Restrictive Constraints Research Articles

Content-based filter queries on DNA data storage systems

Recent developments in DNA data storage systems have revealed the great potential to store large amounts of data at a very high density with extremely long persistence and low cost. However, despite recent contributions to robust data encoding, current DNA storage systems offer limited support for random access on DNA storage devices due to restrictive biochemical constraints. Moreover, state-of-the-art approaches do not support content-based filter queries on DNA storage. This paper introduces the first encoding for DNA that enables content-based searches on structured data like relational database tables. We provide the details of the methods for coding and decoding millions of directly accessible data objects on DNA. We evaluate the derived codes on real data sets and verify their robustness.

The impact of active and passive investment on market efficiency: a simulation study

ABSTRACT We create a simulated financial market and examine the effect of different levels of active and passive investment on fundamental market efficiency. In our simulated market, active, passive, and random investors interact with each other through issuing orders. Active and passive investors select their portfolio weights by optimizing Markowitz-based utility functions. We find that higher fractions of active investment within a market lead to an increased fundamental market efficiency. The marginal increase in fundamental market efficiency per additional active investor is lower in markets with higher levels of active investment. Furthermore, we find that a large fraction of passive investors within a market may facilitate technical price bubbles, resulting in market failure. By examining the effect of specific parameters on market outcomes, we find that that lower transaction costs, lower individual forecasting errors of active investors, and less restrictive portfolio constraints tend to increase fundamental market efficiency in the market.

Artificial Intelligence In The Big Data Era And Digital Audit

The technology industry has entered the 4.0 era, also known as the Artificial Intelligence (AI) era, which has greatly affected various aspects of human life, particularly with the advancement and growth of big data. The integration of AI in our daily lives, including accounting, has made work more efficient, but also brought both positive and negative impacts. The accounting industry is moving towards the digital audit era, which has transformed the perspective of auditors by digitizing their work and making it easier to access and analyze data. This study aims to investigate the impact of AI development on big data and digital audit, the implications of the gap between big data technology and AI and digital audit, as well as the improvement of auditor's work quality and efficiency due to AI and big data technology. The study utilized both quantitative and qualitative research methods, and concludes that the development of Artificial Intelligence has a significant impact on the work of auditors. However, the study acknowledges its limitations, such as the restricted sample size and time constraints which limited the data analysis to multiple linear regression analysis.

Balanced covering arrays: A classification of covering arrays and packing arrays via exact methods

AbstractIn this paper we investigate the intersections of classes of covering arrays (CAs) and packing arrays (PAs). The arrays appearing in these intersections obey to upper and lower bounds regarding the appearance of tuples in sub‐matrices—we call these arrays balanced covering arrays. We formulate and formalize first observations for which upper and lower bounds on the appearance of tuples it is of interest to consider these intersections of CAs and PAs. Outside of these bounds the intersections will be either empty, for the case of too restrictive constraints, or equal to the maximum element in the emerging lattices, for the case of too weak constraints. We present a column extension algorithm for classification of nonequivalent balanced CAs that uses a SAT solver or a pseudo‐Boolean (PB) solver to compute the columns suitable for array extension together with a lex‐leader ordering to identify unique representatives for each equivalence class of balanced CAs. These computations bring to light a dissection of classes of CAs that is partially nested due to the nature of the considered intersections. These dissections can be trivial, containing only a single type of balanced CAs, or can also appear as highly structured containing multiple nested types of balanced CAs. Our results indicate that balanced CAs are an interesting class of designs that is rich of structure.

Narrowing the coordination solution space during motor learning standardizes individual patterns of search strategy but diversifies learning rates

Constraints on practice can benefit motor learning by guiding the learner towards efficient coordination patterns, but can also narrow the potential solution space of coordination and control. The aim of this paper was to investigate whether narrowing the solution space through more restrictive task constraints limits the expression of potential exploratory behaviours during the learning process, identified using Drifting Markov Models. In a breaststroke swimming task, the change in interlimb coordination of 7 learners practicing for 16 lessons over 2 months was analysed to quantify motor exploration and identify periods of metastable regimes of coordination. Results showed that the observed exploratory dynamics were highly individual both in terms of range of exploration and in the patterns of search. The more restrictive task constraints did not impair the amount of exploration but rather channelled the exploration around a few selected patterns. In addition, restraining the nature of the exploratory process increased the inter-individual differences of the learning rate. Although manipulating the task constraints during learning can help learners to escape from the behavioural consequences of their intrinsic dynamics, maintaining a broad solution space for a diversity of coordination patterns to emerge was key to fostering effective exploration of individual coordination solutions.

The impact of the implementation of ‘double reduction’ policy on tutors in shadow education: legislation goals and early experiences

ABSTRACT Private supplementary tutoring, also known as shadow education, has had a profound influence on Chinese education during the past twenty years. The rapid expansion of shadow education has attracted the attention and increased regulation of the Chinese government. In July 2021, the Chinese government adopted one of the most restrictive legislative constraints on shadow education: the so-called ‘double reduction’ policy. This policy aims to reduce the dual burden for students of on-campus classes and off-campus tutoring. Through analysis of China’s central and provincial double reduction policies, this study examines the policy goals, accompanying instruments, and delineated implementation strategies. Interviews with tutors from shadow education institutions in five major cities in China uncover the strategies shadow education tutors have applied to mitigate the policy constraints and offer criticism of the Chinese political system and deficiencies in the neoliberal education system.

Relational Turbulence: A Latent Model Test of Theoretical Propositions

ABSTRACT In this study we simultaneously tested theoretically specified predictions from relational turbulence theory (RTT) using a fully latent structural regression model. A total of 807 college students in dating relationships responded to survey items measuring variables embedded in RTT, including relationship parameters (relational uncertainty and interdependence), biased cognitive appraisals, emotions, relational communication, and relational turbulence. Results from structural equation modeling revealed that RTT predictions were consistent with our data, but a respecified model with less restrictive constraints was a better fit. This less restrictive model allowed relational uncertainty to predict negative affect, and partner interference and facilitation to predict biased cognitive appraisals. Propositions of RTT were sustained.

Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference (EMI) has become a contentious research problem. In certain mission environments, due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations, the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages. Furthermore, the data interaction between unmanned aerial vehicles (UAVs) can only be performed in specific communication-available stages. Traditional cooperative search algorithms cannot handle such situations well. To solve this problem, this study constructed a distributed model predictive control (DMPC) architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor. An attention mechanism ant-colony optimization (AACO) algorithm is proposed for UAV search-control decision planning. The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information, a priori information, and emergent information of the mission to satisfy different search expectations to the maximum extent. Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.

Restricted orthogonal gradient projection for continual learning

Continual learning aims to avoid catastrophic forgetting and effectively leverage learned experiences to master new knowledge. Existing gradient projection approaches impose hard constraints on the optimization space for new tasks to minimize interference, which simultaneously hinders forward knowledge transfer. To address this issue, recent methods reuse frozen parameters with a growing network, resulting in high computational costs. Thus, it remains a challenge whether we can improve forward knowledge transfer for gradient projection approaches using a fixed network architecture. In this work, we propose the Restricted Orthogonal Gradient prOjection (ROGO) framework. The basic idea is to adopt a restricted orthogonal constraint allowing parameters optimized in the direction oblique to the whole frozen space to facilitate forward knowledge transfer while consolidating previous knowledge. Our framework requires neither data buffers nor extra parameters. Extensive experiments have demonstrated the superiority of our framework over several strong baselines. We also provide theoretical guarantees for our relaxing strategy.

Encryption with User Authentication Model for Internet of Medical Things Environment

Internet of Medical Things (IoMT) enabled e-healthcare has the potential to greately improve conventional healthcare services significantly. However, security and privacy become major issues of IoMT because of the restricted processing abilities, storage, and energy constraints of the sensors. Therefore, it leads to infeasibility of developing traditional cryptographic solutions to the IoMT sensors. In order to ensure security on sensitive medical data, effective encryption and authentication techniques need to be designed to assure security of the patients and healthcare service providers. In this view, this study designs an effective metaheuristic optimization based encryption with user authentication (EMOE-UA) technique for IoMT environment. This work proposes an EMOE-UA technique aims to accomplish mutual authentication for addressing the security issues and reducing the computational complexity. Moreover, the EMOE-UA technique employs optimal multikey homomorphic encryption (OMKHE) technique to encrypt the IoMT data. Furthermore, the improved social spider optimization algorithm (ISSOA) was employed for the optimal multikey generation of the MKHE technique. The experimental result analysis of the EMOE-UA technique takes place using benchmark data and the results are examined under various aspects. The simulation results reported the considerably better performance of the EMOE-UA technique over the existing techniques.

Automatic model training under restrictive time constraints

We develop a hyperparameter optimisation algorithm, Automated Budget Constrained Training, which balances the quality of a model with the computational cost required to tune it. The relationship between hyperparameters, model quality and computational cost must be learnt and this learning is incorporated directly into the optimisation problem. At each training epoch, the algorithm decides whether to terminate or continue training, and, in the latter case, what values of hyperparameters to use. This decision weighs optimally potential improvements in the quality with the additional training time and the uncertainty about the learnt quantities. The performance of our algorithm is verified on a number of machine learning problems encompassing random forests and neural networks. Our approach is rooted in the theory of Markov decision processes with partial information and we develop a numerical method to compute the value function and an optimal strategy.

Energy and environmental efficiency of China's regional electric power industry by considering renewable energy constraints

The renewable electricity quota standard (RES) policy plays an important role in achieving sustainable and green development targets. Thus, it is essential to measure the sustainability of the electric power sectors in the context of RES. In terms of the energy and environmental efficiency (EEE) measurements, few studies divided energy inputs into renewable and non-renewable energy or explored the restricted problem of minimizing resource summation. To fill these gaps, we propose an improved fix-sum energy input data envelopment analysis (FSIDEA) model based on the adjustment strategy of weighted sum minimization of renewable energy inputs. The assurance region (AR) restriction and renewable energy input sum constraint (IC) are, respectively, imposed on the FSIDEA model to achieve the common equilibrium effective frontier (EEF). Finally, this study assesses the EEE of the electric power sectors in China's 30 main regions based on the common EEF. The result reveals that Beijing and Shanghai can be selected as benchmarks after efficiency comparison. In addition, our proposed model can compressively rank all decision-making units (DMUs) compared with traditional efficiency models.

An ITS System for Reducing Congestion and Noise Pollution due to Vehicles to/from Port Terminals

This paper deals with a new ITS system aimed at reducing road congestion and noise emissions on the urban roads leading to port terminals. This system is composed of: traffic video cameras, VMS panels and an app. These three components are connected and managed by an integrated ITS system management platform, called “LIST Port ITS System Central” in this paper. Video cameras measure traffic characteristics, such as flow rates, speed and composition of the vehicle stream and provide these data to the LIST Port ITS System Central. The central elaborates the data and provide the traffic flow characteristics to the app. The app calculates, in real time, the best route to/from port terminals, according to traffic congestion and noise emissions, and provides this information to the Central. Then, the app shows to the user the best route and the traffic and noise status in real time. The calculation of the best routes takes place according to the “physical capacity” and “acoustic capacity” of road infrastructures. Noise emissions are directly calculated from traffic characteristics by means of the CNOSSOS-EU model, and are after compared to the noise emission limit levels established by law. The services provided by the LIST Port ITS system are new, because, currently, routing strategies are usually based only on congestion, while noise pollution is almost always neglected. Noise pollution could be a more restrictive constraint because the “acoustic capacity” is often lower than the physical one.

Review: The Dreamer and the Dream: Afrofuturism and Black Religious Thought, by Roger A. Sneed

Review: <i>The Dreamer and the Dream: Afrofuturism and Black Religious Thought</i>, by Roger A. Sneed

A novel power allocation strategy for cooperative PDMA systems

Cooperative communication technology is of great importance for increasing the user reachable rate, further improving throughput and reducing the outage probability of non-orthogonal multiple access (NOMA) systems. This paper mainly studies the power allocation optimization method based on amplify-and-forward (AF) pattern division multiple access (PDMA) to obtain the maximum achievable throughput. We formulate an optimization problem of user power allocation in a downlink PDMA system with cooperative relaying, the exact expressions of system throughput and user outage probability of the AF-PDMA system are derived, and a novel power allocation optimization method based on uniform distribution and restricted constraints is proposed. The effectiveness of the restricted constraints and optimization method is verified by theoretical analysis and simulation. The studies we have performed showed that the proposed scheme with uniform distribution and restricted constraints can be significantly improved in terms of the system throughput in comparison to the case with a genetic algorithm (GA) and fixed power allocation scheme. Concerning the proposed method, the search space is reduced to 1/3 of the original feasible region, and the runtime of the algorithm accounts for only 20% of the GA runtime.

An unusual mode of baseline translation adjusts cellular protein synthesis capacity to metabolic needs.

In all domains of life, mechanisms exist that adjust translational capacity to nutrient restriction and other growth constraints. The mammalian target of rapamycin (mTOR) regulates the synthesis of ribosomal proteins and translation factors in mammalian cells via phosphorylation of the La-related protein 1 (LARP1). In the present model of starvation-induced translational silencing, LARP1 targets mRNAs carrying a 5' terminal oligopyrimidine (5'TOP) motif to shift these into subpolysomal ribonucleoprotein particles. However, how these mRNAs would be protected from degradation and rapidly made available to restore translation capacity when needed remained enigmatic. Here, to address this, we employ gradient profiling by sequencing (Grad-seq) and monosome footprinting. Challenging the above model, we find that 5'TOP mRNAs, instead of being translationally silenced during starvation, undergo low baseline translation with reduced initiation rates. This mode of regulation ensures a stable 5'TOP mRNA population under starvation and allows fast reversibility of the translational repression.

LINEAR PROGRAMMING MODEL OF PRODUCTION, INVENTORY AND DISTRIBUTION PROBLEM BASED ON RANDOM SAMPLING

A linear programming model using the random sampling technique was used for the optimization of the products in the production line of Nigerian Bottling Company (NBC), Lagos, which has many production facilities and multi-products systems. Products from the company are distributed to a number of depots across the country of which the demand for each product is known. The problem of interest involves determining what product/s should be made, how much of each product should be produced and where production should take place. The objectives of the company are to minimize the total cost as well as maximize the total sales revenue based on the set of decisions including demands, capacity restriction and budget constraints. Linear Programming software solver (LiPs) was used to solve large linear programming problems based on the mathematical model developed. The model improved the profit of the company under study by 5%.

A detailed analysis of the Gl 486 planetary system

Context.The Gl 486 system consists of a very nearby, relatively bright, weakly active M3.5 V star at just 8 pc with a warm transiting rocky planet of about 1.3R⊕and 3.0M⊕. It is ideal for both transmission and emission spectroscopy and for testing interior models of telluric planets.Aims.To prepare for future studies, we aim to thoroughly characterise the planetary system with new accurate and precise data collected with state-of-the-art photometers from space and spectrometers and interferometers from the ground.Methods.We collected light curves of seven new transits observed with the CHEOPS space mission and new radial velocities obtained with MAROON-X at the 8.1 m Gemini North telescope and CARMENES at the 3.5 m Calar Alto telescope, together with previously published spectroscopic and photometric data from the two spectrographs and TESS. We also performed near-infrared interferometric observations with the CHARA Array and new photometric monitoring with a suite of smaller telescopes (AstroLAB, LCOGT, OSN, TJO). This extraordinary and rich data set was the input for our comprehensive analysis.Results.From interferometry, we measure a limb-darkened disc angular size of the star Gl 486 atθLDD= 0.390 ± 0.018 mas. Together with a correctedGaiaEDR3 parallax, we obtain a stellar radiusR*= 0.339 ± 0.015R⊕. We also measure a stellar rotation period atProt= 49.9 ± 5.5 days, an upper limit to its XUV (5-920 A) flux informed by newHubble/STIS data, and, for the first time, a variety of element abundances (Fe, Mg, Si, V, Sr, Zr, Rb) and C/O ratio. Moreover, we imposed restrictive constraints on the presence of additional components, either stellar or sub-stellar, in the system. With the input stellar parameters and the radial-velocity and transit data, we determine the radius and mass of the planet Gl 486 b atRp= 1.343−0.062+0.063R⊕andMp= 3.00−0.12+0.13M⊕, with relative uncertainties of the planet radius and mass of 4.7% and 4.2%, respectively. From the planet parameters and the stellar element abundances, we infer the most probable models of planet internal structure and composition, which are consistent with a relatively small metallic core with respect to the Earth, a deep silicate mantle, and a thin volatile upper layer. With all these ingredients, we outline prospects for Gl 486 b atmospheric studies, especially with forthcomingJames WebbSpace Telescope (Webb) observations.

Obstacle avoidance flocking motion in multi-agent systems with limited sensing radius and heterogeneous input constraints

This paper addresses the leaderless and leader-followers flocking motion problems of multi-agent systems in a workspace with obstacles. We consider inertial agents with equally limited communication radius and heterogeneous input constraints. Using the notion of proximity graphs, we provide distributed controllers to induce the desired flocking behaviour. Unlike previous results, our controller designs do not require predicted states or a target determination scheme and allow edge deletions. Also, by taking advantage of the group's heterogeneity, our controllers allow agents with less restrictive input constraints to compensate for the control effort that their less capable neighbours cannot provide. We illustrate the effectiveness of our proposals through numerical simulations.

Constrained Layer Assignment for the Protein Burial Folding Code Accounting for Chain Connectivity.

The connection between protein sequences and tertiary structures has intrigued investigators for decades. A plausible hypothesis for the coding scheme postulates that atomic burial information obtainable from the sequence could be sufficient for structural determination when combined to sequence-independent constraints. Accordingly, folding simulations using native burial information expressed by atomic central distances, discretized into a small number L of equiprobable burial layers, have indeed been successful in reaching and distinguishing the native structure of several globular proteins. Attempted predictions of layers from sequence, however, turned out to be insufficiently accurate for most proteins. Here we explore the possibility that a nonuniform assignment of layers, which is intended to account for constraints imposed by chain connectivity, might provide a more efficient burial encoding of tertiary structures. We consider the condition that adjacent Cα-atoms along the sequence cannot occupy nonadjacent layers, in which case the information required to specify sequences of burials would be smaller. It is shown that appropriate folding behavior can still be observed in this explicitly more constrained scenario with a structure-dependent assignment intended to produce the thinnest possible layers still compatible with the imposed burial constraint. This thinnest assignment turns out to be sufficiently restrictive for the observed examples and provides appropriately thinner layers or, equivalently, a larger number of layers, for examples previously observed to indeed require more restrictive constraints when compared to counterparts of similar size, as well as the appropriate increase in number of layers for larger proteins. Implications for the general understanding of the protein folding code are discussed.