Precise Rules Research Articles

Co-evolutive Resilience: The Hidden Identity of Phoenicia

Finiq is the biggest town in its municipality and has served as an administrative center for various years. Most of the dwellings are placed during the 1990-2000 decade, referring to the previous and the current maps. Because of massive emigration trends in the past, there are many abandoned and unfinished houses. The uninhabited villages that have been abandoned in recent decades make up a serious problem, which is all too common in many Balkan countries. As a result, various buildings are semi-finished and in a degraded state, both architectural and structural. Also, on top of the hill of Finiq, there is an archeological site of the old Phoenicia city. It serves as the main touristic attraction for the town, along with the bucolic scene of the countryside. By analyzing the territory of Finiq we figured out that there is a hidden identity of the urban structure. As the town lays morphologically on the strip, it fragmentises from the second and third row of buildings, showing a “missed typological block”. On the other hand, it shows the creation of spontaneous empty spaces, that suggest this hidden spirit of the town, created imaginatively by connecting them. The simple disposal of each building to the nearest one creates what Franco Purini calls the “border distance”, a distance that, like in Michelangelo’s fresco, creates a necessary situation for one to feel the magnetic force of attraction or repulsion between the two bodies. The space where this magnetic force is exerted is the key point from which to draw the hidden spirit of Finiq. It is precisely the shape of that space that will determine the rules for the evolution of the town's identity. We will analyze in this paper how this space "between" can “co-evolve” if the institutions and the people who live there find a common language to reinvent a new but existing architecture of the place. As spotted in the analysis, we can confirm that there is an identity in Finiq. A “hidden spirit” lies in empty and fragmented spaces. It’s a millennial town, therefore something stronger than the constructions exists there. The resilience of the context’s form, space, and time coexist in lost identity, which we can strongly expose by intervening surgically. the co-evolution of these public spaces must start from precise rules which, through the careful analysis of the architect and urban planner, must be able to direct the inevitable spontaneity of the place’s history, without having a drastic top- down effect.

Architectural underpinnings of stochastic intergenerational homeostasis.

Living systems are naturally complex and adaptive and offer unique insights into the strategies for achieving and sustaining stochastic homeostasis in different conditions. Here we focus on homeostasis in the context of stochastic growth and division of individual bacterial cells. We take advantage of high-precision long-term dynamical data that have recently been used to extract emergent simplicities and to articulate empirical intra- and intergenerational scaling laws governing these stochastic dynamics. From these data, we identify the core motif in the mechanistic coupling between division and growth, which naturally yields these precise rules, thus also bridging the intra- and intergenerational phenomenologies. By developing and utilizing techniques for solving a broad class of first-passage processes, we derive the exact analytic necessary and sufficient condition for sustaining stochastic intergenerational cell-size homeostasis within this framework. Furthermore, we provide predictions for the precision kinematics of cell-size homeostasis and the shape of the interdivision time distribution, which are compellingly borne out by the high-precision data. Taken together, these results provide insights into the functional architecture of control systems that yield robust yet flexible stochastic homeostasis.

Deep fakes and the Artificial Intelligence Act—An important signal or a missed opportunity?

AbstractThe Artificial Intelligence Act (AI Act) adopted by the European Union might serve as a global regulatory reference point. Heated negotiations over the AI Act have shown that reconciling the interests of numerous stakeholders is not an easy task. As well as creating clear and precise rules that would enable implementing effective safeguards for citizens against the manipulative potential of technology. The AI Act introduces the first legal definition of deep fakes and creates a system of protection against their harmful applications, that is based on transparency obligations. I argue that in the course of negotiations, the EU has made progress in regulating deep fakes, but the adopted solutions should only be an introduction to a more strict protection of individuals. The AI Act targets a specific group of deep fakes, leaving some of them unregulated or poorly regulated and disregarding the consequences of nonconsensual deep fake pornography. By analyzing the legal provisions related to deep fakes in the AI Act, I point to the rationale behind the chosen forms of protection, criticize the shortcomings and list the consequences of adopting the AI Act for deep fakes landscape.

Interval-valued intuitionistic fuzzy three-way conflict analysis based on cumulative prospect theory

This paper makes a significant contribution to the field of conflict analysis by introducing a novel Interval-Valued Intuitionistic Fuzzy Three-Way Conflict Analysis (IVIFTWCA) method, which is anchored in cumulative prospect theory. The method’s key innovation lies in its use of interval-valued intuitionistic fuzzy numbers to represent an agent’s stance, addressing the psychological dimensions and risk tendencies of decision-makers that have been largely overlooked in previous studies. The IVIFTWCA method categorizes conflict situations into affirmative, impartial, and adverse coalitions, leveraging the evaluation of the closeness function and predefined thresholds. It incorporates a reference point, value functions and cumulative weight functions to assess risk preferences, leading to the formulation of precise decision rules and thresholds. The method’s efficacy and applicability are demonstrated through detailed examples and comparative analysis, and its exceptional performance is confirmed through a series of experiments, offering a robust framework for real-world decision-making in conflict situations.

A novel attribute reduction method with constraints on empirical risk and decision rule length

Attribute reduction is a crucial issue of rough set theory and has been applied to various fields. It aims to remove useless or redundant features from data and extract precise rules. Traditional attribute reduction methods have obvious limitations, that is, it can not guarantee the generalization ability of reduced decision rules to classification but can only guarantee the classification performance to visible objects. To overcome this issue and enhance the model classification generalization ability without increasing rules' complexity based attribute reduction, we propose a novel attribute method by employing the Structural Risk Minimization principle, which is a classic method in machine learning to balance model complexity and performance. The improved attribute reduction method with rough set tries to get a trade-off between the number of features and empirical error, wherein the former is defined by rule confidence, and the latter is explored by the mutual information between the condition attribute subset and the decision attribute. In other words, model accuracy can be regarded as an empirical error term to measure the model's classification or prediction performance, and the model complexity as a penalty term to characterize the size of the reduction subset. To implement this approach, genetic algorithm is employed as a heuristic search technique, to obtain the optimal reduction subset. Several comparative experiments are performed on ten UCI datasets to evaluate the model's classification accuracy and the size of the reduction subset. The experimental results indicate the method proposed in this paper with better generalization ability compared to other traditional algorithms under the condition of equal reducted set length.

The QCD axion sum rule

We demonstrate that the true QCD axion that solves the strong CP problem can be found in all generality outside the customary standard QCD band, with QCD being the sole source of Peccei-Quinn breaking. The essential reason is that the basis of axion-gluon interactions does not need to coincide with the mass basis. Specifically, we consider the case in which the QCD axion field is not the only singlet scalar in Nature but it mixes with other singlet scalars (besides the η′). We determine the exact mathematical condition for an arbitrary N-scalar potential to be Peccei-Quinn invariant. Such potentials provide extra sources of mass for the customary axion without enlarging the Standard Model gauge symmetry. The contribution to the axion mass stemming from the QCD topological susceptibility is shown to be shared then among the N axion eigenstates through a precise sum rule. Their location can only be displaced to the right of the standard QCD band. We demonstrate that the axion closest to this band can be displaced from it by a factor of N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sqrt{N} $$\\end{document} at most, and this corresponds to the case in which all axion signals are maximally deviated. Conversely, if one axion is found on the standard QCD band, the other eigenstates will be out of experimental reach. Our results imply that any ALP experiment which finds a signal to the right of the standard QCD axion band can be solving the strong CP problem within QCD, with the associated N − 1 excitations to be found in an area of parameter space that we determine. We illustrate the results and phenomenology in some particular cases.

Ethically Determining the Authorship of Health Science Research Publications: A Scoping Review

Authorship is a significant aspect of scientific research publications, serving as a primary means of acknowledging the intellectual contributions of individuals and shaping career trajectories. However, determining authorship ethically and fairly in health science research remains a complex and multifaceted issue. This scoping review aims to explore the existing literature surrounding ethical considerations associated with authorship determination in health science research publications, published between 1989 and 2023. A total of 25 studies met the inclusion criteria. The findings revealed that ethical authorship practices in health science research are complex and multifaceted. This review emphasized how crucial it is to have precise rules and guidelines for deciding who is the author in academic journals, research institutes, and societies. The International Committee of Medical Journal Editors (ICMJE) has published several ethical principles to address the difficulties associated with authorship determination. Still, there are differences and disagreements in how these rules are interpreted and applied, which could present moral enigmas. The review also examines new ethical issues arising from multidisciplinary and collaborative research, such as the responsibilities and funding of industry collaborations, shared authorship, and acknowledging the contributions of other groups. Maintaining the integrity of health science research and encouraging responsible collaboration requires making sure that authorship determination is transparent, equitable, and accountable. Authors should disclose any conflicts of interest that could influence their judgment or interpretation of the research findings.

Towards a consulting model for a good urban generation of social housing districts in terms of equipment

<div>Social housing in Morocco is housing built with state assistance. It is subject to precise rules of construction, management and allocation and governed by specifications defining requirements in a manual manner. This work which is done manually can produce errors either at the level of calculations or at the level of equipment proposal. The objective of this article is to computerize this area as there is a lack of tools and platforms. To solve this problem, we have created an advisory platform for good urban planning for residents which will help engineers. Based on mathematical formulas. This tool will allow architects and urban planners to design autonomous populations in terms of equipment.</div>

Using model-driven engineering to automate software language translation

The porting or translation of software applications from one programming language to another is a common requirement of organisations that utilise software, and the increasing number and diversity of programming languages makes this capability as relevant today as in previous decades. Several approaches have been used to address this challenge, including machine learning and the manual definition of direct language-to-language translation rules, however the accuracy of these approaches remains unsatisfactory. In this paper we describe a new approach to program translation using model-driven engineering techniques: reverse-engineering source programs into specifications in the UML and OCL formalisms, and then forward-engineering the specifications to the required target language. This approach can provide assurance of semantic preservation, and additionally has the advantage of extracting precise specifications of software from code. We provide an evaluation based on a comprehensive dataset of examples, including industrial cases, and compare our results to those of other approaches and tools. Our specific contributions are: (1) Reverse-engineering source programs to detailed semantic models of software behaviour, to enable semantically-correct translations and reduce re-testing costs; (2) Program abstraction processes defined by precise and explicit rules, which can be edited and configured by users; (3) A set of reusable OCL library components appropriate for representing program semantics, and which can also be used for OCL specification of new applications; (4) A systematic procedure for building program abstractors based on language grammars and semantics.

Mathematics and the formal turn

Since the early twentieth century, it has been understood that mathematical definitions and proofs can be represented in formal systems with precise grammars and rules of use. Building on such foundations, computational proof assistants now make it possible to encode mathematical knowledge in digital form. This article enumerates some of the ways that these and related technologies can help us do mathematics.

Ensemble methods of rank-based trees for single sample classification with gene expression profiles

Building Single Sample Predictors (SSPs) from gene expression profiles presents challenges, notably due to the lack of calibration across diverse gene expression measurement technologies. However, recent research indicates the viability of classifying phenotypes based on the order of expression of multiple genes. Existing SSP methods often rely on Top Scoring Pairs (TSP), which are platform-independent and easy to interpret through the concept of “relative expression reversals”. Nevertheless, TSP methods face limitations in classifying complex patterns involving comparisons of more than two gene expressions. To overcome these constraints, we introduce a novel approach that extends TSP rules by constructing rank-based trees capable of encompassing extensive gene-gene comparisons. This method is bolstered by incorporating two ensemble strategies, boosting and random forest, to mitigate the risk of overfitting. Our implementation of ensemble rank-based trees employs boosting with LogitBoost cost and random forests, addressing both binary and multi-class classification problems. In a comparative analysis across 12 cancer gene expression datasets, our proposed methods demonstrate superior performance over both the k-TSP classifier and nearest template prediction methods. We have further refined our approach to facilitate variable selection and the generation of clear, precise decision rules from rank-based trees, enhancing interpretability. The cumulative evidence from our research underscores the significant potential of ensemble rank-based trees in advancing disease classification via gene expression data, offering a robust, interpretable, and scalable solution. Our software is available at https://CRAN.R-project.org/package=ranktreeEnsemble.

On the Origin of the Moon and the Continents

All astronomical bodies originate inside clouds of gas and dust, therefore there should be a common process that leads to their condensation. In a galactic cloud there is always a gradient of speeds from point to point. Thanks to it, vortices originate that rake the material of the surrounding cloud gradually forming large gaseous disks, inside which vortices of second order develop that concentrate the matter of their orbits forming smaller and much denser disks, within which third order vortices further concentrate the matter. The dense cores of these vortices finally condense in massive bodies: sun, planets and satellites. The result should be a well ordered planetary system with no “debris” around and where both planets and satellites obey to a precise rule of the distances from their central body. The solar system complies with these conditions with three main exceptions. First, in an orbit where a large planet should be there is only a huge number of scattered asteroids. Second, Earth and its moon with all evidence were not formed in the same vortex, which means that Moon originated somewhere else. Third, Neptune’s satellite system has been shattered by the intrusion of a foreign body, Triton, and its largest satellites are missing. These exceptions seem to be strictly connected to each other and all due to a unique event, that is: Triton has diverted the largest Neptune satellite towards the Sun. This satellite impacted at high speed against the missing planet, scattering myriads of fragments from its mantle and pushing it towards the sun, where it eventually fell. The planet had at least 4 satellites some of which remained in their previous orbit, but two of them were dragged towards the sun and were captured by Earth. The largest became its lonely moon while the second fell on its surface giving origin to the continents. This event happened about 3,96 billion of years ago, as it is proven by the ages of the numerous samples brought from the moon.

Emergent nonlocal combinatorial design rules for multimodal metamaterials.

Combinatorial mechanical metamaterials feature spatially textured soft modes that yield exotic and useful mechanical properties. While a single soft mode often can be rationally designed by following a set of tiling rules for the building blocks of the metamaterial, it is an open question what design rules are required to realize multiple soft modes. Multimodal metamaterials would allow for advanced mechanical functionalities that can be selected on the fly. Here we introduce a transfer matrix-like framework to design multiple soft modes in combinatorial metamaterials composed of aperiodic tilings of building blocks. We use this framework to derive rules for multimodal designs for a specific family of building blocks. We show that such designs require a large number of degeneracies between constraints, and find precise rules on the real space configuration that allow such degeneracies. These rules are significantly more complex than the simple tiling rules that emerge for single-mode metamaterials. For the specific example studied here, they can be expressed as local rules for tiles composed of pairs of building blocks in combination with a nonlocal rule in the form of a global constraint on the type of tiles that are allowed to appear together anywhere in the configuration. This nonlocal rule is exclusive to multimodal metamaterials and exemplifies the complexity of rational design of multimode metamaterials. Our framework is a first step towards a systematic design strategy of multimodal metamaterials with spatially textured soft modes.

Puckered and JNK signaling in pioneer neurons coordinates the motor activity of the Drosophila embryo

Central nervous system organogenesis is a complex process that obeys precise architectural rules. The impact that nervous system architecture may have on its functionality remains, however, relatively unexplored. To clarify this problem, we analyze the development of the Drosophila embryonic Ventral Nerve Cord (VNC). VNC morphogenesis requires the tight control of Jun kinase (JNK) signaling in a subset of pioneer neurons, exerted in part via a negative feedback loop mediated by the dual specificity phosphatase Puckered. Here we show that the JNK pathway autonomously regulates neuronal electrophysiological properties without affecting synaptic vesicle transport. Manipulating JNK signaling activity in pioneer neurons during early embryogenesis directly influences their function as organizers of VNC architecture and, moreover, uncovers a role in the coordination of the embryonic motor circuitry that is required for hatching. Together, our data reveal critical links, mediated by the control of the JNK signaling cascade by Puckered, between the structural organization of the VNC and its functional optimization.

WHAT S HAPPEN WHEN PIGS OR POULTRY ARE DELIVRED TO SLAUGHTER PRIOR TO THE END OF WITHDRAWAL PERIOD?

"The withdrawal period after treatment with microbials is established to minimize the concentration of residues in meat of treated pigs or poultry. Even Food Chain has very precise rules about sending or not animals to slaughter before withdrawal period thus human errors may occur. In a prior study two questionnaires was distributed to food business operator (FBO) and competent authority (CA) involving 28 countries in and outside Europe (Romania included), involving pig meat production. Then in the second part of the study, the questionnaires were distributed for poultry meat production. The models developed in the previous study were applied for poultry industry and Romanian study case."

Carbon insetting as a measure to raise supply chain energy efficiency potentials: Opportunities and challenges

There is great potential for companies to reduce greenhouse gas emissions (GHGE) by implementing and financing decarbonisation measures in their own value chain. A company's active contribution to reducing GHGE at a stakeholder in its value chain is considered as carbon insetting e.g. financing of energy efficiency measures along the supply chain as one possibility. The implementation of insetting measures by industrial companies is currently only rudimentarily established. Moreover, there are no precise rules on how insetting should be accounted for. However, the resulting GHGE reductions can affect the corporate carbon footprints of several companies. This leads to the question on how insetting could be transparently reported, while still setting incentives for emission reductions along the supply chain. A comparison of literature based carbon insetting concepts are distinguished from each other in regard to local references and accounting approaches. Based on these a case study of the implementation of supply chain energy efficiency measures in the automotive industry to illustrate accounting issues is developed. The case study exemplifies, that a company that finances an energy efficiency measure at a supplier can only report 10% of the reduced GHGE in its carbon footprint. In light of this background, the pillar model for GHGE accounting is adapted to incorporate carbon insetting. In addition to the carbon footprint (pillar A), two other pillars are introduced. In pillar B, the GHGE reductions outside the scopes of the reporting company can be accounted for. Greenhouse gases removed from the atmosphere can be accounted for in pillar C. The novelty of this study is applying the concept of insetting to an industrial case study and the use of the pillar model to enable transparent accounting of insetting measures. It can thereby serve as a basis for industrial reporting of carbon insetting. By addressing methodological challenges the study provides a basis for further discussion on the concept of carbon insetting.

Fuzzy-Based Active Queue Management Using Precise Fuzzy Modeling and Genetic Algorithm

Active Queue Management (AQM) methods significantly impact the network performance, as they manage the router queue and facilitate the traffic flow through the network. This paper presents a novel fuzzy-based AQM method developed with a computationally efficient precise fuzzy modeling optimized using the Genetic Algorithm. The proposed method focuses on the concept of symmetry as a means to achieve a more balanced and equitable distribution of the resources and avoid bandwidth wasting resulting from unnecessary packet dropping. The proposed method calculates the dropping probability of each packet using a precise fuzzy model that was created and tuned in advance and based on the previous dropping probability value and the queue length. The tuning process is implemented as an optimization problem formulated for the b0, b1, and b2 variables of the precise rules with an objective function that maximizes the performance results in terms of loss, dropping, and delay. To prove the efficiency of the developed method, the simulation was not limited to the common Bernoulli process simulation; instead, the Markov-modulated Bernoulli process was used to mimic the burstiness nature of the traffic. The simulation is conducted on a machine operated with 64-bit Windows 10 with an Intel Core i7 2.0 GHz processor and 16 GB of RAM. The simulation used Java programming language in Apache NetBeans Integrated Development Environment (IDE) 11.2. The results showed that the proposed method outperformed the existing methods in terms of computational complexity, packet loss, dropping, and delay. As such, in low congested networks, the proposed method maintained no packet loss and dropped 22% of the packets with an average delay of 7.57, compared to the best method, LRED, which dropped 21% of the packets with a delay of 10.74, and FCRED, which dropped 21% of the packets with a delay of 16.54. In highly congested networks, the proposed method also maintained no packet loss and dropped 48% of the packets, with an average delay of 16.23, compared to the best method LRED, which dropped 47% of the packets with a delay of 28.04, and FCRED, which dropped 46% of the packets with a delay of 40.23.

Pertanggungjawaban Pidana Advokat Tanpa Mekanisme Pemeriksaan Kode Etik Advokat

Advocates in carrying out their profession are under the protection of laws, statutes and codes of ethics, which cannot be prosecuted before a court, either civil or criminal, as long as they have good faith in defending the interests of their clients. However, in practice, many Advocates are caught in criminal cases in carrying out their professional duties. The purpose of this study is to analyze and find out the characteristics of good faith referred to in article 16 of Law Number 18 of 2003 concerning Advocates in terms of law enforcement and legal protection for the advocate profession and criminal liability for the actions of advocates in carrying out their professional duties without going through the mechanism of examining the Code of Ethics. . The theory used to analyze is law enforcement theory, legal protection theory, and criminal responsibility theory. The writing method used is normative writing method, by analyzing a legal issue through laws and regulations, literature, and other reference materials. The results of this study are that the work of a legal advisor is a work of trust in good faith to rectify the problems faced by clients whose existence is regulated and protected by law. The criminal responsibility of an advocate who commits a crime in carrying out his profession must be seen from the mistakes made by the advocate so that he is seen as having committed an act that meets the elements of a crime which must be proven first through an examination of the code of ethics. The government and/or legislators must pay more attention to and scrutinize the formation of statutory regulations so that there are no overlapping rules and so that one law does not conflict with another. As well as to strengthen the profession, it is hoped that there will be a need for stricter and more precise rules that can be used as a legal umbrella in professional life.

Identification of Design Principles for the Preparation of Colloidal Plexcitonic Materials.

Colloidal plexcitonic materials (CPMs) are a class of nanosystems where molecular dyes are strongly coupled with colloidal plasmonic nanoparticles, acting as nanocavities that enhance the light field. As a result of this strong coupling, new hybrid states are formed, called plexcitons, belonging to the broader family of polaritons. With respect to other families of polaritonic materials, CPMs are cheap and easy to prepare through wet chemistry methodologies. Still, clear structure-to-properties relationships are not available, and precise rules to drive the materials' design to obtain the desired optical properties are still missing. To fill this gap, in this article, we prepared a dataset with all CPMs reported in the literature, rationalizing their design by focusing on their three main relevant components (the plasmonic nanoparticles, the molecular dyes, and the capping layers) and identifying the most used and efficient combinations. With the help of statistical analysis, we also found valuable correlations between structure, coupling regime, and optical properties. The results of this analysis are expected to be relevant for the rational design of new CPMs with controllable and predictable photophysical properties to be exploited in a vast range of technological fields.

Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm

The grinding process in the concentrator is a part of the largest energy consumption, but also the most likely to cause a waste of resources, so the optimization of the grinding process is a very important link. The traditional fuzzy controller relies solely on the expert knowledge summary to construct control rules, which can cause significant steady-state errors in the model. In order to solve the above problem, this paper proposes an elastic variable universe fuzzy control based on Particle Swarm Optimization (PSO) algorithm. The elastic universe fuzzy control model does not need precise fuzzy rules, but only needs to input the general trend of the rules, and the division of the universe is performed by the contraction-expansion factor. The control performance is directly related to the contraction-expansion factor, so this article also proposes using particle swarm optimization to optimize the scaling factor to achieve the optimal value. Finally, simulation models of traditional fuzzy control and elastic universe fuzzy control of feeding system of mill were built using Python to verify the control effect. Its simulation results show that the time of the reaction of the fuzzy control system in the elastic variable theory universe based on particle swarm optimization was shorter by 34.48% comparing to the traditional one. Elastic variable universe fuzzy control based on particle swarm optimization (PSO) effectively improved the control accuracy of the mill feeding system and improved the response speed of the system to a certain extent.