Traditional Problems Research Articles

Designing new types of problems using peer-reviewed papers

In the 21st century teachers, including university teachers, have to help students develop higher-level thinking skills, conceptual understanding, and problem-solving strategies similar to those used by experts. Previous research shows that solving traditional physics problems, which usually have one right answer and provide all the given and relevant assumptions, do not help students develop those skills. Thus, new types of problems have to be designed. We show two examples of how new types of problems can be designed using peer-reviewed research papers as the basis and share the experience with the evolution of those problems. In addition, we show that the students included in the research appreciate the structure and the context of the problems and that the faculty colleagues who are using traditional teaching approach identify several skills and competencies which these problems develop, and which cannot be developed by solving traditional problems.

Consubstantial dualism: a Zoroastrian perspective on the soul

AbstractThis article describes the group of ninth-century Zoroastrian philosophers I call the ‘Dēnkard School’ and sketches the way they do philosophy. It presents their argument against substance dualism, which the Zoroastrians argue is in tension with the belief in repentance. From an analysis of this polemic, there follows a reconstruction of the Dēnkard School's own doctrine of the consubstantiality of body and soul. To understand these arguments, I describe some background eschatological and ontological beliefs upheld by the Dēnkard School and their specific conception of substance, which includes the notions of ownership and responsibility. Overall, the argument can be seen as a new position on a traditional problem, and so increasing the scope of philosophy in a more global perspective.

A Big Data Framework to Address Building Sum Insured Misestimation

In the insurance industry, the accumulation of complex problems and volume of data creates a large scope for actuaries to apply big data techniques to investigate and provide unique solutions for millions of policyholders. With much of the actuarial focus on traditional problems like price optimisation or improving claims management, there is an opportunity to tackle other known product inefficiencies with a data-driven approach. The purpose of this paper is to build a framework that exploits big data technologies to measure and explain Australian policyholder Sum Insured Misestimation (SIM). Big data clustering and dimension reduction techniques are leveraged to measure SIM for a national home insurance portfolio. We then design predictive and prescriptive models to explore the relationship between socioeconomic and demographic factors with SIM. Real-world results from a national home insurance portfolio provide actionable business insight on SIM and facilitate solutions for stakeholders, being government and insurers.

Gamification in infrastructure and its application in Vietnam

In the Fourth Industrial Revolution, innovative approaches that are emerging to solve traditional problems are regularly updated and improved. Prominent among them is the gamification application in the infrastructure construction industry. This is an intuitive simulation tool that brings a high level of experience and efficiency. The article presented an overview of the gamification application in infrastructure. It can be seen that with the development of modern game engine and new technologies such as Building Information Modelling (BIM), Virtual Reality (VR), Mixed Reality (MR), Augmented Reality (AR), digital twin, gamification is used in different phases of a project such as design, construction and operation. Furthermore, the article also provides an assessment of opportunities and challenges when applying gamification to the infrastructure construction in Vietnam. Furthermore, some preliminary suggestions to improve the application efficiency are also made.

Survival Prediction After Transarterial Chemoembolization for Hepatocellular Carcinoma: a Deep Multitask Survival Analysis Approach.

The accurate prediction of postoperative survival time of patients with Barcelona Clinic Liver Cancer (BCLC) stage B hepatocellular carcinoma (HCC) is important for postoperative health care. Survival analysis is a common method used to predict the occurrence time of events of interest in the medical field. At present, the mainstream survival analysis models, such as the Cox proportional risk model, should make strict assumptions about the potential random process to solve the censored data, thus potentially limiting their application in clinical practice. In this paper, we propose a novel deep multitask survival model (DMSM) to analyze HCC survival data. Specifically, DMSM transforms the traditional survival time prediction problem of patients with HCC into a survival probability prediction problem at multiple time points and applies entropy regularization and ranking loss to optimize a multitask neural network. Compared with the traditional methods of deleting censored data and strong hypothesis, DMSM makes full use of all the information in the censored data but does not need to make any assumption. In addition, we identify the risk factors affecting the prognosis of patients with HCC and visualize the importance of ranking these factors. On the basis of the analysis of a real dataset of patients with BCLC stage B HCC, experimental results on three different validation datasets show that the DMSM achieves competitive performance with concordance index of 0.779, 0.727, and 0.780 and integrated Brier score (IBS) of 0.172, 0.138, and 0.135, respectively. Our DMSM has a comparatively small standard deviation (0.002, 0.002, and 0.003) for IBS of bootstrapping 100 times. The DMSM we proposed can be utilized as an effective survival analysis model and provide an important means for the accurate prediction of postoperative survival time of patients with BCLC stage B HCC.

Optimal Train Platforming with Shunting Operations for Multidirectional Passenger Stations: A Case Study of Guangzhou Station

Busy, complex railway stations that serve as origin and termination points for a significant proportion of trains are essential to regional railway networks. Resolving conflicts between arrival–departure operations and shunting operations of cross-line trains and originating or terminating passenger trains in the throat area is important for safety in these multidirectional stations. The main task of this paper is to study the train platforming problem, and we consider the integration of track and route allocation with shunting route allocation on the basis of the traditional TTP problem, so as to formulate a strong anti-interference track allocation plan for busy, complex railway stations. Therefore, in view of the complex characteristics of train operation in busy, complex railway stations, we extensively examine the technical operational characteristics of various trains in multidirectional stations, which are the key constraints of the model, and establish a mixed-integer linear programming model. This model aims to balance the buffer time for track occupation and optimize the routing and scheduling of trains in stations. Furthermore, an improved genetic algorithm is proposed to effectively implement the developed model. In the case study of Guangzhou Station, the occupation analysis after the optimization of the method in this paper indicates that the shunting operations significantly interfere with arrival–departure operations in throat areas. The optimization of buffer times and track utilization times resulted in notable reductions of 30.55% and 77.82%, respectively, in quadratic differences. These outcomes provide empirical evidence supporting the feasibility of the proposed model and algorithm for addressing train platforming problems, particularly in complex, multidirectional, and heavily trafficked railway stations.

A Certificateless Signcryption Mechanism Based on Blockchain for Edge Computing

The emergence of edge computing makes it possible to realize new technologies such as virtual reality and augmented reality. However, a large number of devices and more messages at the edge bring more security problems. Therefore, it is an important research topic to provide users with faster network services while ensuring confidentiality and authentication of data transmission. Because signcryption can encrypt and sign messages at the same time, it has become a new cryptographic primitive. In the meantime, certificateless signcryption guarantees data confidentiality and authentication and addresses traditional single point failure problems based on the trust center and the problem of relying on a trusted third party. Therefore, certificateless signcryption has attracted great attention from academia and industry. But certificateless signcryption also faces two types of attacks. In order to more effectively resist these two types of attacks, we propose a certificateless signcryption mechanism based on blockchain. This mechanism can make good use of the nontamperable feature of blockchain, prevent illegal users from substituting public key of the user, and guarantee signature non-repudiation. And our scheme is investigated in a comparative study with eight schemes. Comparative analysis outcomes demonstrate our scheme has achieved better results in efficiency and security. The process of signcryption and unsigncryption consumes the least amount of computation, which is very suitable for the edge computing environment.

Internal Light Sources-Mediated Photodynamic Therapy Nanoplatforms: Hope for the Resolution of the Traditional Penetration Problem.

Photodynamic therapy (PDT) is an alternative cancer treatment technique with a noninvasive nature, high selectivity, and minimal adverse effects. The indispensable light source used in PDT is a critical factor in determining the energy conversion of photosensitizers (PSs). Traditional light sources are primarily concentrated in the visible light region, severely limiting their penetration depth and making them prone to scattering and absorption when applied to biological tissues. For that reason, its efficacy in treating deep-seated lesions is often inadequate. Self-exciting PDT, also known as auto-PDT (APDT), is an attractive option for circumventing the limited penetration depth of traditional PDT and has acquired significant attention. APDT employs depth-independent internal light sources to excite PSs through resonance or radiative energy transfer. APDT has considerable potential for treating deep-tissue malignancies. To facilitate many researchers' comprehension of the latest research progress in this field and inspire the emergence of more novel research results. This review introduces internal light generation mechanisms and characteristics and provides an overview of current research progress based on the recently reported APDT nanoplatforms. The current challenges and possible solutions of APDT nanoplatforms are also presented and provide insights for future research in the final section of this article.

Therapeutic effects of engineered exosome-based miR-25 and miR-181a treatment in spinocerebellar ataxia type 3 mice by silencing ATXN3

BackgroundSpinocerebellar ataxia type 3 (SCA3) is the most common autosomal dominant hereditary ataxia worldwide, which is however in a lack of effective treatment. In view of that engineered exosomes are a promising non-invasive gene therapy transporter that can overcome the traditional problem of poor drug delivery, the aim of this study was to evaluate, for the first time, the value of exosome-based microRNA therapy in SCA3 and the therapeutic effects of intravenously administrated ATXN3 targeting microRNAs in transgenic SCA3 mouse models.MethodsThe rabies virus glycoprotein (RVG) peptide–modified exosomes loaded with miR-25 or miR-181a were peripherally injected to enable targeted delivery of miRNAs to the brain of SCA3 mice. The behaviors, ATXN3 level, purkinje cell and other neuronal loss, and neuroinflammation were evaluated 4 weeks after initial treatment.ResultsThe targeted and efficient delivery of miR-25 and miR-181a by modified exosomes substantially inhibited the mutant ATXN3 expression, reduced neuron apoptosis and induced motor improvements in SCA3 mouse models without increasing the neuroinflammatory response.ConclusionsOur study confirmed the therapeutic potential of engineered exosome-based miR-25 and miR-181a treatment in substantially reducing ATXN3 aggregation and cytotoxicity by relying on its targeted and efficient drug delivery performance in SCA3 mice. This treatment method shows a promising prospect for future clinical applications in SCA3.

Multi-Objective Optimal Power Flow Incorporating Flexible Alternating Current Transmission Systems: Application of a Wavelet-Oriented Evolutionary Algorithm

This article presents a meta-heuristic hybrid algorithm to solve multi-objective optimal power flow (MOOPF) taking into account multi-fuel constraint. Four different objective functions, which comprise total generation cost, emission, real power transmission losses, and voltage deviation, are simultaneously considered in the proposed MOOPF. Furthermore, two types of flexible alternating current transmission system (FACTS) devices are utilized including thyristor controlled series compensator (TCSC) and unified power flow controller (UPFC). Considering all of the aspects referred to above makes the traditional OPF problem more complicated; hence, it is necessary to approach the problem by a powerful optimization algorithm. Toward this end, this article develops a hybrid algorithm oriented to shuffled frog leaping algorithm (SFLA) and particle swarm optimization (PSO), called hybrid wavelet mutation (WM) based SFLA-wavelet mutation based PSO (HWM2SFLA-PSO). Obtained results validate the effectiveness of the proposed hybrid algorithm in solving different forms of the OPF problem on IEEE test systems compared to those available in the literature. Moreover, the comparison substantiates the superiority of the proposed HWM2SFLA-PSO algorithm in terms of execution time, quality of solution as well as preventing from convergence to local optima, to name but three.

Theory languages in designing artificial intelligence

The foundations of AI design discourse are worth analyzing. Here, attention is paid to the nature of theory languages used in designing new AI technologies because the limits of these languages can clarify some fundamental questions in the development of AI. We discuss three types of theory language used in designing AI products: formal, computational, and natural. Formal languages, such as mathematics, logic, and programming languages, have fixed meanings and no actual-world semantics. They are context- and practically content-free. Computational languages use terms referring to the actual world, i.e., to entities, events, and thoughts. Thus, computational languages have actual-world references and semantics. They are thus no longer context- or content-free. However, computational languages always have fixed meanings and, for this reason, limited domains of reference. Finally, unlike formal and computational languages, natural languages are creative, dynamic, and productive. Consequently, they can refer to an unlimited number of objects and their attributes in an unlimited number of domains. The differences between the three theory languages enable us to reflect on the traditional problems of strong and weak AI.

ДИНАМИЧЕСКОЕ МОДЕЛИРОВАНИЕ ИНТЕНСИВНОСТИ ТРАНСПОРТНОГО ПОТОКА В ЗАДАЧЕ НАЗНАЧЕНИЯ

The paper presents a variant of solving the traditional assignment problem. In such models, the traffic flow is taken into account at all its links, since the cars are tied to the route. Consider a variant of the model with the study of the traffic flow in space over time as the third dimension

Electronic problem book on design patterns: Implementation and use

The article is dedicated to the problems associated with organizing the practical component of object-oriented programming (OOP) courses. A review of current learning resources for OOP courses reveals a lack of learning assignments related to design patterns, which are essential components of modern OOP theory. To solve this problem, the author proposes using a specialized training resource — an electronic problem book on object-oriented design patterns. The features of the design patterns e-taskbook are described in detail and an example of its implementation is given: the Programming Taskbook for OOP, which includes tasks related to 21 classic design patterns, as well as an introductory group of tasks dedicated to the principles and techniques of OOP.The article emphasizes the novelty of this approach, noting that unlike traditional problem books, the Programming Taskbook for OOP not only contains the wording of tasks, but also provides the courseware with input data and automatic verification of the proposed solution. Another important feature of the book is its versatility: the tasks can be performed in different languages and different programming environments, which allows to easily adapt the book for use in various undergraduate and graduate courses. Finally, the author presents his experience using the Programming Taskbook in OOP courses.

Lead-Time-Constrained Middle-Mile Consolidation Network Design with Fixed Origins and Destinations

Many large e-commerce retailers move sufficient freight volumes to operate private middle-mile consolidation networks for order fulfillment, transporting customer shipments from stocking locations to last-mile delivery partners in consolidated loads to reduce freight costs. We study a middle-mile network design optimization problem with fixed origins and destinations to build load consolidation plans that minimize cost and satisfy customer shipment lead-time constraints. We propose models that extend traditional flat network service network design problems to capture waiting delays between load dispatches and ensure that shipment lead-time requirements are satisfied with a desired probability. We approximate these chance constraints using hyperparameterized linear constraints, resulting in new mixed-integer programs (MIPs) for service network design. To find high-quality solutions to the proposed MIPs, we develop an effective integer-programming-based local search (IPBLS) heuristic that iteratively improves a solution by optimizing over a smartly selected subset of commodities. For the largest problem instances, we propose a two-phase IPBLS heuristic that first utilizes a simplified, restricted MIP that constrains leg waiting delays individually. Computational experiments using data from a large U.S.-based e-commerce partner demonstrate the significant impact of tight lead-time constraints on the structure of the consolidation network designs and their concomitant operating costs. Notably, tighter constraints lead to solutions with increased shipment consolidation and higher dispatch frequencies on selected key transportation lanes. Such solutions trade off higher shipment transit times with significantly reduced shipment waiting times to meet lead-time constraints at lower cost.

A multi-agent deep reinforcement learning approach for solving the multi-depot vehicle routing problem

The multi-depot vehicle routing problem (MDVRP) is one of the most essential and useful variants of the traditional vehicle routing problem (VRP) in supply chain management (SCM) and logistics studies. Many supply chains (SC) choose the joint distribution of multiple depots to cut transportation costs and delivery times. However, the ability to deliver quality and fast solutions for MDVRP remains a challenging task. Traditional optimization approaches in operation research (OR) may not be practical to solve MDVRP in real-time. With the latest developments in artificial intelligence (AI), it becomes feasible to apply deep reinforcement learning (DRL) for solving combinatorial routing problems. This paper proposes a new multi-agent deep reinforcement learning (MADRL) model to solve MDVRP. Extensive experiments are conducted to evaluate the performance of the proposed approach. Results show that the developed MADRL model can rapidly capture relative information embedded in graphs and effectively produce quality solutions in real-time.

An Escalated Eavesdropping Attack on Mobile Devices via Low-Resolution Vibration Signals

With the global prevalence of mobile devices, concerns about mobile devices regarding privacy breaches and data leakage are rising. Although sensor permissions are required for mobile applications to access outputs of built-in sensors, motion sensors (e.g., accelerometer and gyroscope) can be visited directly without permission requirement. Extant studies have shown that motion sensors may cause breaches of confidential information, such as passwords, digits, and voice-based commands, but whether it is possible to synthesize intelligible speech waveforms from low-resolution motion sensors has been understudied. In this paper, we present an escalated side-channel attack of built-in speakers by synthesizing intelligible speech waveforms from low-resolution vibration signals. Opposite to traditional classification problems, we formulate this task as a generative problem and introduce an end-to-end synthesis framework dubbed as <i>AccMyrinx</i> to eavesdrop on the speaker via the low-resolution vibration signals. In <i>AccMyrinx</i>, we introduce the data alignment solution to provide the pair-wise voice-vibration sequences and present wavelet-based MelGAN (WMelGAN) with multi-scale time-frequency domain discriminators to generate intelligible acoustic waveforms. We conducted intensive experiments and demonstrated the feasibility of synthesizing the intelligible acoustic signals from low-resolution solid-borne vibration signals. Compared with existing synthesis solutions, our proposed solution outperforms the baselines in both subject and object metrics with the smoothed word error rate of 42.67&#x0025; and the Mel-Cepstral distortion of 0.298. In addition, the quality of synthetic speeches could be impacted by several factors, including gender, speech rate, volume, and sampling frequency.

Drone Optimization in Factory: Exploring the Minimal Level Vehicle Routing Problem for Efficient Material Distribution

The efficient movement of raw materials within organizations is fundamental to maintaining the seamless progression of production processes. However, these logistical operations can inadvertently compromise overall company efficiency, primarily due to the substantial time invested in transporting materials. This paper introduces an innovative mathematical model specifically designed to optimize the transport of raw materials via drones across multiple workstations. This model employs a novel modification of the traditional multi-level Vehicle Routing Problem by incorporating an additional index and accounting for the drone’s energy consumption. We employ a widely-recognized solver for practical resolution and compare it with a heuristic algorithm. The resultant strategies offer promising prospects for the organization studied, introducing robust solutions for elevating the efficiency of raw material transportation.

Cross-Modality Person Re-identification with Memory-Based Contrastive Embedding

Visible-infrared person re-identification (VI-ReID) aims to retrieve the person images of the same identity from the RGB to infrared image space, which is very important for real-world surveillance system. In practice, VI-ReID is more challenging due to the heterogeneous modality discrepancy, which further aggravates the challenges of traditional single-modality person ReID problem, i.e., inter-class confusion and intra-class variations. In this paper, we propose an aggregated memory-based cross-modality deep metric learning framework, which benefits from the increasing number of learned modality-aware and modality-agnostic centroid proxies for cluster contrast and mutual information learning. Furthermore, to suppress the modality discrepancy, the proposed cross-modality alignment objective simultaneously utilizes both historical and up-to-date learned cluster proxies for enhanced cross-modality association. Such training mechanism helps to obtain hard positive references through increased diversity of learned cluster proxies, and finally achieves stronger ``pulling close'' effect between cross-modality image features. Extensive experiment results demonstrate the effectiveness of the proposed method, surpassing state-of-the-art works significantly by a large margin on the commonly used VI-ReID datasets.

Task-Specific Scene Structure Representations

Understanding the informative structures of scenes is essential for low-level vision tasks. Unfortunately, it is difficult to obtain a concrete visual definition of the informative structures because influences of visual features are task-specific. In this paper, we propose a single general neural network architecture for extracting task-specific structure guidance for scenes. To do this, we first analyze traditional spectral clustering methods, which computes a set of eigenvectors to model a segmented graph forming small compact structures on image domains. We then unfold the traditional graph-partitioning problem into a learnable network, named Scene Structure Guidance Network (SSGNet), to represent the task-specific informative structures. The SSGNet yields a set of coefficients of eigenvectors that produces explicit feature representations of image structures. In addition, our SSGNet is light-weight (56K parameters), and can be used as a plug-and-play module for off-the-shelf architectures. We optimize the SSGNet without any supervision by proposing two novel training losses that enforce task-specific scene structure generation during training. Our main contribution is to show that such a simple network can achieve state-of-the-art results for several low-level vision applications including joint upsampling and image denoising. We also demonstrate that our SSGNet generalizes well on unseen datasets, compared to existing methods which use structural embedding frameworks. Our source codes are available at https://github.com/jsshin98/SSGNet.

Data security of machine learning applied in low-carbon smart grid: A formal model for the physics-constrained robustness

Towards the low-carbon goal, a smart grid features remote connection, data sharing, and cyber–physical integration to increase the flexibility of energy supplies, to reduce electricity wastage, and to enhance safety under intermittent renewables. In this context, machine learning (ML) is increasingly employed in smart grids to solve tasks that require deep data analytics. However, it is well recognized that ML models are vulnerable to adversarial examples that might be contained in open shared data, which creates opportunities for potential attackers to launch cyberattacks. In particular, system operating states acting as critical inputs to ML-based smart grid applications (MLsgAPPs) can be manipulated by malicious actors to mislead the system operator into making wrong decisions that in turn may cause major blackouts and other damaging cascading events. It is thus imperative to advance vulnerability assessment for MLsgAPPs.In this paper, we propose a physics-constrained robustness evaluation framework for MLsgAPPs, which is a first attempt of using tree ensemble (TE) models. Unlike adversarial attacks against prior studied image-classification tasks, adversarial examples against TE-based smart grid applications (TEsgAPPs) must not only cheat human intuition but also follow laws of physics and bypass error-checking mechanisms for power systems. Thus, formulation of the traditional robustness evaluation problem must be reconsidered to account for domain-specific misclassification, physical limit, power balance, and error-bypass constraints, in order to obtain tight lower bounds of the magnitudes of tolerated adversarial perturbations. We adopt a formal modeling approach to construct a discrete tree structure and to specify the model evasion conditions. We propose an efficient robustness evaluation method by transforming variables considering ℓ1 and ℓ∞ norms, followed by a generalization that expands the variable space to admit other types of norms. Using TE-based power system security assessment as an example, comprehensive simulations are conducted for evaluating the physics-constrained robustness under different TE models and model parameters.