Minimal Rules Research Articles

Exploring highly selective polymethoxy fenamate isosteres as novel anti-prostate cancer agents: Synthesis, biological activity, molecular docking, molecular dynamics, and ADME studies

In this study, we synthesized and characterized sixteen new polymethoxy-substituted hydrazone derivatives. These compounds were evaluated for their in vitro cytotoxic activity against human prostate cancer (PC3) and human umbilical vein endothelial (HUVEC) cell lines. Compounds 5, 6, 7, and 11 exhibited significant cytotoxic effects with high selectivity index. Molecular docking studies and MM-GBSA binding free energy calculations were performed against tubulin protein. Compound 7 emerged as a particularly promising candidate, displaying an IC50 value of 1.49 µM against PC3 cells and a selectivity index of 264. Compound 7 achieved impressive docking score -13.655 kcal/mol against tubulin and formed stable hydrogen bonds and π-π stacking interactions with key residues in this protein. MD simulations confirmed the stability of the 7-tubulin complex. ADME analysis indicated that compound 7 has favorable absorption, solubility, and permeability properties, with minimal rule violations. The SAR analysis highlighted the significance of specific functional groups in enhancing the compound's cytotoxic and binding properties. Overall, compound 7 is identified as a leading candidate due to its exceptional cytotoxicity, high selectivity index, strong binding affinities, and favorable pharmacokinetic profile, making it a top candidate for further investigation and development as a novel anticancer agent.

Iterated variable neighborhood search for integrated scheduling of additive manufacturing and multi-trip vehicle routing problem

This study addresses a novel problem structure for integrated scheduling of additive manufacturing and delivery processes. In the manufacturing process, we consider batch production of parts, simulating Selective Laser Melting technology in parallel additive manufacturing machines with heterogeneous specifications. A batch production, referred to as a build, must consist of parts with homogenous material. During the production run, sequence-dependent material setup times occur in between builds. In the delivery process, parts are delivered in batches using a limited number of homogenous capacitated vehicles. Each vehicle might perform multi-trips. Thus, the route for each trip and departure schedule among trips must be decided. In this regard, batch delivery is independent to batch production. The problem objective function is to minimize the total completion time of all parts. For optimally solving the problem, a mixed-integer linear programming model is devised. To solve larger problems sizes under a lower computation time, an iterated variable neighborhood search algorithm with embedded rule-based heuristics is proposed. The computational experiment denotes that the proposed algorithm with route minimization rule effectively solves small-scale problems, while capacity usage maximization rule performs the best in large-scale problems and outperforms other existing metaheuristics. Further result analysis is also performed to provide managerial insights.

FedGAMMA: Federated Learning With Global Sharpness-Aware Minimization.

Federated learning (FL) is a promising framework for privacy-preserving and distributed training with decentralized clients. However, there exists a large divergence between the collected local updates and the expected global update, which is known as the client drift and mainly caused by heterogeneous data distribution among clients, multiple local training steps, and partial client participation training. Most existing works tackle this challenge based on the empirical risk minimization (ERM) rule, while less attention has been paid to the relationship between the global loss landscape and the generalization ability. In this work, we propose FedGAMMA, a novel FL algorithm with Global sharpness-Aware MiniMizAtion to seek a global flat landscape with high performance. Specifically, in contrast to FedSAM which only seeks the local flatness and still suffers from performance degradation when facing the client-drift issue, we adopt a local varieties control technique to better align each client's local updates to alleviate the client drift and make each client heading toward the global flatness together. Finally, extensive experiments demonstrate that FedGAMMA can substantially outperform several existing FL baselines on various datasets, and it can well address the client-drift issue and simultaneously seek a smoother and flatter global landscape.

표현의 자유와 국가의 역할 그리고 상호주체성

The expansion of social networks among individuals has sparked discussions about freedom of expression and its limitations. Due to the development of technology, individuals have been able to share their experiences, knowledge, and thoughts without particular and/or restrictive difficulties. The basis of conversation initiates from comprehension. The public sphere is the product of a great plan through the restoration of reason to overcome the problems of a society that centered only on the pre-modernity. The formation of a public sphere presupposes overcoming the paradoxical situation of rationalization. If we are to leave behind comprehensive dogmatic that we can not compromise on and find topics on which we can achieve ‘overlapping consensus’, we must adhere to minimal rules of communication. Autonomy in a community can be secured by guaranteeing freedom of expression, and the state's restrictions on this should remain in an auxiliary role for the expansion of public reason. What something is valuable doesn't always mean we should enforce it to another. Beyond the belief that freedom of expression is an extension of autonomy through rational resolution of conflicts, we must have the duty to respect others under the value of solidarity.

Simulation research on breakdown diagnosis based on least squares support vector machine optimized by simulated annealing genetic algorithm

The application of support vector machine to the aircraft power supply system breakdown diagnosis is one of the research focuses in the tomorrow. Support vector machine (SVM) belongs a new type of machine learning technique, which uses structural hazard minimization rule to substitute for the conventional empirical hazard minimization according to great specimen. The powerful performance of the least squares support vector machine (LSSVM) needs to be reflected in the optimal selection of appropriate parameters, and the quality of parameters greatly affects the accuracy and efficiency of breakdown diagnosis. The traditional LSSVM parameters selection method is inefficient, the calculation is huge, and it takes expensive time to select the satisfactory solution. Aiming at the problem of parameters selection of LSSVM, the way of optimizing the parameters of LSSVM by apply simulated annealing genetic algorithm (SAGA) is proposed. SAGA uses the parallel sampling process of genetic algorithm to improve the time performance of optimization, and makes use of the simulated annealing algorithm to dominate the constriction of majorization to prevent precocity. On the one hand, genetic algorithm has strong ability to grasp the overall search process, on the other hand, simulated annealing algorithm is used to control the convergence of the algorithm to prevent premature phenomenon. The automatic optimal selection of LSSVM parameters is achieved. The Iris system classification and recognition in the UCI machine learning database and the breakdown diagnosis of the autopilot flight control box are used as experimental platforms to obtain data samples for simulation research. The simulation results show that LSSVM optimized by SAGA improves both the accuracy and efficiency of classification recognition. It not only effectively overcomes the problem of low efficiency caused by searching optimal parameters by experience, but also effectively improves the accuracy of classification recognition. The false alarm rate and redundant breakdowns are effectively reduced.

An instance of Satisfiability Problem learnt with Instance based, Decision trees, Naive Bayes

Though it is not recommended for mining the data sets produced by any closed-form solution, our approach is very much aiming at the standard satisfiability (SAT) or assignment problem in terms of searching for the existence of learning models and their performance using machine learning algorithms. Here we claim that we do not apply the analytical or theoretical method in the sense it is not based on conventional or well-established minimization rules or circuit synthesis in mind but the application of computational codes for prediction for the given Boolean circuits. The main objectives are to get classification on mapping Boolean literal values to some predefined functional output and to get maximum accuracy of such classifiers. Here we have enumerated the results up to 20 bits majority operator as an instance for constructing the learning models based on machine learning algorithms. The similarity and significance of the results are shown and thus it may open the possibility for extensibility and generalizability to other types of Boolean circuits. The maximum average performance is found to be 80.72% of accuracy and 74.81% of precision based on the selected set of classifiers.

Particle swarm optimization for integrated scheduling problem with batch additive manufacturing and batch direct-shipping delivery

This study addresses a novel proRblem for the integrated scheduling of additive manufacturing and delivery processes with the objective of minimizing the total weighted completion time of the ordered parts. In the manufacturing process, batch production on non-identical parallel additive manufacturing machines is considered. A single batch of production, which is referred to as a build, consists of parts requiring the same material. Sequence-dependent setup times occur between two adjacent builds. Once the production run is completed, the parts in the builds are regrouped into delivery batches within the same customer. In the delivery process, parts are delivered by homogeneous vehicles to the corresponding customers through direct-shipping method. To optimally solve the small problem, a mixed-integer linear programming model is formulated. For solving large-scale problems, three rule-based heuristics that are embedded in particle swarm optimization (PSO) are proposed. A tight lower bound is developed to evaluate the performance of the proposed algorithms. The computational experiment shows that the proposed PSO with delivery waiting time minimization rule is effective to solve small-scale problems, while in large-scale problems, capacity usage maximization rule performs the best and both outperform another existing metaheuristic. Several sensitivity analyses are also performed to provide additional managerial insights.

In-Vivo fluorescent nanosensor implants based on hydrogel-encapsulation: investigating the inflammation and the foreign-body response

Nanotechnology-enabled sensors or nanosensors are emerging as promising new tools for various in-vivo life science applications such as biosensing, components of delivery systems, and probes for spatial bioimaging. However, as with a wide range of synthetic biomaterials, tissue responses have been observed depending on cell types and various nanocomponent properties. The tissue response is critical for determining the acute and long term health of the organism and the functional lifetime of the material in-vivo. While nanomaterial properties can contribute significantly to the tissue response, it may be possible to circumvent adverse reactions by formulation of the encapsulation vehicle. In this study, five formulations of poly (ethylene glycol) diacrylate (PEGDA) hydrogel-encapsulated fluorescent nanosensors were implanted into SKH-1E mice, and the inflammatory responses were tracked in order to determine the favorable design rules for hydrogel encapsulation and minimization of such responses. Hydrogels with higher crosslinking density were found to allow faster resolution of acute inflammation. Five different immunocompromised mice lines were utilized for comparison across different inflammatory cell populations and responses. Degradation products of the gels were also characterized. Finally, the importance of the tissue response in determining functional lifetime was demonstrated by measuring the time-dependent nanosensor deactivation following implantation into animal models.

Local decarbonisation opportunities and barriers: UK public procurement legislation

Significant changes in public procurement are underway in the UK. Post-Brexit, the UK government is in the process of modifying the procurement rulebook. The new regime will be guided by the procurement strategy priorities of social value, jobs and net zero; however, the new approach to procurement foresees minimal rules and significant guidance. This paper explores implications of changing procurement legislation on the delivery of net zero at a local level in the UK. Particular focus lies on the many contracting authorities, such as county, city and district councils, that have declared climate emergencies and ambitions to decarbonise. Two case studies depicting the procurement of local net zero solutions, one on the Energy Efficient Scotland Programme and the other on place-based finance instruments, such as community municipal investments (CMIs), are used to identify existing opportunities and barriers to procure local net zero solutions with a particular focus on institutional governance. This paper concludes with a set of questions that need to be addressed to ensure that the changes to public procurement legislation are understood and that the institutional governance of intermediation can lead to outcomes that balance economic, social and environmental considerations across multiple scales. <em><strong><br /></strong></em> <em><strong>Practice relevance</strong></em> <em><strong></strong></em>The absence of a rich approach to multilevel governance in the UK means that procurement is one of the few options currently available to many local authorities to deliver net zero at the local level. Given the scale of investments necessary to reach net zero, institutional governance is necessary to align top-down procurement and bottom-up finance with many other laws, policies and regulations. This paper explores the implications of changing procurement legislation on the delivery of net zero at a local level. Two case studies depicting the procurement of local net zero solutions (the Energy Efficient Scotland Programme and place-based finance instruments, <em>e.g.</em> CMIs) are used to identify existing opportunities and barriers to procure local net zero solutions. Guidance on understanding public procurement legislation is provided to ensure that the institutional governance of intermediation can lead to outcomes that balance economic, social and environmental considerations (especially decarbonisation) across multiple scales.

Filling Process Optimization through Modifications in Machine Settings

In this paper, a mathematical model is developed for the modified settings of an automatic filling machine to minimize the filling time of orders for different volumes of dairy product and flavors. The linear programming model is solved using the Simplex method to find an optimal solution to the optimization problem. The results of the model are used for sequencing the processing of orders using one-dimensional rules with the aim of obtaining an optimal sequence for the most valued performance measure. The comparative analysis of the one-dimensional rules showed that Shortest Processing Time (SPT) is better than the other rules for minimization of the average time past due. Additionally, the results of the model for the new machine settings, when compared with previous similar studies, yielded encouraging results.

The Rich Get Stronger: The Purse Seine Fishery of the Turkish Straits System

Purse seiners generally try to maximize their revenues by targeting multiple species, starting with the most valuable ones available. The technology and capacity of purse seiners can be exceptionally high for the stocks found in the Marmara Sea, Turkey’s only inland sea, due to its small size and nearly totally enclosed nature. Due to their large capabilities, they are responsible for the majority of catches and hence declines from this sea and thus should be held responsible for the poor state of marine stocks in the region. This study examines the catch compositions, expenses, and revenue sharing of purse seiners by using one representative vessel class for each of the four vessel length categories in the Marmara Sea. Surveys were also conducted with purse seine fishers to assess their perceptions related to fisheries management strategies along with their ideas for transitioning towards sustainable fisheries. As purse seiners are prominent stakeholders in the Marmara Sea fisheries, they should be incorporated into management guidance for effectiveness, along with other stakeholders. This study provides novel socio-economic data along with their perspectives, which may assist in improving policy decisions and capabilities. Our results demonstrate that purse seiners do not consider that their actions are a contributing factor to the current state of the fisheries, they trust in their amassed historical local knowledge, and seem to want to rebuild the resources, however only with minimal rules imposed on their sector.

Sensing the shape of a cell with reaction diffusion and energy minimization

Some dividing cells sense their shape by becoming polarized along their long axis. Cell polarity is controlled in part by polarity proteins, like Rho GTPases, cycling between active membrane-bound forms and inactive cytosolic forms, modeled as a "wave-pinning" reaction-diffusion process. Does shape sensing emerge from wave pinning? We show that wave pinning senses the cell's long axis. Simulating wave pinning on a curved surface, we find that high-activity domains migrate to peaks and troughs of the surface. For smooth surfaces, a simple rule of minimizing the domain perimeter while keeping its area fixed predicts the final position of the domain and its shape. However, when we introduce roughness to our surfaces, shape sensing can be disrupted, and high-activity domains can become localized to locations other than the global peaks and valleys of the surface. On rough surfaces, the domains of the wave-pinning model are more robust in finding the peaks and troughs than the minimization rule, although both can become trapped in steady states away from the peaks and valleys. We can control the robustness of shape sensing by altering the Rho GTPase diffusivity and the domain size. We also find that the shape-sensing properties of cell polarity models can explain how domains localize to curved regions of deformed cells. Our results help to understand the factors that allow cells to sense their shape-and the limits that membrane roughness can place on this process.

Modifying rules in women's professional sport: a case study of the Australian Football League Women's league

Rule modifications are common-place in men's professional sport however there is little understanding of why rules are modified in women's professional sport beyond sociological enquiry. This research aims to understand the Australian Football League's (AFL) rationale for, and approach to, modifying match rules for the inaugural Australian Football League Women's league (AFLW). Performance data was collected from 295 players during ten exhibition matches in 2016 and semi-structured interviews were conducted with AFLW managers (n = 2) and coaches (n = 8). A rigorous data-driven approach to modifying rules for the AFLW suggested that the AFL had commercial logics and were not driven by gendered expectations. However, quantitative data indicated that rule modifications made no significant difference to the commercial appeal of match play. Rather, the AFL's commercial priorities in modifying rules were tempered by developmental logics of operational stakeholders which led to the introduction of minimal rule modifications for the AFLW.

Optimal Braking Torque Distribution of Dual-Motor Front-Rear Individually Driven Electric-Hydraulic Hybrid Powertrain Based on Minimal Energy Loss

Regenerative braking is one of the most important methods for improving energy utilization in electric vehicles. Electric vehicles with front-rear, individually driven configurations exhibit significant potential and flexibility for recovering braking energy. To improve the system’s high-power impact tolerance, a high-power density hydraulic energy storage system can be incorporated to facilitate a full-drive dual-motor electric–hydraulic hybrid (DMEHH) powertrain. The DMEHH system is composed of an independently driven electric–hydraulic hybrid front axle and a purely electric rear axle. In this study, a method for distributing braking torque to minimize energy loss was devised based on the proposed DMEHH powertrain. Power loss models for both the electric and hydraulic subsystems have been developed. Loss minimization control was adapted for the power loss model of the permanent magnet synchronous motors in the powertrain, and the front-rear and front electric–hydraulic torque braking distribution maps were calculated using the energy-loss minimization rule. The application of torque distribution maps resulted in energy losses less than those of the general ideal torque distribution for all braking conditions. The energy loss decreased by 27.2% when the loss minimization control method was used in the WLTC cycle, and decreased by 29.1%, 25.5%, and 21.6% in UDDS, NEDC, and US06 driving cycles, respectively.

Plant Hotels: designing the imaginary foundations of communities

ABSTRACT Communities can be a significant source of well-being. Design literature has recently paid attention to social design and social innovation and has advanced our understanding of designing for communities in many ways. One thing that has been left to the sidelines has been those imageries that shape communities. They are usually seen but unnoticed, but important in their consequences. This paper builds on Garfinkel’s ethnomethodology to explicate some of these imageries through a series of four Plant Hotels in Helsinki and one in Stockholm. Inspired by relational art they were meant to explore community-formation with minimal rules. They were treated as breaching experiments that led us to classify the imageries into four main orientations. The paper discusses how these imageries create a web of assumptions that create and maintain communities and make them robust, and how they can be turned into design material that helps us to reimagine communities.

GST complexities in Malaysia: views from tax experts

PurposeThe Goods and Services Tax (GST) implementation in Malaysia had raised various responses from many parties involved when the new tax was introduced in 2015 until its abolishment in 2018. GST complexity has been stated as part of the reasons for its resistance and abolishment. The purpose of this paper is to identify causes of GST complexities during its implementation in Malaysia.Design/methodology/approachThis paper draws on causes of tax complexity from previous studies and conducted semi-structured interviews with tax experts. The respondents comprised of subject matter experts in GST and the findings are based on their numerous years of experiences handling clients with diverse backgrounds.FindingsFindings from this study are categorised into legal complexity and declarative complexities. Almost all the causes of complexities from prior studies were encountered by Malaysian businesses. This study concluded that the most likely reasons for the abolishment of GST in Malaysia are: numerous rules and exception to the rules (e.g. 35 tax codes, gifts rules, 21-day rules), frequent changes of GST rulings and guidelines, GST computations that were too difficult for mixed supply and process of GST refund that were onerous and lengthy.Research limitations/implicationsLimitations of this paper include reliance on the respondents’ ability to accurately and honestly recall details of their experiences, circumstances, thoughts or behaviours that are being asked.Practical implicationsDespite the abolishment of the GST in Malaysia, it is pertinent to identify the causes of GST complexities that can be learnt by the relevant authorities. Measures can be formulated to mitigate the identified GST complexities to ensure high compliance among businesses with other existing taxes or new taxes (if any) to be introduced in the future. This includes addressing the competency of RMCD officers and businesses on the rules and regulations, minimal rules and exception to the rules, a simplified GST computations for mixed supply and a systematic refund process.Originality/valueThe research method used is a flexible and powerful tool to capture the voices and the ways respondents make meaning of their experience dealing with the GST matters. The findings reveal a deep understanding of the issues from the views of the experts in the field.

A Polylobar Nucleus Identifying and Extracting Method for Leukocyte Counting

Accurate counting of leukocytes is an important method for diagnosing human blood diseases. Because most nuclei of neutrophils and eosinophils are polylobar, it is easily confused with the unilobar nuclei in nucleus segmentation. Therefore, it is very essential to accurately identify and determine the polylobar leukocytes. In this paper, a polylobar nucleus identification and extracting method is proposed. Firstly, by using the Otsu threshold and area threshold method, the nuclei of leukocytes are accurately segmented. According to the morphological characteristics of polylobar leukocytes, the edges of the mitotic polylobar leukocytes are detected, and the numbers of polylobar leukocytes are determined according to the minimal distance rule. Therefore, the accurate counting of leukocytes can be realized. From the experimental results, we can see that using the Otsu method and the area threshold to segment the polylobar nuclear leukocytes, the segmentation ratio of the leukocyte nucleus reached 98.3%. After using the morphological features, the polylobar nuclear leukocytes can be accurately counted. The experimental results have verified the feasibility and practicability of the proposed method.

Values of Travel Time for Recreational Trips under Different Behavioural Rules

In this study, discrete choice models that combine different behavioural rules are estimated to study the visitors’ preferences in relation to their travel mode choices to access a national park. Using a revealed preference survey conducted on visitors of Teide National Park (Tenerife, Spain), we present a hybrid model specification—with random parameters—in which we assume that some attributes are evaluated by the individuals under conventional random utility maximization (RUM) rules, whereas others are evaluated under random regret minimization (RRM) rules. We then compare the results obtained using exclusively a conventional RUM approach to those obtained using both RUM and RRM approaches, derive monetary valuations of the different components of travel time and calculate direct elasticity measures. Our results provide useful instruments to evaluate policies that promote the use of more sustainable modes of transport in natural sites. Such policies should be considered as priorities in many national parks, where negative transport externalities such as traffic congestion, pollution, noise and accidents are causing problems that jeopardize not only the sustainability of the sites, but also the quality of the visit.

Semi-Automatic Adaptation of Diagnostic Rules in the Case-Based Reasoning Process

The paper presents a new approach to effectively support the adaptation phases in the case-based reasoning (CBR) process. The use of the CBR approach in DSS (Decision Support Systems) can help the doctors better understand existing knowledge and make personalized decisions. CBR simulates human thinking by reusing previous solutions applied to past similar cases to solve new ones. The proposed method improves the most challenging part of the CBR process, the adaptation phase. It provides diagnostic suggestions together with explanations in the form of decision rules so that the physician can more easily decide on a new patient’s diagnosis. We experimentally tested and verified our semi-automatic adaptation method through medical data representing patients with cardiovascular disease. At first, the most appropriate diagnostics is presented to the doctor as the most relevant diagnostic paths, i.e., rules—extracted from a decision tree model. The generated rules are based on existing patient records available for the analysis. Next, the doctor can consider these results in two ways. If the selected diagnostic path entirely covers the actual new case, she can apply the proposed diagnostic path to diagnose the new case. Otherwise, our system automatically suggests the minimal rules’ modification alternatives to cover the new case. The doctor decides if the suggested modifications can be accepted or not.

Generalized eigenvalue methods for Gaussian quadrature rules

A quadrature rule of a measure $\mu$ on the real line represents a convex combination of finitely many evaluations at points, called nodes, that agrees with integration against $\mu$ for all polynomials up to some fixed degree. In this paper, we present a bivariate polynomial whose roots parametrize the nodes of minimal quadrature rules for measures on the real line. We give two symmetric determinantal formulas for this polynomial, which translate the problem of finding the nodes to solving a generalized eigenvalue problem.