Specific Constraints Research Articles

Protein-lipid interactions and protein anchoring modulate the modes of association of the globular domain of the Prion protein and Doppel protein to model membrane patches.

The Prion protein is the molecular hallmark of the incurable prion diseases affecting mammals, including humans. The protein-only hypothesis states that the misfolding, accumulation, and deposition of the Prion protein play a critical role in toxicity. The cellular Prion protein (PrPC) anchors to the extracellular leaflet of the plasma membrane and prefers cholesterol- and sphingomyelin-rich membrane domains. Conformational Prion protein conversion into the pathological isoform happens on the cell surface. In vitro and in vivo experiments indicate that Prion protein misfolding, aggregation, and toxicity are sensitive to the lipid composition of plasma membranes and vesicles. A picture of the underlying biophysical driving forces that explain the effect of Prion protein - lipid interactions in physiological conditions is needed to develop a structural model of Prion protein conformational conversion. To this end, we use molecular dynamics simulations that mimic the interactions between the globular domain of PrPC anchored to model membrane patches. In addition, we also simulate the Doppel protein anchored to such membrane patches. The Doppel protein is the closest in the phylogenetic tree to PrPC, localizes in an extracellular milieu similar to that of PrPC, and exhibits a similar topology to PrPC even if the amino acid sequence is only 25% identical. Our simulations show that specific protein-lipid interactions and conformational constraints imposed by GPI anchoring together favor specific binding sites in globular PrPC but not in Doppel. Interestingly, the binding sites we found in PrPC correspond to prion protein loops, which are critical in aggregation and prion disease transmission barrier (β2-α2 loop) and in initial spontaneous misfolding (α2-α3 loop). We also found that the membrane re-arranges locally to accommodate protein residues inserted in the membrane surface as a response to protein binding.

Thermal Conductivity Calculation in Organic Liquids: Application to Poly-α-Olefin.

In this work, we aim to understand and predict the thermal properties of automotive lubricants using non-equilibrium molecular dynamics. After a previous study on model materials for the mechanical parts of a car engine, we now focus on the thermal conductivity κ of the poly-α-olefin base oil (PAO4) using the well-known sink and source method to study the response of the system to an imposed heat flux. We present a detailed methodology for the calculation of κ, taking into account specific constraints related to the system under study, such as large steady-state fluctuations and rapidly growing stationarization times. We provide thermal conductivity results using four different force fields, including OPLS-AA, PCFF and COMPASS, in a temperature range of 300 to 500 K, which corresponds to the typical operating range of a car engine. The results are compared to experimental measurements performed on the commercial compound using the laser flash method. Agreement at room temperature is shown to be excellent for our in-house force field.

ChatGPT Makes a Difference in Daily Life: As a Life Guidance

Although Chat Generative Pre-trained Transformer (ChatGPT) has made significant strides in various domains such as education, healthcare, and music, the current situation highlights a lack of comprehensive research and analysis regarding its influence on these vital aspects of life. This research commences by outlining ChatGPT's training capabilities and its developmental journey. Subsequently, this review delve into its practical application in real-world scenarios, where ChatGPT is assigned the task of generating a 90-second background score while adhering to specific constraints. A notable assessment by Marco Cascella and colleagues is then presented, where ChatGPT was engaged to generate a medical report for ICU patients, using a set of non-sequentially provided medical parameters such as treatments, laboratory results, and respiratory metrics. This section also critically examines the limitations of ChatGPT in handling medical queries. Further, this review discussed the generative AI architecture of Baidu Anu, D, highlighting its remarkable efficacy, using ChatGPT as an illustrative example, particularly in pedagogical settings. The paper concludes by acknowledging the substantial contributions of ChatGPT in various arenas, while also addressing its inherent limitations. The study findings suggest that ChatGPT can notably benefit areas such as education, healthcare, or music, thanks to the robust language AI model created by OpenAI.

Timed-release encryption anonymous interaction protocol based on smart contract

Timed-release encryption (TRE) is a cryptographic primitive that can control the decryption time and has significant application value in time-sensitive scenarios. To solve the reliability issue of nodes in existing TRE anonymous interaction schemes, we propose a blockchain-based TRE protocol for anonymous query time trapdoors. In our protocol, the recipient divides the encrypted trapdoor request information into n ciphertext fragments using secret sharing technology near the decryption time, and employs the idea of onion routing to perform layer-by-layer encryption, creating onion-type data transmitted through middlemen selected from the smart contract. After receiving the ciphertext fragments, the time server integrates them to obtain the trapdoor request information and returns the corresponding time trapdoor to the recipient. This allows the recipient to query any time trapdoor anonymously. Our protocol provides a normative design for the smart contract and specific constraints on the participants’ behavior. Compared with the related anonymous query trapdoor schemes, our protocol improves the probability of successful queries. Security analysis shows that our protocol can resist release-ahead attack, interruption attack, eavesdropping attack, and replacement attack. Performance analysis shows that our protocol outperforms related protocols regarding anonymity, efficiency, and flexibility, achieving highly efficient anonymous interactions. Finally, we conducted an experiment in the Ethereum Rinkeby test network. For the settings of ciphertext fragment number n=3 and ciphertext fragment threshold t=2, the gas consumption for a user to execute the contract was $5.66, which was higher than the contract cost of related schemes, but the contract execution cost was within an acceptable range.

VEMstitch: an algorithm for fully automatic image stitching of volume electron microscopy.

As software and hardware have developed, so has the scale of research into volume electron microscopy (vEM), leading to ever-increasing resolution. Usually, data collection is followed by image stitching: the same area is subjected to high-resolution imaging with a certain overlap, and then the images are stitched together to achieve ultrastructure with large scale and high resolution simultaneously. However, there is currently no perfect method for image stitching, especially when the global feature distribution of the sample is uneven and the feature points of the overlap area cannot be matched accurately, which results in ghosting of the fusion area. We have developed a novel algorithm called vEMstitch to solve these problems, aiming for seamless and clear stitching of high-resolution images. In vEMstitch, the image transformation model is constructed as a combination of global rigid and local elastic transformation using weighted pixel displacement fields. Specific local geometric constraints and feature reextraction strategies are incorporated to ensure that the transformation model accurately and completely reflects the characteristics of biological distortions. To demonstrate the applicability of vEMstitch, we conducted thorough testing on simulated datasets involving different transformation combinations, consistently showing promising performance. Furthermore, in real data sample experiments, vEMstitch successfully gives clear ultrastructure in the stitching region, reaffirming the effectiveness of the algorithm. vEMstitch serves as a valuable tool for large-field and high-resolution image stitching. The clear stitched regions facilitate better visualization and identification in vEM analysis. The source code is available at https://github.com/HeracleBT/vEMstitch.

Solving the Binary Puzzle with Genetic Algorithm

The increased internet usage after the pandemic led the UN Forum to improve cybersecurity measures, with zero-knowledge proofs (ZKP) being a viable solution for securing confidential information. ZKP protocols can be demonstrated through the binary puzzle, an NP-complete logic puzzle with four specific constraints. The key contribution of this paper is its successful implementation of the genetic algorithm as a new method to solve the binary puzzle. The optimized fitness function determined the solution at an average of 1.33-2.33 generations for populations ranging from 100 to 500. Its quadratic property calculated the solution faster than the ordinary linear fitness function.

Assessing CNN’s Performance with Multiple Optimization Functions for Credit Card Fraud Detection

In recent days, credit card fraud has emerged as a significant challenge for researchers in the field of detection and prevention. Tackling this challenge holds substantial benefits for both public and private organizations, as it directly impacts economic statistics. Our proposed model introduces crucial advancements to address this issue in real-time scenarios. To achieve this, we harnessed the power of Deep Convolutional Neural Networks (DeepConvNet) in conjunction with various optimization techniques. Optimization algorithms encompass a group of mathematical and computational techniques employed to discover the most suitable solution or set of solutions for a given problem. The primary goal of these algorithms is to optimize, either by maximizing or minimizing, an objective function while ensuring compliance with specific constraints. In this research work, we provide a comparison of highly effective and validated optimization techniques: Stochastic Gradient Descent (Sgd), Adaptive Gradient (Adagrad), Adaptive Moment Estimation (Adam), and Root Mean Squared Propagation (Rmsprop). These optimization algorithms are applied to the Deep Convolutional Neural Network (DeepConvNet) in the subject of our specific problem statement, which involves credit card fraud detection (CCFD). After careful consideration of the problem’s nature, objective function characteristics, and computational aspects, we fnd that all four algorithms are suitable for our CCFD task. However, based on experimental results, it is evident that Rmsprop outperforms others, leading to a remarkable 99.93% in accuracy.

Reinforcement Learning With Data Envelopment Analysis and Conditional Value-At-Risk for the Capacity Expansion Problem

The capacity expansion problem is solved by accurately measuring the existing demand-supply mismatch and controlling the emissions output, considering multiple objectives, specific constraints, resource diversity, and resource allocation. This article proposes a reinforcement learning (RL) framework embedded with data envelopment analysis (DEA) to generate the optimal policy and guide the productivity improvement. The proposed framework uses DEA to evaluate efficiency and effectiveness for reward estimation in RL, and also assesses conditional value-at-risk to characterize the risk-averse capacity decision. Instead of focusing on short-term fluctuations in demand, RL optimizes the expected future reward with sequential capacity decisions over time. An empirical study of U.S. power generation validates the proposed framework and provides the managerial implications to policy makers. The results show that the RL agent can successfully learn the optimal policy through observing the interactions between the agent and the environment, and suggest the capacity adjustment that can improve efficiency by 8.3% and effectiveness by 0.9%. We conclude that RL complements productivity analysis, and emphasizes ex-ante planning over ex-post evaluation.

A Modular Heuristic-Based Solution for Large-Scale Outage Management in Nuclear Power Plants

The Resource Constrained Project Scheduling Problem (RCPSP) is a prominent combinatorial optimization problem in academia. It involves scheduling project activities while considering constraints arising from logical relationships between tasks, resource capacity limitations, and other projectspecific factors. In this paper, we propose a modular heuristic method for efficient outage planning and execution within Nuclear Power Plants (NPPs). The project contains more than 24,000 tasks with specific industrial-oriented constraints such as the notion of prioritization, the event of pausing tasks in progress, handling of resources incompatibility, and adjusting dynamic units of resources based on their availability. The execution speed of the entire solution has been extensively revised to reach an average of 10 to 15 minutes on a standard computer. This enables project managers to promptly evaluate various scenarios with minimal data adjustments, facilitating informed decision-making. The solutions effectiveness has been validated by domain experts, demonstrating its potential for real-world application.

Linking Labour Market Aspirations to Perceived Discrimination: The Case of Refugees in Norway

This article provides a close look at the possible connection between labour market aspirations and perceptions of discrimination. While research on policy implementation and public measures are plentiful regarding immigrants’ labour market integration, less attention has been given to what shapes immigrants’ motivation and desires to become part of the labour market. Following the idea that migrants’ capacity to exert agency is shaped by given — or perceived — opportunity structures the author examines how discrimination, as a specific structural constraint, influence their capacity to aspire in the labour market. This article draws on ethnographic fieldwork among refugees settling in Norway. The findings indicate discrimination as a salient part of their migration experiences. Based on how they negotiate and resist discrimination, the author coins the terms “aspirational deprivation” and “aspirational deskilling” to capture how the refugees’ responses to perceived discrimination shape labour market aspirations.

Role of continental margin architecture in models of ophiolite emplacement

Ophiolite obduction, the process during which part of the oceanic crust overlaps the continental margin, presents a challenge in geodynamic reconstructions of lithospheric processes. The difference in buoyancy between the dense oceanic crust and the relatively buoyant continental crust makes obduction of the oceanic crust difficult, if not impossible, if we only consider the buoyancy forces. The initial configuration of the oceanic basins must have specific thermal and geometric constraints to overcome the difficulties posed by the negative buoyancy. Here, we present a systematic investigation of the geometric and geodynamic parameters controlling the process of ophiolite emplacement. We show which parameters are the most important during ophiolite emplacement and the optimum geometries favouring this emplacement. We focus on ‘Tethyan’ ophiolites, which are characterized by a relatively small inferred basin size and are commonly found in the Mediterranean region. Based on a combination of parameters, we identify the configurations most susceptible to ophiolite obduction. Our models, in agreement with the geological data, show that to achieve ophiolite obduction, the obducted lithosphere must be young and the length of the ocean–continent transition zone must be relatively sharp. Supplementary material: Supplementary figures S1–S5, that are mentioned in the main text, are available at https://doi.org/10.6084/m9.figshare.c.6922526 Thematic collection: This article is part of the Ophiolites, melanges and blueschists collection available at: https://www.lyellcollection.org/topic/collections/ophiolites-melanges-and-blueschists

A statistical analysis method for probability distributions in Erdös-Rényi random networks with preferential cutting-rewiring operation.

The study of specific physiological processes from the perspective of network physiology has gained recent attention. Modeling the global information integration among the separated functionalized modules in structural and functional brain networks is a central problem. In this article, the preferentially cutting-rewiring operation (PCRO) is introduced to approximatively describe the above physiological process, which consists of the cutting procedure and the rewiring procedure with specific preferential constraints. By applying the PCRO on the classical Erdös-Rényi random network (ERRN), three types of isolated nodes are generated, based on which the common leaves (CLs) are formed between the two hubs. This makes the initially homogeneous ERRN experience drastic changes and become heterogeneous. Importantly, a statistical analysis method is proposed to theoretically analyze the statistical properties of an ERRN with a PCRO. Specifically, the probability distributions of these three types of isolated nodes are derived, based on which the probability distribution of the CLs can be obtained easily. Furthermore, the validity and universality of our statistical analysis method have been confirmed in numerical experiments. Our contributions may shed light on a new perspective in the interdisciplinary field of complexity science and biological science and would be of great and general interest to network physiology.

A study of (2+1)-dimensional variable coefficients equation: Its oceanic solitons and localized wave solutions

In this work, shallow ocean-wave soliton, breather, and lump wave solutions, as well as the characteristics of interaction between the soliton and lump wave in a multi-dimensional nonlinear integrable equation with time-variable coefficients, are investigated. The Painlevé analysis is used to verify the integrability of this model. Based on the bilinear form of this model, we use the simplified Hirota's method obtained from the perturbation approach and various auxiliary functions to construct the aforementioned solutions. Besides, the interaction between the soliton and lump wave solutions is also examined. In addition, by imposing specific constraint conditions on the N-soliton solutions, we further derive higher-order breather solutions. To show the physical characteristics of this model, several graphical representations of the discovered solutions are established. These graphs show that the time-variable coefficients result in a variety of novel dynamic behaviors that differ significantly from those for integrable equations with constant coefficients. The acquired results are useful for the study of shallow water waves in fluid dynamics, marine engineering, nonlinear sciences, and ocean physics.

An Analysis of Phonological Nativisation of English Loanwords in Olunyole: An Optimality Theoretic Approach

This study aims to explain how Olunyole nativises English loanwords into its phonological system. The specific objectives are to describe the phonological strategies involved in the nativisation of English loanwords in Olunyole, and to provide an Optimality theoretic account of the phonological strategies involved in the nativization of English loanwords in Olunyole. The study is anchored within the Optimality theoretic framework (OT). OT is a constraints-based approach which argues for competition between inputs that are parsed through universal and language specific constraints to arrive at a well-formed output in a language. A corpus of 170 English loanwords in Olunyole forms the basis of analysis in this study. This data was collected from native speakers of Olunyole in Luanda Township and Wamasiolo sub-location in Luanda Sub-County-Kenya. Data was collected using Semi-Structured Interviews, Note Taking, and Audio Recording. The findings of this study show that English loanwords are fully nativized into Olunyole grammar through four main ways. First, English sounds that lack a counterpart in the Olunyole sound system are replaced by native Olunyole sounds through substitution. Secondly, vowel epenthesis is used to resolve the incidence of syllable codas in English loanwords. Additionally, consonant clusters in syllable onsets are banned, and such occurrence is resolved through vowel epenthesis. Finally, phonotactic gaps native to Olunyole are maintained in loanwords through segment preservation strategies. The findings of this study show that that Olunyole grammar heavily constrains the nativization of English loanwords. The implications of this study from the analysis of data indicate that the nativisation process in Olunyole is motivated by the phonological system of Olunyole and thus peculiar to Olunyole language, and has little to do with the internally motivated phonological rules of the source language, in this case, English.

Decision‐Making and Path Planning for Head‐On Collision Avoidance on Curved Roads

Deviating to the left on two‐way roads can result in fatal head‐on collisions. This article presents an intelligent decision‐making and path‐planning algorithm aimed at avoiding collision with a vehicle that has deviated from the opposing lane. The path‐planning process utilizes the model predictive control (MPC) approach, employing a linear kinematic prediction model with a horizon of 2 seconds. Considering that the deviated vehicle may abruptly return to its original lane at any moment, its motion is associated with significant uncertainty. To address this challenge, the path‐planning algorithm directs the ego vehicle (EV) under specific constraints to ensure that both the left and right sides of the road are symmetrically reachable in future time steps. This enables the decision‐making algorithm to select the safer direction for evasive maneuver at the appropriate moment. The motion prediction of the threat vehicle (TV) is conducted until the potential collision time, taking into account its motion history, and is utilized in the decision‐making process. Once the maneuver direction is determined, the collision‐free path planning continues using the MPC method. To evaluate the algorithm, six simulations are conducted, modeling various distant and close encounter states of the vehicles on roads with left‐ and right‐hand curves. The simulation results indicate the flexibility and appropriate performance of the algorithm in planning safe and maneuverable paths.

Development of Standardization Concepts for Packaging Machines

Packaging machines are designed to meet specific customer requirements and are usually produced on a one-off basis. To cater to consumer demands, packaging machines have become more and more complex. This extends the time taken for development and amplifies expenses. Therefore, novel approaches for standardization have to be explored beyond the established applications of mass production.To achieve this, an interdisciplinary value stream analysis is carried out within the relevant departments of a packaging machine company to identify specific constraints and requirements. These requirements are structured and documented to provide a foundation for targeted development of standardization concepts. Additionally, new possibilities are created to evaluate the effectiveness of standardization measures in the future and to establish a continual improvement process.The aim of this project is to establish a comprehensive strategy for standardization, enhanced communication, and data management that will be applied at all operational levels of the company. New communication methods, along with participatory and reflective methods, are expected to enhance acceptance by the employees and optimize business processes.

Markov Combinatorial Processes for Reinforcement Learning and Combinatorial Optimization Problems

Solving combinatorial optimization problems is a crucial challenge in many real-world applications. These problems require the optimal choice of combinations from a large set of possibilities, subject to the specific constraints of the problem under consideration, in order to maximize or minimize an objective function. In recent years, Reinforcement Learning (RL) has attracted considerable attention as a potential innovative tool for tackling these complex tasks. The main challenge, to solve combinatorial optimization problems using RL, is related to the need of overcoming the sequential nature of the Markov Decision Processes (MDP) model, on which the solution algorithms are based. In this work, we present an extension of the MDP, that enables software agents to learn from a model, that better reflects the non-sequential nature of these problems. The results demonstrate that, for the first time, a software agent can provide optimal results or, at the very least, solutions with minimal deviation from the optimal values, in the majority of the benchmark instances used in the computational study.

Multi-Phase Vertical Take-Off and Landing Trajectory Optimization with Feasible Initial Guesses

The advancement of electric vertical take-off and landing (eVTOL) aircraft has expanded the horizon of urban air mobility. However, the challenge of generating precise vertical take-off and landing (VTOL) trajectories that comply with airworthiness requirements remains. This paper presents an approach for optimizing VTOL trajectories considering six degrees of freedom (6DOF) dynamics and operational constraints. Multi-phase optimal control problems are formulated to address specific constraints in various flight stages. The incremental nonlinear dynamic inversion (INDI) controller is employed to execute the flight mission in each phase. Controlled flight simulations yield dynamically feasible trajectories that serve as initial guesses for generating sub-optimal trajectories within individual phases. A feasible and sub-optimal initial guess for the holistic multi-phase problem is established by concatenating these single-phase trajectories. Focusing on a tilt-wing eVTOL aircraft, this paper computes VTOL trajectories leveraging the proposed initial guess generation procedure. These trajectories account for complex flight dynamics, align with various operation constraints, and minimize electric energy consumption.

An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning

Integrated access and backhaul (IAB) networks offer transformative benefits, primarily their deployment flexibility in locations where fixed backhaul faces logistical or financial challenges. This flexibility is further enhanced by IAB’s inherent ability for adaptive network expansion. However, existing IAB network planning models, which are grounded in the facility location problem and are predominantly addressed through linear programming, tend to neglect crucial geographical constraints. These constraints arise from the specific deployment constraints related to the positioning of IAB donors to the core network, and the geographic specificity required for IAB-node placements. These aspects expose an evident research void. To bridge this, our research introduces a geographically aware optimization methodology tailored for IAB deployments. In this paper, we detail strategies for both single-hop and multi-hop situations, concentrating on IAB donors distribution and geographical constraints. Uniquely in this study, we employ the inherent data rate limitations of network nodes to determine the maximum feasible hops, differing from traditional fixed maximum hop count methods. We devise two optimization schemes for single-hop and multi-hop settings and introduce a greedy algorithm to effectively address the non-convex multi-hop challenge. Extensive simulations across various conditions (such as diverse donor numbers and node separations) were undertaken, with the outcomes assessed against the benchmark of the single-hop scenario’s optimal solution. Our findings reveal that the introduced algorithm delivers efficient performance for geographically constrained network planning.

The Research on Pricing and Replenishment Optimization of Fresh Supermarket Vegetable Products based on Sales Data

This study delves into the challenges and complexities of vegetable sales management in modern commercial environments, particularly in fresh food supermarkets. By utilizing descriptive statistics and visual analysis of sales data, the study reveals the correlations between different vegetable categories and quantifies these correlations using Pearson's correlation coefficient. Subsequently, by integrating time series analysis and multi-objective programming, a mathematical model is constructed, aimed at maximizing profits under specific constraints. The innovation of this research lies in its comprehensive consideration of category-level sales management and the application of modern optimization algorithms for replenishment and pricing strategies. The uniqueness of this paper is in its integrative approach to the vegetable sales problem, providing refined mathematical models and advanced optimization methods. Finally, the paper thoroughly describes the steps of the research design, including sales data analysis, cost markup analysis, and the construction of an optimization model based on time series analysis and multi-objective programming, intending to offer supermarkets a vegetable sales management plan adaptable to market changes.