drivers’) and analysing their underlying driving forces, IPBES, the Intergovernmental Sci-ence-Policy Platform for Biodiversity and Ecosystem Services, also identified a series of indirect drivers. The Montreal-Kunming Global Biodiversity Framework GBF including its suggested monitoring approach is intended to and claims to be a policy response to such analyses. However, to assess the human impact on ecosystems as a basis for planning conservation and restoration, as foreseen in the GBF, monitoring ecosystem typologies (in the GBF with reference to the UN statistical standard SEEA ES, which in turn refers to the IUCN ecosystem classification) is not enough. It needs to be complemented with data on the severity of human impacts, and on the history of places, i.e. how and when the current ecosystem status was brought about. In this conceptual paper we suggest LUI, a deliberately simple ordinal scale index for land use intensity changes, to address these two gaps. It is based on the hemeroby concept, measuring the human impact as deviation from naturalness. This makes it an information collection and presentation tool for those working in landscape planning and management. LUI’s simple and intuitively understandable structure makes it suitable for citizens’ science applications, and thus for participative monitoring when extensive statistical data gathering is not feasible, and past data are not available. Of course is can also be used as a simple too for communicating when detailed statistical data series are available. While the aggregate index is expected to communicate well, its components are more relevant to motivate and help policy makers to prioritise their decisions according to the severity of recent anthropogenic ecosystem disturabances.

The European system of indicators is the result of the European Union sustainable development strategy, based on the objectives of economic prosperity, social equity, cohesion and environmental protection. In 2010 the EC defined four strategic priorities: encourage competitiveness in the tourism sector, promote sustainable high-quality tourism, strengthen the image of Europe as a tourism destination, and finally, improve EU financial policies and instruments. This paper aims to analyze in a positive critical perspective the experience done during the period 2013-2014 in the Italian destination Visit South Sardinia. This experience is in one hand an important primacy in Italy and in Europe, but in the other hand has not reached the expected objectives. Many difficulties were encountered both for data collection and for stakeholder involvement. Also the complexity of the indicators tool kit is relevant. Even if the experience of VSS did not produce the expected results, we believe that the analysis carried out in this paper can help and support other countries and or other destination in Europe in the process of implementation of the ETIS indicators tool kit in order to adapt the system itself to the needs that are encountered locally.

Introduction: This study aims to verify if elderly patients with mixed anxiety-depressive disorder are more prone to euthanasia and identify factors that interfere with their satisfaction with health and capacity for well-informed decisions. Material and Methods: The study applied a paper questionnaire composed of a sociodemographic section and a battery of scales (to assess depression, anxiety, cognitive performance, suicide risk, therapeutic adhesion, functionality, loneliness, attitude towards euthanasia, decision pattern, personality, empathy and health status) in the Psychogeriatric Unity of Senhora da Oliveira Hospital in Portugal. The sample was collected by convenience to include patients and controls of the same age. Six months later, a reassessment was done. Patients and controls were compared using descriptive statistics and a multiple-regression model.Results: A total of 114 patients and 25 controls were included. Eighty-one point six per cent of patients had four or fewer years of schooling. Contrary to controls, they presented mild depressive and anxiety symptoms, loneliness feelings, worse cognitive performance, a more fragile personality, higher personal distress and a poorer health state. No statistically significant differences were found between controls and patients regarding their attitudes towards euthanasia. Patients more favourable to euthanasia had higher empathic concern, conscientiousness and fantasy and lower personal distress.Discussion and Conclusion: When addressing euthanasia in these patients, it is crucial to ensure they are fully self-determinate and that all the necessary treatment and support are available. It may not be the case when the educational level is low and mild disease persists, significantly affecting their well-being and cognitive performance.

Social and behavioral rhythms (SBRs) are related to circadian rhythms well known as central points in the pathophysiology of bipolar disorders. The purpose is to verify in elderly people if having functional SBRs correlated with a positive perception of the quality of life (QoL) and health parameters and if there was an association between dysfunctionality of SBRs and Major Depressive Disorder (MDD). Sample: 141 people aged 65 years (58,9%Female). Each subject was submitted to SBRs Scale (in which higher scores show more dysfunctional SRB); SF-12 for QoL; PHQ-9 for depressive symptoms and underwent blood levels essays including cholesterol and triglyceride. The medical documented lifetime diagnosis including Major Depressive Disorder was taken into account. SBRs Scale score correlated inversely with SF-12 score (r=-0.360, p&lt;0.0001) and positively with PHQ9 (r=0.396, p&lt;0.0001). Lifetime MDD medical diagnosis was associated with a higher score at scale on social rhythms (24.811.9 vs 20.18.0, F=15.848, p&lt;0.0001).The study highlighted, for the first time, that SBRs such as sleep, and nutrition have a role in well-being and that dysregulation of SBRs is related to MDD. Further longitudinal studies with a sufficient number of individuals will have to confirm these data and clarify causal links of the association between QoL and SBRs.

State legitimacy and effectiveness could be seen by the way to deliver welfare to citizens to mitigate social grievances, that could eventually lead to conflicts (Kivimäki, 2021). Social security systems in Maghreb countries are quite similar in their architecture and aims to provide social insurance to all the workers in the labor market. However, they suffer from the same main problem: the low rate of enrollment of workers. Many workers (employees and self-employed) work informally without any social security coverage. The issue of whether informal jobs are chosen voluntarily by workers or as a strategy of last resort is controversial. Many authors recognize that the informal sector is heterogeneous and it is made up of workers who voluntary choose it and others who are pushed inside because of entry barriers to the formal sector (Günther & Launov, 2012). Using the SAHWA survey and discrete choice models, this article confirms the heterogeneity of the informal labor market in three Maghreb countries: Algeria, Morocco, and Tunisia. Furthermore, this article highlights the profiles of workers who voluntarily choose informality, which is missing from previous studies. Finally, this article proposes policy recommendations in order to extend social security to informal workers and to include them in the formal labour market.

Revealing the multidimensional value of cultivated land resources, improving calculation methods, and exploring their spatial distribution characteristics and influencing factors are crucial for the enhancement of natural resource accounting system and the protection of cultivated land resources. This study take Guangxi, China as an example and construct a comprehensive multidimensional value evaluation system for cultivated land resources at the county scale. The income capitalization method, substitution market method, and value equivalent correction method were applied to calculate the economic, social, and ecological values of cultivated land resources in 111 counties. Furthermore, spatial autocorrelation analysis was employed to study their distribution characteristics and influencing factors. The results show that: (1) The total value of cultivated land resources in Guangxi reach 19,729.2 billion yuan, and the average cultivated land value is 5.3511 million yuan/hm2, of which the ratio of economic, social and ecological values is about 6:1:2; (2) The value of cultivated land has a certain distribution pattern in the global space, and the average economic and social value show the spatial distribution patterns of "low in the west and high in the east" and "low in the northwest and high in the southeast" respectively."(3) There is a significant spatial clustering effect of all values of cultivated land resources in local space, with "high-high" and "low-low" clustering. (4) Economic development level is significantly negatively correlated with the ecological value of cultivated land, while has a significant positive correlation with social value; (5) Natural conditions, economic development and agricultural development approaches are the key factors affecting the value of cultivated land resources.

In this paper, the transmutation operator method is used to construct an exact solution to the initial boundary value problem for the equation of multidimensional free transverse vibration of a thin elastic plate with a singular Bessel operator acting on geometric variables. We emphasize that multidimensional Erdélyi–Kober operators of fractional order have the property of a transmutation operator, allowing one to transform more complex multidimensional partial differential equations with singular coefficients acting over all variables into simpler ones. If formulas for solutions are known for a simple equation, then we also obtain representations for solutions to the first complex partial differential equation with singular coefficients. In particular, it is successfully applied to the singular differential equations, especially with Bessel-type operators. Using this operator, the considered problem is reduced to a similar problem without the Bessel operator. Based on the solution to the auxiliary problem, an exact solution to the original problem is constructed and analyzed.

This article proposes a monitoring system that allows to track transitions between different stages in the berry harvesting process (berry picking, waiting for transport, transport, and arrival to the packing) solely using information from temperature and vibration sensors located in the basket. The monitoring system assumes a characterization of the process based on Hidden Markov Models and uses the Viterbi algorithm to perform inference and estimate the most likely state trajectory. The obtained state trajectory estimate is then used to compute a potential damage indicator in real-time. The proposed methodology does not require information about the weight of the basket to identify each of the different stages, which makes it effective and more efficient than other alternatives available in the industry.

Soil moisture can be an important indicator of climate change in humid and semi-arid areas. This indicator can more efficiently propose different public policies related to climate change than just using precipitation and temperature data. Given the above, the objective of this study is to evaluate changes in soil moisture in the state of Pernambuco during the period 1961-2021, using the System of Hydrological Response Units for Pernambuco. In this study, two river basins in the state of Pernambuco that represent the different climatic conditions of the state will be chosen. The results show that in the coastal region, there is a tendency towards more saturated soils, and in the semi-arid region, there is a tendency towards drier soils. With these results, it is possible to conclude that public policy decisions for the economy, environment, and society must consider this vital water balance variable.

Escherichia coli or E. coli is a member of the fecal coliform group and is a more specific indicator of fecal contamination than other fecal coliform species, its presence indicate possibly presence of harmful bacteria which will cause diseases and it also suggests the extent as well as the nature of the contaminants. E. coli bacteria able to survive in water for 4 – 12 weeks and at present, it appears as an indicator to provide the accurate bacterial contamination of fecal matter in drinking water, because of the availability of simple, affordable, fast, sensitive and exact detection techniques. According to the laboratory experiment based techniques, 24 - 48 hours are required for the bacterial concentration to be reported. So, there is a necessity for continuous monitoring. Techniques for detection of many pathogenic bacterial strains are not yet available, sometimes days to weeks are required to get the results. To overcome the difficulties, expensive and time-consuming techniques are required to detect, count and identify the presence of specific bacterial strain. Public health relies on online monitoring of water quality that depends majorly on examination of fecal indicator bacteria, thus protection of health requires fecal pollution indicator so that it is not required to analyze drinking water to overcome the problems associated with waterborne diseases. This paper will brief the classification, sources, survival of E. coli bacteria and its correlation with basic water quality parameters in water sources.```

Droughts occurring more and more frequently in recent years are usually associated with progressive climate changes. Modern monitoring methods is mainly associated with agricultural use due to the high market demand for services protecting against agricultural losses. In natural wetland ecosystem this problem is neglected which is probably due to fact that this are ecosystems rich in water by definition. Nevertheless this droughts might become a problem of wetlands, whose good condition depends on adequate hydration. Therefore this study focused on determination Crop Water Stress Index (CWSI) for natural heterogeneous wetland habitats. CWSI based on remotely sensed land surface temperature and associated basic meteorological (air temperature and humidity) measurements. CWSI was calculated based on high resolution LST measurements from UAV platform and compare with on ground measurements of soil moisture, CCI, fAPAR and meteorological conditions. Results shows that CWSI properly shows water condition of plants in measured habitats and revels high importance of metrological conditions during data acquisition.

Target detection of defense technology is being rapidly upgraded with modern surveillance technologies. The latest techniques of surveillance are already being implemented for defense applications. Self-protection and hiding from opposing forces are the key principle for protection of special team in defense. Camouflage textiles aims to create confusing objects for target detection of military personnel. These textiles are applied for military protection such as clothing, weapon, vehicle and location hiding nets/tents, etc. The urgent need of camouflage textiles have been formulated with a technical solution and implementation of right camouflage materials for concealment of defense target signature against dry leaves, green leaves, tree bark-woodland combat background; water-marine combat background; sand-desertland combat background; stone-stoneland combat background; snow-snowland combat background; sky combat background and ice-iceland combat background, concrete-concreteland combat background (DGTWSICB) in ultraviolet-visible-infrared (UV-Vis-IR) spectrums. This hypothesis of optical & surveillance engineering has been coalesced for advancement of UV-Vis-IR-DGTWSICB camouflage textiles technology. The principle of camouflage engineering has been approached by broader spectrum probe in UV-Vis-IR rather than Vis ranges only. Furthermore, single formulation of camouflage textiles has been proposed for multidimensional CBs-DGTWSICB. Electromagnetic spectrum, reflection, electron energy, photonic signal and imaging mechanism in UV-Vis-IR have been presented for optical engineering of concealment, detection, recognition and identification of target signature against DGTWSICB. Spectrum relationship of camouflage materials and DGTWSICB materials have been illustrated and compared in UV-Vis-IR spectrums. Camouflage material design, method design & spectral design; adaptive camouflage; techniques for camouflage textile assessment for digital camera and hyperspectral camera imaging; image processing techniques and a hierarchical model have been demonstrated for augmentation of camouflage textiles in UV-Vis-IR illumination. Therefore, anticipated design of camouflage textiles may enhance high-performance innovation for modern surveillance of military protection related to digital camera, hyperspectral camera and radar. This hypothesis includes advance guideline for camouflage textiles design for CBs-DGTWSICB.

Frailty is defined as a state of increased vulnerability to stressors, and it predicts the disability and mortality in the older population. This study aimed to investigate standardized prevalence and multidimensional risk factors associated with frailty among the Korean community-dwelling older adults. We analyzed the baseline data of 2,907 adults aged 70–84 years (mean age 75.8±3.9 years, 57.8% women) in the Korean Frailty and Aging Cohort Study. The Fried frailty phenotype was used to define frailty. Analyzed data included sociodemographic, physical, physical function, biological, lifestyle, health condition, medical condition, psychological, and social domains. Data were standardized using the national standard population composition ratio based on the Korean Population and Housing Census. The standardized prevalence of frailty and pre-frailty was 7.9% (95% confidence interval [CI] 6.8–8.9%) and 57.2% (95% CI 45.1–48.8%), respectively. The following 14 risk factors had a significant association with frailty: at risk of malnutrition, sarcopenia, severe mobility limitation, poor social capital, rural dwellers, depressive, poor self-perceived health, polypharmacy, elevated high-sensitivity C-reactive protein, elevated glycosylated hemoglobin, low 25-hydroxy vitamin D level, longer timed up and go, and low short physical performance battery score (p<0.05). Physico-nutritional, psychological, sociodemographic, and medical factors are strongly associated with frailty.

Background: Strong indicators of inflammation, such as C-reactive protein (CRP), hypersensitive CRP (hs-CRP), and a series of hematological indices, including platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), hematocrit (HCT) and red blood cell distribution width (RDW), are regarded related with the incidence of contrast induced nephropathy (CIN) closely. Whereas, it remains unclear whether they can function as predictors of CIN onset. The objective of this meta-analysis was to determine the relationship between above indicators and CIN incidence among patients receiving coronary intervention. Methods: Clinical studies were retrieved from the electronic databases of PubMed, EMBASE, Google Scholar, Clinical Trials, and science direct from their inception to June 3rd, 2020. Meta-analysis was performed on pool eligible studies. Two reviewers screened all titles and abstracts and independently assessed all articles. Results: A total of 26 studies involving 29,454 patients were included in the meta-analysis. Pooled analysis results revealed that patients with higher CRP (odds ratio [OR]=1.06, 95% confidence interval [CI]: 1.01–1.12, P=0.02), hs-CRP (OR=1.03, 95% CI: 1.01–1.06, P=0.004), NLR (OR=1.11, 95% CI: 1.01–1.20, P=0.02), RDW (OR=1.35, 95% CI: 1.19–1.53, P<0.00001), and lower HCT (OR=0.94, 95% CI: 0.92–0.97, P=0.0003) all exhibited significantly higher CIN rates, but there was no significant association between PLR and CIN risk (OR=1.12, 95% CI: 0.99–1.26, P=0.07). Conclusion: The meta-analysis reported here demonstrates that pre-angiography CRP/hs-CRP and some hematological indices are associated with CIN.

End-stage heart failure (ESHF) leads to hypoperfusion and edema formation in the whole body and is accompanied by neurohormonal and immunological alterations. Orthotopic heart transplantation (HTX) has been used as a beneficial option for ESHF. Due to the shortage of donor hearts, the ideal matching and timing of donors and recipients has become more important. Our aim was to explore the relationship between the clinical outcome of HTX and the cytokine and apolipoprotein profiles of the recipient pericardial fluid obtained at transplantation after opening the pericardial sac. The clinical data and the interleukin, adipokine and lipoprotein levels in the pericardial fluid of twenty recipients were investigated. Outcome variables included primer graft dysfunction (PGD), need for posttransplantation mechanical cardiac support (MCS), International Society for Heart and Lung Transplantation grade ≥ 2R rejection and mortality. Recipient risk scores were also investigated. Leptin levels were significantly lower in patients with PGD than in those without PGD (median: 6.36 [IQR: 5.55-6.62]; versus 7.54 [IQR=6.71-10.44]; p = 0.029). Higher ApoCII levels (median: 14.91 [IQR: 11.55-21.30] versus 10.31 [IQR=10.02-13.07]; p = 0.042) and ApoCIII levels (median: 60.32 [IQR: 43.00-81.66] versus 22.84 [IQR=15.84-33.39]; p=0.005) were found in patients (n=5) who died in the first 5 years after HTX. In patients who had rejection (n=4) in the first month after transplantation, the levels of adiponectin (median: 74.48 [IQR: 35.51-131.70] versus 29.96 [IQR: 19.86-42.28]; p=0.039), ApoCII (median: 20.11 [IQR: 13.06-23.54] versus 10.32 [IQR: 10.02-12.84]; p=0.007) and ApoCIII (median: 70.97 [IQR: 34.72-82.22] versus 26.33 [IQR: 17.18-40.17; p=0.029) were higher than those in the nonrejection group. Moreover, the pericardial thyroxine (T4) levels (median: 3.96 [IQR: 3.49-4.46] versus 4.69 [IQR: 4.23-5.77]; p=0.022) were lower in patients with rejection than in patients who did not develop rejection. Our results indicate that apolipoproteins can facilitate the monitoring of rejection and could be a useful tool in the forecast of early and late complications.

High levels of air pollution can contribute to high rate of the COVID-19 outbreaks. Air pollutants induce oxidative stress, inflammatory process, immune imbalance and coagulation at systemic level, making the organism susceptible to complications caused by various pathogens, including viruses, resulting in a possible important damage co-factor. Sperm cells are highly sensitive to the pro-oxidant effects of environmental pollutants, and may represent an important alarm bell indicating that the burden of environmental pressure in a certain area is causing damage to humans. A comparison of the maps of COVID-19 case fatality rates, male infertility rates and air pollution may suggest a way to understand the dynamics of the virus impact. Semen quality may be considered as an early and sensitive environmental marker, and also a potential susceptibility indicator to viral insults (including SARS-CoV-2 ) in heavily polluted areas. Therefore, assessing the burden of environmental exposure of a given population and its potential susceptibility to insults through early biological stress indicators may be helpful for predicting the risk of the adverse effects by the SARS-CoV-2 epidemic .

Here, we propose a novel scheme based on advanced techniques of digital modulation in optical communications to achieve a single-channel transmission rate above 100 Gb/s. We utilize a hybrid scheme amplitude/phase/frequency/dual polarization, combined with multidimensional dual lattice and a low-density parity-check-coded modulation. The Stokes parameters are applied to the proposed scheme to map the four-dimensional classical polarization \({I_X},{Q_X},{I_Y},{Q_Y}\) in a three-dimensional space. In addition, in the proposed system, the packing theory is applied to the bit interleaver process, three wavelengths are packaged before being transmitted over a wavelength-division multiplexing optical channel. This modulation process is carried out using symmetrical geometric shapes, such as a hypercube or a polyhedron, based on the molecular links theory using a grouping of 12 and 13/15 bits for the cubic and spherical lattices, respectively. The proposed technique is evaluated in the context of long distance communications over distances up to 100 km. The bit error rate results showed that the optical signal-to-noise ratio was approximately 4 dB over a distance of 50 km. In addition, the power spectral efficiency was found to be 3 lambda, which is considered good performance considering the effects of distance and the non-linear effects influencing the number of lambdas. Also, we use an optical time division multiplexing scheme (OTDM) in order to achieve a transmission rate beyond 1Tbit/s, where the speed effect is evaluated, taking into consideration that the power spectral efficiency is degraded. Furthermore, the average noise floor is -60dBm for all transmission distances. These findings are expected to contribute to the future implementation of robust long-distance optical communication infrastructure.

Acute kidney injury (AKI) is a common complication post-cardiac surgery, impacting short- and long-term survival. Free light chains (FLCs) are being explored as markers for assessing kidney damage risk. A study aimed to validate early AKI detection by studying FLCs (kappa, lambda, and kappa/lambda ratio) in serum and saliva samples. Clinical data was collected from 149 patients who underwent cardiac surgery in Baghdad. Ninety of these patients provided saliva samples in addition to serum samples for free light chain evaluation. Patients were grouped by proteinuria severity. Serum kappa levels were significantly higher in severe proteinuria patients compared to mild or no proteinuria. Salivary kappa levels were significantly higher in mild proteinuria patients compared to severe or no proteinuria. Salivary lambda levels decreased with increasing proteinuria severity. Serum kappa increase could be due to immune system activation and kidney compromise. Both serum and salivary kappa showed good predictive ability for post-surgery AKI prognosis compared to lambda and the kappa/lambda ratio, depending upon ROC performance. The study suggests FLCs, especially kappa, could be valuable in early AKI detection post-cardiac surgery, indicating potential for improved patient outcomes through proactive monitoring and intervention.

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics and knowledge describing the human presence on the planet based mainly on two quantitative factors: i) the spatial distribution (density) of built-up structures and ii) the spatial distribution (density) of resident people. Both factors are observed in the long-term temporal domain and per uniform surface units in order to support trends and indicators for monitoring the implementation of international framework agreements. The GHSL uses various input data including global, multi-temporal archives of fine-scale satellite imagery, census data, and volunteered geographic information. In this paper, we present the characteristics of GHSL information to demonstrate how original frameworks of data and tools rooted on Earth Observation could support Sustainable Development Goals monitoring. In particular, we demonstrate the reach of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for the Sustainable Development Goal 11. Our experiments produce a global estimate for the Land Use Efficiency indicator (SDG 11.3.1) for 10,000 urban centers, calculating the ratio of land consumption to population growth rate that took place between 1990 and 2015. The results of our research demonstrate that there is a potential to lift SDG 11.3.1 from a tier 2 as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata.

Under the wave of technological advancement and industrial upgrading, new productivity is deeply influencing the field of vocational education and promoting fundamental changes in teachers' roles. Modern teachers must not only keep pace with technological development but also master innovative teaching modes and guide students to form correct values. They are gradually evolving from mere knowledge transmitters to guides for learning, stimulators of interest, explorers of in-depth research, and promoters of comprehensive growth. Through interdisciplinary knowledge structures and innovative abilities, combined with advanced tools such as information technology and artificial intelligence, teachers innovate teaching methods to meet students' diverse learning needs and focus on cultivating students' professional skills, comprehensive qualities, and innovative spirits. This multi-dimensional reshaping of roles is not only an inevitable choice for the development of vocational education but also provides important insights for educational reform.

The relocation of Indonesia's capital to the IKN (Ibukota Negara) Nusantara in East Kalimantan is leading to significant changes in land use, shifting from natural vegetation and agriculture to urban infrastructure. This transition brings about economic diversification and urban expansion, but it also raises concerns about its impact on society, the economy, and the environment. The rapid development affects biodiversity conservation, food security, and the livelihoods of rural and indigenous communities, leading to conflicts across social and economic dimensions. This research uses qualitative and quantitative data to examine the socio-economic and environmental changes in the IKN Nusantara area from 2003 to 2023. The findings show a notable increase in built-up areas, indicating urbanization and a decrease in agricultural land. The study discusses the implications for local populations and ecosystems, emphasizing the need for inclusive governance, community participation, and conflict resolution. It also proposes a comprehensive policy framework that promotes sustainable land management, recognizes indigenous and local rights, and fosters inclusive economic growth to respect Indonesia's rich environmental and cultural heritage.

This study introduces a fruit harvesting mechanism powered by a single motor, designed for integration with unmanned aerial vehicles (UAVs). The mechanism performs reciprocating motion by converting linear motion into rotational motion. Consequently, the end-effector can execute multi-dimensional kinematic trajectories, including biaxial and rotational movements, synchronized with the motor's position. These axial and rotational motions facilitate the gripper's ability to reach, retrieve, and detach fruit from branches during the harvesting process. Notably, a critical consideration in designing this fruit harvesting mechanism is to generate the necessary torque at the end-effector while minimizing reaction forces and torque that could destabilize the UAV during flight. With these considerations in mind, this preliminary study aimed to harvest a Fuji apple and conducted dynamic analysis. We constructed a prototype of the single motor-driven fruit harvesting mechanism using a suitable servo motor. To assess its mechanical performance and evaluate its impact on the hexacopter, we developed both a specific test platform featuring a six-spherical-prismatic-spherical parallel structure and a virtual environmental flight simulator. Overall, the results demonstrate the successful harvesting of a Fuji apple weighing approximately 300 g by the single motor-driven fruit harvesting mechanism, with no adverse effects observed on the hexacopter's operation.

In conclusion, this qualitative exploration has unveiled the profound and essential impact of music on psychological well-being, especially in the post-pandemic world. Through in-depth literature reviews, informal interviews, and observations with thirty CAS participants from SMC, the researcher uncovered ten salient themes that collectively illustrate music's multifaceted role as a potent remedy. These findings showcase how music serves as an emotional outlet, a cognitive enhancer, and a source of inspiration. The emotional depth and spiritual resonance participants associate with music underscore its capacity to address diverse human needs, making it an indispensable tool in navigating the complexities of our post-pandemic reality. Moreover, the cognitive benefits observed, such as improved concentration and focus, are particularly pertinent in the context of remote work and education, highlighting music's necessity in our daily lives. Additionally, this study emphasizes the vital role of virtual music communities in fostering social connections, even amidst physical isolation, further underscoring the enduring relevance of music in these challenging times. This research contributes to the expanding knowledge of music's impact on well-being, offering a holistic perspective encompassing spiritual, emotional, social, and cognitive dimensions. It enriches existing music psychology theories and invites further exploration into the mechanisms underpinning music's diverse effects. These insights have practical implications for fields such as mental health, education, and remote work, where music can be utilized as an essential tool for stress reduction and emotional regulation. This is highlighted further in the Suggested songs that can help sought the mind, body, spirit and soul. Looking ahead, future research could delve deeper into the long-term impact of music engagement on well-being and explore the universality of music's benefits through cross-cultural studies. Overall, music, as illuminated by this study, stands as a timeless and indispensable companion, offering solace, inspiration, vitality, and connection in the face of any contemporary challenges.

The concept of Emotional Intelligence (EI) has gained prominence due to its substantial impact on a range of life outcomes, including academic performance. The present study aims to examine the relationship between Emotional Intelligence and academic accomplishment in the context of a school environment. Analyzing students' emotional skills and competencies makes it possible to uncover the potential of emotional intelligence (EI) in predicting their academic achievement. Additionally, the study emphasizes the importance of integrating emotional intelligence (EI) training within the educational curriculum. Developing students' emotional awareness, empathy, self-regulation, and social skills can yield notable academic benefits, cultivating an environment more favorable to learning. This systematic review examines the relationship between emotional intelligence and its impact on children and teachers by synthesizing several relevant research studies. This study encompasses a comprehensive review conducted from 2016 to 2023. A total of 64 publications were examined in this study, specifically chosen based on their relevance to the review's parameters. These articles were sourced from reputable academic databases, including Scopus, PsycINFO, PubMed, and WoS. The study revealed a correlation between the five dimensions of personality and emotional intelligence, creativity, resilience, the educational framework, and specific experimental programs about its education. The results mentioned above are of great significance, as they demonstrate that emotional intelligence (EI) is not solely a non-essential skill but a vital factor in education that can provide significant perspectives for students, educators, and politicians.

One of the key challenges of stock trading is the stock prices follow a random walk process, which is a special case of a stochastic process, and are highly sensitive to new information. A random walk process is difficult to predict in the short-term. Many linear process models that are being used to predict financial time series are structural models that provide an important decision boundary, albeit not adequately considering the correlation or causal effect of market sentiment on stock prices. This research seeks to increase the predictive capability of linear process models using the SPDR S\&P 500 ETF (SPY) and the CBOE Volatility (VIX) Index as a proxy for market sentiment. Three econometric models are considered to forecast SPY prices: (i) Auto-Regressive Integrated Moving Average (ARIMA), (ii) Generalized Auto Regressive Conditional Heteroskedasticity (GARCH), and (iii) Vector Autoregression (VAR). These models are integrated into two technical indicators, Bollinger Bands and Moving Average Convergence Divergence (MACD), focusing on forecast performance. The profitability of various algorithmic trading strategies is compared based on a combination of these two indicators. This research finds that linear process models that incorporate the VIX Index do not improve the performance of algorithmic trading strategies.

Ethiopian agriculture is not only affected by precipitation declines (meteorological droughts) but also soil dryness caused by temperature increases and associated long-term hydrological changes. Meteorological drought indicators (e.g., SPI), do not fully capture the water deficits in agricultural systems (i.e., agricultural droughts). An Ethiopia-wide assessment of meteorological and agricultural drought trends was carried out to characterize century-scale (1902 – 2016) changes in droughts. SPI and SPEI calculated using two-month accumulation and the Palmer Z-index were used for assessing intra-season drought trends. SPI and SPEI at six-month accumulations and PDSI were used to define full season droughts. Detrended variance corrected Mann-Kendall test was used for trend analysis during Bega (dry), Belg (short-rainy) and Meher (long-rainy) seasons. The SPEI-2 and PDSI were most aggressive in characterizing intra-season and seasonal-drought trends. There is on average 1% - 6% annual increase in dryness with the lower estimate based on precipitation declines and the upper end accounting for seasonal soil moisture dynamics. The area between 37.5° E – 42.5° E denotes a climate hot-spot. Precipitation declines in Belg along the Ethiopia-South-Sudan/Sudan border during Belg and along Eretria-Ethiopia border during Meher have the potential to exacerbate transboundary water conflicts and further threaten the food security of the region.

Behavioural disorders in dogs are common and have severe welfare consequences for dogs. This study aimed to assess the factors that are significant and predictive of behaviour problems in dogs using the Animal Welfare Assessment Grid (AWAG) to further understand what factors influence their welfare. 177 AWAG assessments were undertaken across 129 dogs that clinicians deemed to have a behavioural disorder. Wilcoxon-rank sum tests were used to assess the difference in scores between dogs with behaviour disorders and a cohort of healthy dogs (n=117). This analysis showed that all physical factors besides body condition, all procedural factors besides procedure pain, and all psychological, and environmental factors were significantly different between healthy dogs and dogs with behaviour disorders. Spearman Rank Correlation Coefficient (RS) revealed several significant strong positive correlations including the procedural impact on the dog’s daily routine with aggression towards unfamiliar people and procedure pain, as well as other correlations between the dog’s behaviour during assessment with the frequency at which they encounter fears and anxieties, clinical assessment and procedure pain, and reaction to stressors and social interactions. These findings highlight the interdependent nature of the various influences of welfare. Logistic regression analysis identified that aggression towards the caregiver, fears and anxieties frequency, and choice, control, and predictability were all significant predictors of behaviour disorders. The findings have important implications for veterinary, behaviour, and animal welfare professionals as any changes across these factors may indicate poor welfare linked to emotional disorders in dogs.

Breakthrough cancer pain (BTcP) is a temporary exacerbation of pain that "breaks through" a phase of adequate pain control by an opioid-based therapy. The non-predictable BTcP (NP-BTcP) is a subtype of BTcP that occurs in the absence of any specific activity. Since NP-BTcP has an important clinical impact, this analysis is aimed at characterizing the NP-BTcP phenomenon through a multidimensional statistical technique. This is a secondary analysis based on the Italian Oncologic Pain multiSetting - Multicentric Survey (IOPS-MS) . A correlation analysis was performed to characterize NP-BTcP profile about its intensity, number of episodes per day, and type. The Multidimensional Correspondence Analysis (MCA) determined the identification of 4 groups (Phenotypes). A univariate analysis was performed to assess differences between the 4 Phenotypes and selected covariates. The four phenotypes represent the hierarchical classification according to the status of NP-BTcP: from the best (Phenotype 1) to the worst (Phenotype 4). The univariate analysis found a significant association between the onset time &gt;10 min in the Phenotype 1 (37.3%) vs. the onset ≤ 10 min in Phenotype 4 (74.2%) (p&lt;0.001). The Phenotype 1 was characterized by gastrointestinal type of cancer (26.4%) respect to Phenotype 4 where the most frequent cancer affected the lung (28.8%) (p&lt;0.001). Phenotype 4 was mainly managed with rapid onset opioids, while in Phenotype 1 many patients were treated with oral, subcutaneous, or intravenous morphine (56.4% and 44.4%, respectively; p=0.008). The ability to characterize NP-BTcP can offer enormous benefits for the management of this serious aspect of cancer pain. This strategy can provide many indications for identifying the diagnostic and therapeutic gaps on NP-BTcP management.

Three tiny bones compose the human ossicular chain: malleus, incus and stapes. Also known as auditory ossicles, they are united by joints in the middle ear cavity of the petrous part of the temporal bone. Completely developed 2 years after birth, the ossicular chain is involved in the physiological process of hearing, by which sound waves from the environment are converted into electrochemical impulses. In the last 500 years, most studies have focussed on the morphogenesis, morphological variability and clinical pathology of the ossicular chain, whilst only a few studies have added relevant knowledge to anthropology and forensic science. The auditory ossicles and the enclosing petrous bone are some of the hardest in the human skeleton. This reflects in a relative resistance to fire. In the post mortem period and in favourable circumstances, the auditory ossicles can fossilize and survive for millions of years. Post mortem loss in skeletal or decomposed remains can be ascribed to their destruction or physical displacement, by either macro-micro-faunal action and/or any other natural or artificial disturbance. Physical displacement is closely connected to the depositional environment of the skeletal remains, such as burial, entombment (sarcophagus, coffin, vault…), submersion or exposure to natural elements. Auditory ossicles can be recovered in situ, or very close to their anatomical location, when the skeletal material has been involved in an archaeological excavation. In the case of accessible or disturbed remains, scavengers may remove the tiny ossicles, and/or they can slip out of the middle ear cavity following skull movements. Entombment offers an effective protection against the displacement of the auditory ossicles, whereas aquatic submersion and aquatic movement almost invariably displace them. The preservation of the human auditory ossicles should be critically considered in the comprehensive context of any forensic investigation on human remains, since it can assist the reconstruction of their taphonomic history. Taphonomic histories of remains can add crucial information to forensic investigations (e.g., the Post Mortem Interval, PMI). The aim of this study, limited by scarce relevant literature, is to discuss the potential role of the ossicular chain, detected by post mortem imaging techniques, as a taphonomical indicator in decomposing and/or skeletonized bodies.

Abstract: Autophagy has a dual role in gliomagenesis in a microRNA-modulated environment. We investigated the potential relevance of autophagy in glioma development and survival by exploring the association of autophagy-associated genes and microRNAs in low- and high-grade gliomas. Real-time PCR (qPCR) was used to determine the expression of genes and microRNAs in 50 fresh glioma tissues while Formalin-fixed paraf-fin-embedded tissues of the same patients were used for immunohistochemistry. The Mann-Whitney U-test test, Spearman correlation test, and Kaplan-Meier survival analysis were performed to evaluate the expression, association, and overall survival in patients respectively. The expression of LC3, AKT, and miR-21 was increased in high-grade glioma compared to low-grade glioma while ULK2 expression was decreased in high-grade glioma. A strong positive correlation was observed for ULK2 with UVRAG, PTEN, miR-7, and miR-100, while the moderate correlation with mTOR, Beclin1, miR-30, miR-204, miR-374, miR-21 and miR-126 in low-grade glioma, while a moderate positive correlation between ULK2 and PI3K, PTEN, ULK1, VPS34, mTOR, Beclin1, UVRAG, AKT and miR-374, and between AKT and ULK1, VPS34, UVRAG, and miR-7 in high-grade gliomas. The low ULK2 and LC3 expression group was significantly associated with better overall survival in gliomas while miR-21 overexpression showed a poor prognosis in glioma patients. Therefore, miR-21, ULK2, and LC3 may serve as prognostic biomarkers for survival outcomes in glioblastoma.