The operating temperature of lithium-ion battery (LIB) cells significantly influences their degradation behavior. In indirect liquid cooling systems, temperature variations within a Battery Electric Vehicle (BEV) LIB module are inevitable due to the increasing downstream temperature of the cooling medium as it absorbs heat. This leads to reduced temperature differentials between the cooling medium and the LIB cells. As a result, LIB cells located further along the flow path experience higher average temperatures than those at the front. Typically, a maximum average cell temperature difference of 5 K within LIB modules is considered acceptable. However, results from a conventional cooling system indicate that, when fast charging is exclusively used, this can lead to a 15.5 % difference in the total ampere-hours passed before the End-of-Life (EOL) is reached for the front and back LIB cells. To address this issue, a switchable thermal management system for the traction battery is proposed. In this concept, the coolant inlet flow direction is reversed after every fast charge. Compared to conventional systems, this flow direction switch can increase the total ampere-hours passed by 7.2 %, leading to a corresponding 7.2 % increase in vehicle range until the first cell reaches EOL. The concepts are evaluated using a validated 1D electro-thermal-aging model. Additionally, a 3D electro-thermal Computational Fluid Dynamics (CFD) model of a module section, including a wavy-mini-channel cooling design, is employed. Together, these approaches ensure that the 1D model accurately captures the thermal behavior of the LIB module cells.

While deep learning and backpropagation continue to dominate the field of machine learning in terms of benchmarks and versatility, recent neuroscientific advances shed light on more biologically plausible approaches. Spiking neural networks (SNNs), modelled after action potential dynamics, offer inherent time sensitivity and more efficiency in terms of performance to complexity. While investigating paradigms to support such alternatives, we attempt to answer whether reservoir computing can benefit from a spiking network based implementation with elements of biologically realistic models. This is done by varying both hyperparameters and reservoir generation approaches and comparing implementations to spot potential improvements. We demonstrate how customized training of SNNs can result in competitive performance levels at lower operational complexity and be readily applied to other paradigms, such as the development of reservoir dynamics.

We compute the weight distribution of R(4, 9) by combining the approach described in D. V. Sarwate's Ph.D. thesis from 1973 with knowledge on the affine equivalence classification of Boolean functions. To solve this problem posed, e.g., in the MacWilliams and Sloane book [12, p. 447], we apply a refined approach based on the classification of Boolean quartic forms in eight variables due to Ph. Langevin and G. Leander, and recent results on the classification of the quotient space R(4, 7)/R(2, 7) due to V. Gillot and Ph. Langevin.

Large arrays of 1024 single and 2048 coupled quantum dots (QDs) are characterized at 2-4 K and 300 K for the first time using transport measurements. We demonstrate < 0.2 fA gate leakage current, tunnel current transport through 19 series-connected p-and n-type QDs, 2D-coupling in arrays of 18nm×15nm×6nm QDs with 40nm spacing, back gate selectivity and tuneability of quantum features, and scaling of the gate-voltage difference between Coulomb peaks with increasing gate oxide thickness and decreasing QD size for both p-and n-type structures.

The exploration and categorization of essential and synthetic lethality genes hold significant importance in seeking effective and targeted therapies for diverse ailments. This endeavor hinges upon genetic minimal cut sets (gMCSs), which also find utility in metabolic engineering. There have been various methods suggested for calculating gMCSs. Still, with the emergence of numerous new models and their growing intricacy, it has become vital to introduce new algorithms in this field. This paper presents a new algorithmic approach for computing gMCSs, which utilizes linear programming techniques to improve temporal efficiency. The key concept of the method is to use a k-representative subset to replace the target set with a smaller one.

A document by Shruti Sapra . Click on the document to view its contents.

A document by Jinzheng Zhao . Click on the document to view its contents.

This work presents a procedural method for analytically computing losses in solid conductor air-core coils. The approach was developed for the coaxial subset of air-core coil systems in which hoop-turn, axisymmetric assumptions can be made. It encapsulates loss generally associated with magnetic diffusion, including that induced by rotating magnetic fields from multi-phase current sources, without computing the complex eddy current distribution in a conductor cross-section. Each turn in the system is replaced with an infinitesimally thin circular current loop and is independently excited according to a peak current and phase. Field contributions are summed up at each loop center, with the inclusion of phase to calculate the major and minor components of the resultant magnetic field at each turn. Well established analytic formulae are then used to compute loss accordingly. The approach is significantly faster than 2D axisymmetric finite element analysis (FEA) and greatly improves approximations for losses in isolated coils.

Generative Artificial Intelligence (GenAI) is rapidly being integrated across industries, promising transformative practices in areas such as software development, marketing, healthcare, finance, manufacturing, and customer service, and enhancing efficiency through Human-Artificial Intelligence (HAI) interaction and collaboration. However, one of the major obstacles for fluent HAI interaction is the lack of common context and understanding. Without these, the AI and the user are not guaranteed to be aligned, resulting in inefficient and frustrating collaboration. In this position paper, we propose to integrate model-based understanding of the user into GenAI unlike current use of information retrieval augmented generation in copilots. The model implements hypotheses about human cognition and how human goals, beliefs, and abilities impact behavior and adaptation in interactive environments. This permits the GenAI to infer the latent mental states of the user and predict how the user would adapt to different actions taken by the AI. We emphasize the importance of cognitively informed HAI interaction, advocating for future interdisciplinary research in the intersection of cognitive science and AI, focusing on finding empirical evidence about how GenAIs' ability to infer the latent cognitive states of the user benefits human-GenAI interaction. To facilitate responsible development, implementation and widespread acceptance of cognitively informed AI agents we propose and recommend the CRAFTS AI Framework. Ignoring the principles laid out in the framework could significantly hinder the progress and potential benefits of HAI collaboration.

Bit and power loading (BPL) algorithms played a pivotal role in the success of orthogonal frequency division multiplexing (OFDM) in digital transmission, including lightemitting diode (LED) based wireless optical communications. Nevertheless, the conventional BPL algorithms do not distinguish the nonlinear distortion generated in LED transmitters from an additive noise, which leaves room for improvement. This letter presents a novel power loading and two BPL algorithms that maximize the transmission capacity while minimizing the nonlinear distortion generated in LED. The effectiveness of the proposed algorithms is evaluated through simulations and transmission experiments, revealing a throughput increase of up to 10% in comparison to what can be achieved employing classical algorithms.

The problem of computing machine passing the maze is one of theoretical computer science key tasks. This task was partially considered by the classics of computer science, e.g. A. Turing, C. Shannon, L. Budach and Z. Pawlak. Labyrinth problem solving includes fundamental knowledges in the main three branches, namely  environment (labyrinth) knowledge, computing machines features knowledge and behavior of computing machines in labyrinth knowledge. These three components are built on the fundamental basis of the theoretical computer science. Nowadays the labyrinth passing task is complemented by intellectuality as an additional criterion. That is, computing machines must acquire intelligent functions, and this task gets a new component and a new statement, which is briefly formulated as the search for a way out of the labyrinth by intelligent computing machines. The main concept of this article is to develop artificial neural networks based intelligent functions for calculating machine to pass the maze. Besides this visual aspect is emphasized, i.e. the computational machine includes a maze viewing function. Authors accentuate fundamental principles of artificial neural networks technologies building  and engage backpropagation algorithm, which is used in a artificial neural network learning process. The article discusses maze, dataset construction, artificial neural network training, maze recognition computational experiment and analysis of the hyperparameters effect on artificial neural network training. The aim of the article is to build mathematical model based on backpropagation algorithm and to identify how hyperparameters affect the learning process of artificial neural networks. Thus the research is based on the methods of computational experiment and step-by-step detailing to implement the algorithm. The results of the research include theoretical basis of computing machines intellectualization for the maze and computing machine ability to recognize the maze. These results may be useful to theorists for a detailed description of the process and to practitioners for ability to test this algorithm and approach while solving a problem of maze passing by intelligent agents.

The emergence of 5G systems brought to fore the importance of network slicing (NS) as it allows infrastructure providers (InPs) to create logical networks (slices) and virtually share network resources to their tenants. However, due to the limited resources of the InP, the resource management algorithms like resource allocation and admission control are required to ensure efficient management of these scarce resources. Indeed, admission control algorithms play a critical role of regulating access to the network, by determining whether a slice request should be accepted or not with respect to some standards such as maximizing the InP’s revenue and maintaining service level agreements (SLAs). In this paper, we propose an admission control algorithm that employs the concept of overbooking to admit slice requests beyond the InP’s nominal available resources. Moreover, we employ a dynamic queue adaption priority, step-wise pooling and dynamic buyback price mechanism to ensure efficient and profitable admission decision for the InP. We assess the performance of the proposed algorithm against state of the art (SOTA) solution considering different priority schemes. The results show that the proposed solution outperforms the SOTA solution as it yields i) higher revenue, ii) lower buyback cost and iii) higher net revenue for the InP while still maintaining a marginally higher slice acceptance rate.

Brain tumors are one of the deadliest forms of cancer with a mortality rate of over 80%. A quick and accurate diagnosis is crucial to increase the chance of survival. However, in medical analysis, the manual annotation and segmentation of a brain tumor can be a complicated task. Multiple MRI modalities are typically analyzed as they provide unique information regarding the tumor regions. Although these MRI modalities are helpful for segmenting gliomas, they tend to increase overfitting and computation. This paper proposes a region of interest detection algorithm that is implemented during data preprocessing to locate salient features and remove extraneous MRI data. This decreases the input size, allowing for more aggressive data augmentations and deeper neural networks. Following the preprocessing of the MRI modalities, a fully convolutional autoencoder with soft attention segments the different brain MRIs. When these deep learning algorithms are implemented in practice, analysts and physicians cannot differentiate between accurate and inaccurate predictions. Subsequently, test time augmentations and an energy-based model were used for voxel-based uncertainty predictions. Experimentation was conducted on the BraTS benchmarks and achieved state-of-the-art segmentation performance. Additionally, qualitative results were used to assess the segmentation models and uncertainty predictions. The code for this work is made available online at: https://github.com/WeToTheMoon/BrainTumorSegmentation.

Current research uses TOPSIS to evaluate 14 Cricket World Cup 2023 teams. Data from the Espn Cricinfo website was used in this analysis. A comprehensive set of criteria (P1 to P11) was used to evaluate each squad, encompassing various game aspects. A numerical labeling system (A1 to A14) and parameter system (P1 to P11) were used to idenMfy team names and qualiMes more efficiently. The research calculates the normalized matrix and weighted matrix, then finds the best and worst values using TOPSIS. A normalized matrix creates a consistent and uniform framework for evaluaMng and comparing factors, ensuring imparMality and jusMficaMon. In contrast, the weighted matrix integrates each criterion's proporMonal importance into the evaluaMon process. For each criterion, the ideal best and ideal worst values indicate the best and worst performance. The TOPSIS analysis placed Australia first, Bangladesh second, and New Zealand third. In fourth and fiXh place were India and Sri Lanka. Afghanistan, West Indies, England, South Africa, and Pakistan rated sixth to tenth. Nepal was tenth, Ireland, the US, and Zimbabwe fourteenth.To understand team performance, the TOPSIS technique must be accepted. It is important to acknowledge that the Cricket World Cup 2023 results may vary owing to many factors. This study provides a systematic and comprehensive approach to team performance, making it a useful resource for cricket fans and experts interested in the event's competitive dynamics.

Responsible Artificial Intelligence (RAI) has emerged as a crucial framework for addressing ethical concerns in the development and deployment of Artificial Intelligence (AI) systems. A significant body of literature exists, primarily focusing on either RAI guidelines and principles or the technical aspects of RAI, largely within the realm of traditional AI. However, a notable gap persists in bridging theoretical frameworks with practical implementations in real-world settings, as well as transitioning from RAI to Responsible Generative AI (Gen AI). To bridge this gap, we present this article, which examines the challenges and opportunities in implementing ethical, transparent, and accountable AI systems in the post-ChatGPT era, an era significantly shaped by Gen AI. Our analysis includes governance and technical frameworks, the exploration of explainable AI as the backbone to achieve RAI, key performance indicators in RAI, alignment of Gen AI benchmarks with governance frameworks, reviews of AI-ready test beds, and RAI applications across multiple sectors. Additionally, we discuss challenges in RAI implementation and provide a philosophical perspective on the future of RAI. This comprehensive article aims to offer an overview of RAI, providing valuable insights for researchers, policymakers, users, and industry practitioners to develop and deploy AI systems that benefit individuals and society while minimizing potential risks and societal impacts. A curated list of resources and datasets covered in this survey is available on GitHub1.    

During today’s e-learning era, students often need to opt for self-study sessions to solve word problems. Word problems are generally solved by transforming them into corresponding algebraic equations. This paper presents an approach for automatically generating such algebraic representations for any word problems in the physics domain. The current system first suggests a suitable template to generate an abstract version of the given word problem. These templates are formed with one or more equations, replacing specific values with generic variables. The motivation for choosing such templates was limiting the number of possible templates to the minimum to reduce further ambiguities. Once the templates are predicted, the values for the variables are tagged by parsing the word problems using named entity recognition (NER) based and rule-based approaches. While developing and assessing our system, we collected a database of Physics word problems and asked experts to determine their algebraic representations manually. Inconsistencies are filtered out using proper preprocessing and normalization measures. Although our system is a preliminary step towards automatically solving physics word problems, the template generation and tagging modules performed their corresponding tasks with around 70% accuracy.

The criteria for choosing the number and shape of the subarrays are examined. We propose a possible sub-array configuration that seems capable of offering good performance in terms of jammer and clutter cancellation.

Co-Channel Interference is a classical problem in cellular systems that has been studied extensively and several methods have been proposed to overcome it. These include interference rejection techniques as well as joint detection techniques. We have previously proposed a joint detection technique for MSK-type signals that works quite well in certain conditions. In this paper, we formally present what we call Orthogonal MSK and postulate that if two MSK signals have a 90-degree phase offset between them then both can be detected successfully increasing the spectral efficiency two-fold. This technique works well even if the two signals are near equal power and have the same carrier frequency.