Set Of Objects Research Articles

THE QUALITY OF ENVIRONMENTAL FACTORS AND ORGANIC AGRICULTURE

Greenhouse gas emissions are the main cause of the global climate crisis. The EU is the fourth largest emitter of greenhouse gases in the world, after China, the United States and India, which determined the setting of the Community objective of reducing emissions by 55% by the year 2030 and reaching climate neutrality by 2050. In Romania, agriculture ranks second in terms of greenhouse gas emissions and can play an important role in ensuring its own climate neutrality and can contribute to neutralising the effects produced by other anthropogenic activities. The quality of environmental factors influences the quantity, but especially the quality of food. Organic agriculture is an essential tool in protecting the quality of the environment, by conserving soil, water quality and supporting biodiversity, but also a tool in ensuring a healthy diet. The paper is intended to be an analysis of the quality of the main environmental factors and evolution of organic agriculture in Romania.

Preliminary Analysis of Simple Novelty Search.

Novelty search is a powerful tool for finding diverse sets of objects in complicated spaces. Recent experiments on simplified versions of novelty search introduce the idea that novelty search happens at the level of the archive space, rather than individual points. The sparseness measure and archive update criterion create a process that is driven by a two measures: (1) spread out to cover the space while trying to remain as efficiently packed as possible, and (2) metrics inspired by k nearest neighbor theory. In this paper, we generalize previous simplifications of novelty search to include traditional population (μ,λ) dynamics for generating new search points, where the population and the archive are updated separately. We provide some theoretical guidance regarding balancing mutation and sparseness criteria and introduce the concept of saturation as a way of talking about fully covered spaces. We show empirically that claims that novelty search is inherently objectiveless are incorrect. We leverage the understanding of novelty search as an optimizer of archive coverage, suggest several ways to improve the search, and demonstrate one simple improvement-generating some new points directly from the archive rather than the parent population.

SceneDirector: Interactive Scene Synthesis by Simultaneously Editing Multiple Objects in Real-Time.

Intelligent tools for creating synthetic scenes have been developed significantly in recent years. Existing techniques on interactive scene synthesis only incorporate a single object at every interaction, i.e., crafting a scene through a sequence of single-object insertions with user preferences. These techniques suggest objects by considering existent objects in the scene instead of fully picturing the eventual result, which is inherently problematic since the sets of objects to be inserted are seldom fixed during interactive processes. In this article, we introduce SceneDirector, a novel interactive scene synthesis tool to help users quickly picture various potential synthesis results by simultaneously editing groups of objects. Specifically, groups of objects are rearranged in real-time with respect to a position of an object specified by a mouse cursor or gesture, i.e., a movement of a single object would trigger the rearrangement of the existing object group, the insertions of potentially appropriate objects, and the removal of redundant objects. To achieve this, we first propose an idea of coherent group set which expresses various concepts of layout strategies. Subsequently, we present layout attributes, where users can adjust how objects are arranged by tuning the weights of the attributes. Thus, our method gives users intuitive control of both how to arrange groups of objects and where to place them. Through extensive experiments and two applications, we demonstrate the potentiality of our framework and how it enables concurrently effective and efficient interactions of editing groups of objects.

Medics as influencers: a historical analysis of British Army military medical exercises in Kenya over two decades

IntroductionAnnual British Army medical training exercises have run in Kenya since the early 1990s, initially with a dual purpose—to deliver the Kenyan Extended Programme of Immunisation (in remote locations) and...

Implementation and evaluation of a SARI surveillance system in a tertiary hospital in Scotland in 2021/2022

ObjectiveTo set up and evaluate a new surveillance system for severe acute respiratory infection (SARI) in Scotland. Study designCross-sectional study and evaluation of surveillance system. MethodsThe SARI case definition comprised patients aged 16 years or over with an acute respiratory illness presentation requiring testing for influenza and SARS-CoV-2 and hospital admission. Data were collected from SARI cases by research nurses in one tertiary teaching hospital using a bespoke data collection tool from November 2021 to May 2022. Descriptive analyses of SARI cases were carried out. The following attributes of the surveillance system were evaluated according to Centers for Disease Control and Prevention (CDC) guidelines: stability, data quality, timeliness, positive predictive value, representativeness, simplicity, acceptability and flexibility. ResultsThe final surveillance dataset comprised 1163 records, with cases peaking in ISO week 50 (week ending 19/12/2021). The system produced a stable stream of surveillance data, with the proportion of SARI records with sufficient information for effective surveillance increasing from 65.4% during the first month to 87.0% over time. Similarly, the proportion where data collection was completed promptly was low initially, but increased to 50%–65% during later periods. ConclusionSARI surveillance was successfully established in one hospital, but for a national system, additional sentinel hospital sites across Scotland, with flexibility to ensure consistently high data completeness and timeliness are needed. Data collection should be automated where possible, and demands on clinicians minimised. SARI surveillance should be embedded and resourced as part of a national respiratory surveillance strategy.

Call-Graph-Based Context-Sensitive Points-to Analysis for Java

Pointer analysis or points-to analysis (PTA) is a static program analysis for variables in a program, which determines a set of heap objects that individual variables may refer to at run time. In the literature, various types of context-sensitive analyses have been applied to improve the precision of PTA. In this article, we propose a framework that unifies existing context-sensitive PTA methods, under which we further explore more efficient ways for points-to calculation. In particular, we propose a call-graph-based context generation algorithm that combines the object-sensitive PTA and parameter-sensitive PTA approaches, and we implement the algorithm in the Soot compiler framework. Our new algorithm generates contexts for methods in a more complete and effective way, and it has been shown to achieve better precision with fewer generated contexts and less execution time than some of the known state-of-the-art context-sensitive approaches for PTA when tested with a selection of benchmarks from the DaCapo suite.

Risk management practices and financial performance nexus: Evidence from MSMEs in Osun State, Nigeria

This study assessed the effect of enterprise risk management on the firm financial performance of Micro, Small and Medium-scale Enterprises in Osun state Nigeria. Precisely, the risk encountered, the adoption of risk management practices, the implementation of Enterprise Risk Management (ERM) practices, and the financial performance of micro, small and medium scale enterprises in Osun state Nigeria. Primary data was used for this study; a well-structured questionnaire was used to elicit data from 273 respondents. The study population consists of owners and managers of small and medium businesses in Osun state, Nigeria. Data were analyzed using descriptive analysis and was the inferential statistic used to test the hypotheses. The results showed that market (33.7%), strategic (57%), financial (46.9%), operational (34.2%), management (47.6%), and technological (45.4%) are risks prevalent to micro and small businesses, but most of the respondents disagreed that the relationship of their staff to the customer is poor. Risk acceptance with a mean score of (2.27 ± 1.21) is the most utilized risk management practice adopted by the MSMEs. The implementation of ERM practices shows that MSMEs more utilized setting of objectives (43.6%), risk identification (38.3%), risk assessment (36.2%), and control activities (31.2%) and the financial performance of the businesses was moderate (69.7%). The correlation analysis shows no significant relationship between the scale of business and the adoption of risk management practices. Also, the regression analysis shows that Risk identification (? = 0.388, p ?0.01) and Control activities (? = 1.096, p ?0.01) are the only ERM variables that significantly affect financial performance. The study concluded that the implementation of enterprise risk management practices has a significant effect on the financial performance of Micro, Small and Medium enterprises in Osun State, Nigeria.

Multi-Object Detection using Enhanced YOLOv2 and LuNet Algorithms in Surveillance Videos

Multiple object tracking (MOT) in videos benefits multiple applications, including robot navigation, video surveillance, video analytics, and intelligent transportation systems. Although significant progress has been made since early studies, visual tracking of many objects remains challenging because of frequent occlusions in measurements, environmental noise, changeable number of objects, and appearance similarity across objects. The proposed work focused on three significant processes, feature extraction, object detection, and classification, to identify moving objects before sharing information. This work proposes a multi-object video detection method using LuNet and deep reinforcement learning. The enhanced “you only look once” version 2 (YOLOv2) initially detects numerous objects. In this work, a base network of the YOLOv2 changed by lowering the metrics and substituting it with LuNet. In the enhanced model, the LuNet network is used for feature extraction to extract the most expected characteristics from the image. Furthermore, the proposed model is compact because of the underlying network's LuNet architecture. To demonstrate the proposed technique's performance, this method compares it to numerous state-of-the-art algorithms on the MOT20 vehicle benchmark dataset. The proposed method achieves a higher % classification accuracy of 94% for moving object settings. The experiments demonstrate that the proposed method outperforms existing models in terms of performance and accuracy.

Designing pareto-optimal selection systems for multiple minority subgroups and multiple criteria.

Currently used Pareto-optimal (PO) approaches for balancing diversity and validity goals in selection can deal only with one minority group and one criterion. These are key limitations because the workplace and society at large are getting increasingly diverse and because selection system designers often have interest in multiple criteria. Therefore, the article extends existing methods for designing PO selection systems to situations involving multiple criteria and multiple minority groups (i.e., multiobjective PO selection systems). We first present a hybrid multiobjective PO approach for computing selection systems that are PO with respect to (a) a set of quality objectives (i.e., criteria) and (b) a set of diversity objectives where each diversity objective relates to a different minority group. Next, we propose three two-dimensional subspace procedures that aid selection designers in choosing between the PO systems in case of a high number of quality and diversity objectives. We illustrate our novel multiobjective PO approaches via several example applications, thereby demonstrating that they are the first to reveal the complete gamut of eligible PO selection designs and to faithfully capture the Pareto trade-off front in case of more than two objectives. In addition, a small-scale cross-validation study confirms that the resulting PO selection designs retain an advantage over alternative designs when applied in new validation samples. Finally, the article provides a link to an executable code to perform the new multiobjective PO approaches. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Fast Clustering With Anchor Guidance.

Clustering aims to partition a set of objects into different groups through the internal nature of these objects. Most existing methods face intractable hyper-parameter problems triggered by various regularization terms, which degenerates the applicability of models. Moreover, traditional graph clustering methods always encounter the expensive time overhead. To this end, we propose a Fast Clustering model with Anchor Guidance (FCAG). The proposed model not only avoids trivial solutions without extra regularization terms, but is also suitable to deal with large-scale problems by utilizing the prior knowledge of the bipartite graph. Moreover, the proposed FCAG can cope with out-of-sample extension problems. Three optimization methods Projected Gradient Descent (PGD) method, Iteratively Re-Weighted (IRW) algorithm and Coordinate Descent (CD) algorithm are proposed to solve FCAG. Extensive experiments verify the superiority of the optimization method CD. Besides, compared with other bipartite graph models, FCAG has the better performance with the less time cost. In addition, we prove through theory and experiment that when the learning rate of PGD tends to infinite, PGD is equivalent to IRW.

Quantitative Research Design and Sample Trends: A Systematic Examination of Emerging Paradigms and Best Practices

Research design and sampling are pivotal in scientific research, shaping methodology and ensuring the credibility and validity of findings, necessitating clear and focused attention from researchers. Based on the above statement the study was focused on underlining the research design's basic terms and sample in order for researchers to be reliable in their research. The study used PRISMA to select the sample of documents. 30 major studies were used to explain the research objectives. The study covers only four types of research design i.e. Descriptive, Developmental, Experimental, and Casual Comparative. Results highlight the four research designs that aim to answer, words to be concerned with during objective setting, statistical tools, data visualization, words to be selected during hypothesis setting based on tools, and the minimum number of samples required depending on various tools. The research concludes that the choice of effective research design terms is pivotal. In statistical analysis, hypothesis wording directs research, with terminology so depending on the tools appropriate words need to be selected. Researchers need to make sure that the minimum sample size is right. However, larger samples improve the stability and accuracy of results. This study contributes by explaining essential research design terminology on sample selection, and their influence on statistical analysis, providing researchers with a clear guide to improve credibility and validity in their work.

The DECam Ecliptic Exploration Project (DEEP). VI. First Multiyear Observations of Trans-Neptunian Objects

We present the first set of trans-Neptunian objects (TNOs) observed on multiple nights in data taken from the DECam Ecliptic Exploration Project. Of these 110 TNOs, 105 do not coincide with previously known TNOs and appear to be new discoveries. Each individual detection for our objects resulted from a digital tracking search at TNO rates of motion, using two-to-four-hour exposure sets, and the detections were subsequently linked across multiple observing seasons. This procedure allows us to find objects with magnitudes m VR ≈ 26. The object discovery processing also included a comprehensive population of objects injected into the images, with a recovery and linking rate of at least 94%. The final orbits were obtained using a specialized orbit-fitting procedure that accounts for the positional errors derived from the digital tracking procedure. Our results include robust orbits and magnitudes for classical TNOs with absolute magnitudes H ∼ 10, as well as a dynamically detached object found at 76 au (semimajor axis a ≈ 77 au). We find a disagreement between our population of classical TNOs and the CFEPS-L7 three-component model for the Kuiper Belt.

A signal-detection account of item-based and ensemble-based visual change detection: A reply to Harrison, McMaster, and Bays.

Growing empirical evidence shows that ensemble information (e.g., the average feature or feature variance of a set of objects) affects visual working memory for individual items. Recently, Harrison, McMaster, and Bays (2021) used a change detection task to test whether observers explicitly rely on ensemble representations to improve their memory for individual objects. They found that sensitivity to simultaneous changes in all memorized items (which also globally changed set summary statistics) rarely exceeded a level predicted by the so-called optimal summation model within the signal-detection framework. This model implies simple integration of evidence for change from all individual items and no additional evidence coming from ensemble. Here, we argue that performance at the level of optimal summation does not rule out the use of ensemble information. First, in two experiments, we show that, even if evidence from only one item is available at test, the statistics of the whole memory set affect performance. Second, we argue that optimal summation itself can be conceptually interpreted as one of the strategies of holistic, ensemble-based decision. We also redefine the reference level for the item-based strategy as the so-called "minimum rule," which predicts performance far below the optimum. We found that that both our and Harrison et al. (2021)'s observers consistently outperformed this level. We conclude that observers can rely on ensemble information when performing visual change detection. Overall, our work clarifies and refines the use of signal-detection analysis in measuring and modeling working memory.

Human-autonomy collaboration in supervisory risk control of autonomous ships

This paper presents a method for developing and testing a risk-based control system, as a first step towards including the human supervisor explicitly in the design of the system. The result is a control system with improved decision-making capabilities compared to existing control systems. The methodology presented in the paper uses the Systems Theoretic Process Analysis (STPA) to analyse the risks of an autonomous ship within its concept of operations (CONOPS), and a Human-STPA (H-STPA) is used to analyse human responsibilities and involvement. The STPA results are then used to construct a Bayesian belief network (BBN)-based risk model to assess the operational risk of the ship. This is represented as a risk cost, describing the expected cost of consequences caused by potential hazardous events. This cost is combined with fuel costs, operations costs, and the potential loss of income if new missions are not undertaken using a supervisory risk controller (SRC). The SRC is capable of making decisions about how the ship should be safely operated and notifies the human supervisor in due time when it is necessary for them to take control. The last part of the methodology presented in this paper is testing the control system using a set of verification objectives based on results from the STPA and H-STPA. A case study involving an autonomous cargo ship with a human supervisor located in a remote operation center (ROC) is included; it shows that the proposed control system can operate the ship safely in different conditions and situations. By designing the SRC to notify the human supervisor before it reaches its operational limit, the ship is able to operate in a wider range of conditions compared to when just the autonomous control system is in charge. Hence, the proposed methodology shows promising results and provides useful insights related to shared control for autonomous ships.

The greener the living environment, the better the health? Examining the effects of multiple green exposure metrics on physical activity and health among young students

The sedentary and less active lifestyle of modern college students has a significant impact on the physical and mental well-being of the college community. Campus Green Spaces (GSs) are crucial in promoting physical activity and improving students' health. However, previous research has focused on evaluating campuses as a whole, without considering the diverse spatial scenarios within the campus environment. Accordingly, this study focused on the young people’s residential scenario in university and constructed a framework including a comprehensive set of objective and subjective GSs exposure metrics. A systematic, objective exposure assessment framework ranging from 2D (GSs areas), and 2.5D (GSs visibility) to 3D (GSs volume) was innovatively developed using spatial analysis, deep learning technology, and unmanned aerial vehicle (UAV) measurement technology. Subjective exposure metrics incorporated GSs visiting frequency, GSs visiting duration, and GSs perceived quality. Our cross-sectional study was based on 820 university students in Nanjing, China. Subjective measures of GSs exposure, physical activity, and health status were obtained through self-reported questionnaires. The Generalized Linear Model (GLM) was used to evaluate the associations between GSs exposure, physical activity, and perceived health. Physical activity and social cohesion were considered as mediators, and path analysis based on Structural Equation Modeling (SEM) was used to disentangle the mechanisms linking GSs exposure to the health status of college students. We found that (1) 2D indicator suggested significant associations with health in the 100m buffer, and the potential underlying mechanisms were: GSs area → Physical activity → Social cohesion → Physical health → Mental health; GSs area → Physical activity → Social cohesion → Mental health. (2) Subjective GSs exposure indicators were more relevant in illustrating exposure-response relationships than objective ones. This study can clarify the complex nexus and mechanisms between campus GSs, physical activity, and health, and provide a practical reference for health-oriented campus GSs planning.

An effective multiphysics toolkit for lithium prospecting: From geophysics to the static reservoir model in the Pozuelos salt flat, Argentina

Energy transition drives the energy sector to renewable energy and electrification, being the critical minerals key players in the industrial development map. They comprise rare-earth elements and 35 other elements, including lithium, which holds 60% of its world reserves in the so-called lithium triangle located in Argentina, Bolivia, and Chile. The low electrical resistivities, variations in salt concentrations, low acoustic impedances, and dynamics of the hydrogeologic system make brine monitoring a complex geophysical exploratory problem. So, the objective is to find a suitable combination of geophysical techniques that fit the lithium exploration objectives, which are the characterization of the salt flat in-depth, fluid detection, basement delineation, definition of the main structures and main faults, and detection of semifresh water aquifers that contribute to its recharge and that are key to the reservoir water balance of an endorheic basin, which has the resource in solution. For this purpose, the evaluation of several prospecting methods in different salt flats was executed, concluding that full tensor magnetotellurics (MT), electrical resistivity tomography, and gravity comprise a toolkit that fits the objectives set. The methodology was validated at the Pozuelos salt flat. The results show that the fresh water-brines contact and the recharge system were well-defined and imaged by ERT. The full tensor MT detected two ultraconductive units: a shallower multilayer system saturated with brines, has a 400 m thickness, whereas the deeper unit has a 500 m thickness. Both MT and gravity characterized the basement, and gravity successfully delineated the main structures. The geophysical interpretation is in concordance with shallow and deep exploration wells. Finally, the integration of geophysical and well data allowed the construction of a 3D static reservoir model that finds the deepest basement area at approximately 900 m depth and discriminates eight lithofacies.

Outlier detection using flexible categorization and interrogative agendas

Categorization is one of the basic tasks in machine learning and data analysis. Building on formal concept analysis (FCA), the starting point of the present work is that different ways to categorize a given set of objects exist, which depend on the choice of the sets of features used to classify them, and different such sets of features may yield better or worse categorizations, relative to the task at hand. In their turn, the (a priori) choice of a particular set of features over another might be subjective and express a certain epistemic stance (e.g. interests, relevance, preferences) of an agent or a group of agents, namely, their interrogative agenda. In the present paper, we represent interrogative agendas as sets of features, and explore and compare different ways to categorize objects w.r.t. different sets of features (agendas). We first develop a simple unsupervised FCA-based algorithm for outlier detection which uses categorizations arising from different agendas. We then present a supervised meta-learning algorithm to learn suitable (fuzzy) agendas for categorization as sets of features with different weights or masses. We combine this meta-learning algorithm with the unsupervised outlier detection algorithm to obtain a supervised outlier detection algorithm. We show that these algorithms perform at par with commonly used algorithms for outlier detection on commonly used datasets in outlier detection. These algorithms provide both local and global explanations of their results.

Historical Perspective. The Constitution of the Fifth Republic: a Spirit, Institutions and Practice?

It can be argued that the Constitution of the Fifth Republic is an asset for France, if only because of its longevity. Besides, given the objective set by its founders in 1958, namely to restore the state around the executive, the Fifth Republic is undoubtedly a success. However, this success has led to an imbalance in the functioning of the institutions, which, despite attempts at reform, remain essentially centred on the head of state. The article addresses this issue through the prism of three fundamental issues: ‘the spirit’ of the Constitution, focused on the reconstruction of the State; ‘institutions’, unequal in favour of the President of the Republic; and ‘practice’, sometimes unconstitutional.

Double-Constraint Fuzzy Clustering Algorithm

Given a set of data objects, the fuzzy c-means (FCM) partitional clustering algorithm is favored due to easy implementation, rapid response, and feasible optimization. However, FCM fails to reflect either the importance degree of the individual data objects or that of the clusters. Numerous variants of FCM have been proposed to address these issues. However, most of them cannot effectively apply the available information on data objects or clusters. In this paper, a double-constraint fuzzy clustering algorithm is proposed to reflect the importance degrees of both individual data objects and clusters. By incorporating double constraints into each data object and cluster, the objective function of FCM is reformulated and its realization equation is mathematically conducted. Consequently, the clustering accuracy of FCM is improved by applying the available information on both data objects and clusters. Especially, the proposed algorithm effectively addresses the limitations inherent in the existing variants of FCM. The experimental results validate the effectiveness, implementation, and robustness of the new fuzzy clustering algorithm.

Jewelry, Accessories, and Decorative Elements of Women’s Funeral Costume of the First Half of the 6th Century BCE in the Territory of Forest-Steppe Scythia

Among the antiquities of the archaic period of Forest-Steppe Scythia, a group of elite burials of women, possibly endowed with priestly functions during their lifetime, stands out. Until recently, only two unrobbed burial complexes were known to contain the main burials of women of high social rank, in whose graves golden costume elements were found—primarily expressive details of headdresses. The barrows (kurgans) were discovered at the end of the 19th century when amateur excavations were actively carried out on the right bank of the Dnipro. As a result of research conducted by the author at the Skorobir necropolis (in the area of the Bilsk fortified settlement, on the left bank of the Dnipro), two similar graves were recently discovered, which provided new material that significantly expanded the known geographical distribution of this phenomenon. The materials are closely analogous to the previously discovered elite female burials of the Middle Dnipro (barrow 100 near the village of Syniavka, barrow 35 near the village of Bobrytsa) and allow us to highlight a number of stable elements of the funeral costume of noble women and the sets of objects that complemented them. In this article, we consider the social and cultural significance of female attire in elite burials and delimit the chronological framework of this previously understudied phenomenon within the first half of the 6th century BCE. The new finds offer unprecedented insight into the form and meaning of one type of female headdress which researchers have tried to reconstruct for over a century.