Heart failure (HF) is considered a global pandemic because of increasing prevalence, high mortality rate, frequent hospitalization and associated economic burden. This study explores a noninvasive method that may help in managing HF patients by predicting HF readmission. Methods: Seismocardiogram (SCG) signal is the low frequency chest vibration produced by the mechanical activity of heart. SCG signal was acquired from 101 patients with HF including in those readmitted to the hospital during the study period. Features were extracted from SCG signals. Several conventional machine learning (ML) models were developed using selected SCG and heart rate variability features. Furthermore, SCG signals were transformed into images using a time-frequency distribution method. Images were used to train a deep learning model. The models were able to predict the readmission status of the HF patients. Results: ML algorithms achieved higher accuracy than the deep learning model in classifying the readmitted and non-readmitted HF patients. K-nearest neighbor (KNN) achieved the highest classification accuracy (89.4% accuracy, 87.8% sensitivity, and 90.1% specificity). The study results suggest that SCG signal may be useful for readmission prediction of HF patients. Significance: Use of SCG signal may help the management of HF patients and improve their quality of life.

Convolutional Neural Networks (CNNs) have opened up new possibilities in a variety of fields, including plant pathology. Through image analysis, this study aims to take advantage of CNNs' capacity to recognize and categorize plant illnesses. Automated disease identification has potential for prompt intervention and crop preservation in modern times, when agricultural output assumes critical relevance. This work elaborates on the application and assessment of a CNN-based model designed for the detection of plant diseases. The technology separates healthy plants from those with problems by taking pictures of plants and putting them through the trained model. The growing demand for effective disease monitoring and management in the agriculture industry highlights the urgency of this research. The suggested model shows promising accuracy and dependability by utilising a large dataset and stringent evaluation metrics. This research highlights the potential of CNNs as a workable tool for upgrading agricultural practices by using a systematic approach. The results of this study shed light on both the strengths and weaknesses of CNNs in the context of automated plant disease diagnosis.

In this work, we utilize symmetric Fourier features to improve the accuracy of deep learning-augmented MOSFET current-voltage model. We first apply the Fourier analysis on the cost function and discuss the spectral bias of neural network. The benefits of augmenting the neural network with symmetric Fourier features, designed to satisfy the Gummel symmetry, are then discussed. Using TCAD-simulated FinFET as our dataset, our improved model, augmented with 8 Fourier features, achieves outstanding 3-sigma errors of 0.15% for IV characteristics, 4.14% for output conductance, and 0.33% for transconductance. This represents a cost reduction of 13 times compared to the scenario without the Fourier features, highlighting the benefits gained from their inclusion.

We study object detection and image classification models and observe that their respective architectures are vulnerable to image distortions such as noise, compression, blur or snow. We propose alleviating this problem by training the models with antibodies generated using Artificial Immune Systems (AIS) from original training samples (antigens). These antibodies are AIS-distorted antigens at the pixel level through cycles of “select, clone, mutate, select” until an affinity to the antigen is achieved. We then add the antibodies to the antigens, train the models, validate and test them under 15 distortions, and show that our data augmentation approach (AISbod) significantly improved their accuracy without altering their architecture or inference speed. For example, YOLOv4 improves by 3.90% on average over all 15 distortions, 4.06% under snow, and 28.11%  under impulse noise. Our simulations show that compared to related defence methods, our method performs better under distortions, is more consistent across datasets and object detection models, and also under clean samples. Moreover, we show that our approach to image classification significantly improves accuracy under distortions.

This paper explores the potential of biomimetics to revolutionize medical and food waste management systems in the United States. By forging circular economies in these fields, biomimetics can provide robust financial benefits. Furthermore, biomimetics can mitigate waste accumulation and related health hazards from such systems. In light of this paper's findings, ongoing and long-term financial investments in biomimetic technology are recommended to create sustainable medical and food waste systems on a nationwide scale.

Edge/Fog computing recently emerged as a novel distributed virtualized computing paradigm, where cloud services are extended to the edge of the network, thereby increasing network capacity and reducing latencies. In fog computing, applications are composed of {\it microservices} that are mapped to edge computing and communication devices ({\it fog nodes}). A crucial component in fog computing is placement algorithms that assign microservices to fog nodes since they determine the overall system performance in terms of energy consumption, communication costs, load balancing, and others. Placement strategies devised for cloud computing are generally centralized and, therefore, not well suited for decentralized fog systems. In this paper, we consider the joint optimization of two conflicting objectives, energy consumption at fog nodes and communication costs of applications, as a game between fog nodes and applications where each agent is modeled to control one objective. We follow a Markov approximation method for the design of a fully distributed autonomic service placement strategy without central coordination or global state information. Evaluation results show that the new approach provides a more optimal solution as compared to previous autonomic placement algorithms.

The massive deployment of low-end wireless Internet of things (IoT) devices opens the challenge of finding de-centralized and lightweight alternatives for secret key distribution. A possible solution, coming from the physical layer, is the secret key generation (SKG) from channel state information (CSI) during the channel’s coherence time. This work acknowledges the fact that the CSI consists of  deterministic (predictable) and stochastic (unpredictable) components, loosely captured through the terms large-scale and small-scale fading, espectively. Hence, keys must be generated using only the random and unpredictable part. To detrend CSI measurements from deterministic components, a simple and lightweight approach based on Kalman filters is proposed and is evaluated using an implementation of the complete SKG protocol (including privacy amplification that is typically missing in many published works). In our study we use a massive multiple input multiple output (mMIMO) orthogonal frequency division multiplexing outdoor measured CSI dataset. The threat model assumes a passive eavesdropper in the vicinity (at 1 meter distance or less) from one of the legitimate nodes and the Kalman filter is parameterized to maximize the achievable key rate.

Microfinance emerges as a pivotal catalyst within India's micro, medium, and small-scale industries. A notable exemplar is the SIDBI-PRAYAAS initiative, under the auspices of SIDBI, strategically engineered to empower the lower echelons of the financial pyramid. The latest study unveils a compelling triumph, spotlighting an impressive 6.02% Compound Annual Growth Rate (CAGR) surge in the commercial ventures of beneficiaries who availed themselves of this pioneering scheme. In this context, the infusion of digital mechanisms, including B-POS and B-Bank, has admirably streamlined the application, disbursement, and meticulous execution of loan reimbursements. The efficacy of these interventions reverberates triumphantly. Notably, the synergy of the Patanjali Ayurved Limited collaboration has further amplified the scheme's resonance, as eager borrowers harness its potential to nurture the expansion of Gramin Arogya Kendra. Considering these advancements, the current case study stands as a compelling testament to the efficacy of the PRAYAAS schemes, seamlessly interwoven with the digital dexterity of contemporary banking paradigms. This holistic approach underscores not only the scheme's operational prowess but also engenders profound customer gratification, thus forging a harmonious synthesis of financial inclusion and technological ingenuity.

In this manuscript, one of the higher educational institutions located at Lat. 13.62ON; Long. 79.29OE; Alt. 226 M is considered and the roof architectures and  orientations of seven sheds being utilized as six workshops (WS) and one central stores (CS) are studied. NASA-SSE satellite synthetic data is used for resource assessment and PV System 7.3 software is used to estimate the annual GHI at the concerned site. Physical inspection and measurement survey has been carried out to exactly obtain the shed orientation, azimuth angle and roof dimensions including slope angle. Based on these data, gross capacity of solar PV collectors that can be installed on the roof of sheds and corresponding energy yield is estimated. Different cases have been exercised to create real-time feasibility for erecting solar PV system on the roofs of the concerned sheds. The obtained results are so intensive that for the WS and CS sheds, the daily normalized production in kWh/kWp/Day and energy yield is more on the south facing roofs (CS & WS) followed by west (WS), east (WS) and north facing roofs (WS & CS). In comparison to the vertical installation, the specific production in kWh/kWp/Annum is more with the horizontal installation of PV modules on either side of the exposed roofs for WS and less for CS. The total maximum energy that can be generated on the roofs of total seven sheds is about 9,69,566 kWh/Annum which may reduce about 824.12 Tons of CO2 emissions per annum. It is an appreciable figure and could pave a path for establishing green electricity. This research has been carried out to inspire the enthusiastic higher educational institutions to think for installing considerable capacity of solar PV system on the roofs of their workshop sheds and contribute for establishing green electricity.  In this manuscript, one of the higher educational institutions located at Lat. 13.62ON; Long. 79.29OE; Alt. 226 M is considered and the roof architectures and  orientations of seven sheds being utilized as six workshops (WS) and one central stores (CS) are studied. NASA-SSE satellite synthetic data is used for resource assessment and PV System 7.3 software is used to estimate the annual GHI at the concerned site. Physical inspection and measurement survey has been carried out to exactly obtain the shed orientation, azimuth angle and roof dimensions including slope angle. Based on these data, gross capacity of solar PV collectors that can be installed on the roof of sheds and corresponding energy yield is estimated. Different cases have been exercised to create real-time feasibility for erecting solar PV system on the roofs of the concerned sheds. The obtained results are so intensive that for the WS and CS sheds, the daily normalized production in kWh/kWp/Day and energy yield is more on the south facing roofs (CS & WS) followed by west (WS), east (WS) and north facing roofs (WS & CS). In comparison to the vertical installation, the specific production in kWh/kWp/Annum is more with the horizontal installation of PV modules on either side of the exposed roofs for WS and less for CS. The total maximum energy that can be generated on the roofs of total seven sheds is about 9,69,566 kWh/Annum which may reduce about 824.12 Tons of CO2 emissions per annum. It is an appreciable figure and could pave a path for establishing green electricity. This research has been carried out to inspire the enthusiastic higher educational institutions to think for installing considerable capacity of solar PV system on the roofs of their workshop sheds and contribute for establishing green electricity.

Symbolic planning algorithms and large language models have different strengths and weaknesses for story generation, suggesting hybrid models might leverage advantages from both. Others have proposed using a language model in combination with a partial order planning style algorithm to avoid the need for a handwritten symbolic domain of actions. This paper offers a complementary approach. We use a state space planning algorithm to plan coherent multi-agent stories in symbolic domains, with a language model acting as a guide to estimate which events are worth exploring first. We evaluate an initial implementation of this method on a set of benchmark problems and find that the LLM's guidance is helpful to the planner in most domains.

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.

Fuzzy-rough sets (FRS) encapsulate the related but distinct concepts of vagueness (for fuzzy sets) and indiscernibility (for rough sets), both of which occur as a result of uncertainty in data, information or knowledge. The application of FRS in feature selection (FS) has employed the dependency degree to guide the FS process with much success. Whilst promising, most existing fuzzy-rough feature selection (FRFS) approaches are only conducted at the level of individual features, considering the inclusion/exclusion of individual features with regard to a candidate feature subset. In this case, the insight of meaningful information about certain inherent feature structure, such as the correlation between features or the collaborative contribution to a common decision may be ignored. To address this issue, an exclusive lasso assisted two-stage fuzzy-rough FS (EL-TSFRFS) method is presented in this paper. First, regarding discernibility, all features are divided into distinct groups using k-means clustering and the exclusive lasso regularization is utilised to select the representative features in each cluster, with such selected features sorted in descending order within the cluster. Second, a feature grouping-based FRFS algorithm is implemented to further determine the final discriminating feature subset. Comparative experimental results show that the reduct gained by the proposed approach generally outperforms those attained by alternative implementations of FRFS, in terms of both the size of the selected feature subset and the subsequent classification accuracy using the feature subset. Moreover, this is the first work to apply exclusive lasso to fuzzy-rough feature selection.

Cryptography has become an essential tool in information security, preserving data confidentiality, integrity, and availability. However, despite rigorous analysis, cryptographic algorithms may still be susceptible to attack when used on real-world devices. Side-channel attacks (SCAs) are physical attacks that target cryptographic equipment through quantifiable phenomena such as power consumption, operational times, and EM radiation. These attacks are considered to be a significant threat to cryptography since they compromise the integrity of the algorithm by obtaining the internal cryptographic key of a device by seeing its physical implementation. The literature on SCAs has focused on real-world devices, yet with the growing popularity of sophisticated devices like smartphones, fresh approaches to SCAs are necessary. One such approach is electromagnetic side-channel analysis (EM-SCA), which gathers information by listening to electromagnetic (EM) radiation. EM-SCA has been demonstrated to recover sensitive data like encryption keys and has the potential to identify malicious software, retrieve data, and identify program activity. This study aims to evaluate how well EM-SCA compromises encryption under various application scenarios, as well as examine the role of EM-SCA in digital forensics and law enforcement. Regarding this, addressing the susceptibility of encryption algorithms to EM-SCA approaches can provide digital forensic investigators with the tools they desire to overcome the challenges posed by strong encryption, allowing them to continue playing a crucial role in law enforcement and the justice system. Furthermore, this paper seeks to define the current state of EM-SCA in terms of attacking encryption, the encryption algorithms and encrypted devices that are most vulnerable and resistant to EM-SCA, and the most promising EM-SCA on encryption approaches. This study will provide a comprehensive analysis of EM-SCA in the context of law enforcement and digital forensics and point towards potential directions for further research.

Personalized AI-based services involve a population of individual reinforcement learning agents. However, most reinforcement learning algorithms focus on harnessing individual learning and fail to leverage the social learning capabilities commonly exhibited by humans and animals. Social learning integrates individual experience with observing others' behavior, presenting opportunities for improved learning outcomes. In this study, we focus on a social bandit learning scenario where a social agent observes other agents' actions without knowledge of their rewards. The agents independently pursue their own rewards without any explicit motivation for teaching one another. We propose a free energy-based social bandit learning algorithm over policy space, where the social agent evaluates others' expertise levels without resorting to any oracle or social norms. Accordingly, the social agent integrates its direct experiences in the environment and others' estimated policies. The theoretical convergence of our algorithm to the optimal policy is proven. Empirical evaluations validate the superiority of our social learning method over alternative approaches in diverse scenarios. Our algorithm strategically identifies the relevant agents, even in the presence of random or sub-optimal agents, and skillfully exploiters their behavioral information. Extra to societies including expert agents, in the presence of relevant but non-expert agents, our algorithm significantly enhances individual learning performance, where most related methods fail. Importantly, it maintains logarithmic regret.

The birth of the 5G telecommunication network laid a good foundation for driving the establishment of massive interconnectivity among IoT devices, which also coincided with the beginning of the Fourth Industrial Revolution (4IR). Such phenomena accelerated the creation of different innovative scenarios and deepen self-monitoring through smart algorithm analyses, as well as created high volume, high velocity, and high variety of data, further spurring relevant parties' interest in monetizing the data, especially the Telcos, where they see this as a new form of revenue through big data analytics. Through a systematic literature review following the PRISMA framework, this research represents one pioneer attempt to find out what impact Telco's data monetization of IoT systems has on data privacy regulation. It covers identifying potential data privacy regulatory challenges and potential solutions in the Telco IoTbased data monetization scenario through PESTLE analysis. Among the key findings indicated the challenges arising from the speed of various innovations in such a scenario outpacing the maturity of laws and regulations in regulating them. Further impacts, such as consistent re-evaluation of the data privacy boundary from data owners' perspective, are compulsory from time to time, and the Privacy-by-Design concept becomes a critical evaluation factor (in both business and technologies) across all applicable laws and regulations. Furthermore, the outcome of this research also pointed out the future directions for more in-depth applied data analytics studies of the potential emergence of new Telco IoT-based data monetization data privacy regulatory risks as the relevant technology innovations progress.

Anomaly detection plays a crucial role in various domains, including but not limited to cybersecurity, space science, finance, and healthcare. However, the lack of standardized benchmark datasets hinders the comparative evaluation of anomaly detection algorithms. In this work, we address this gap by presenting a curated collection of preprocessed datasets for spacecraft anomalies sourced from multiple sources. These datasets cover a diverse range of anomalies and real-world scenarios for the spacecrafts. Furthermore, we have added two general datsets ensuring comprehensive evaluation and generalizability of anomaly detection algorithms. Our compilation process involves rigorous preprocessing steps to ensure data integrity and privacy protection. Each dataset is thoroughly documented, including descriptions of anomalies, preprocessing methodologies, and evaluation metrics. By providing this unified benchmark dataset, we aim to facilitate fair and transparent evaluation of anomaly detection algorithms, ultimately advancing the state-of-the-art in anomaly detection research.

Video data and algorithms have been driving advances in multi-object tracking (MOT). While existing MOT datasets focus on occlusion and appearance similarity, complex motion patterns are widespread yet overlooked. To address this issue, we introduce a new dataset called BEE23 to highlight complex motions. Identity association algorithms have long been the focus of MOT research. Existing trackers can be categorized into two association paradigms: single-feature paradigm (based on either motion or appearance feature) and serial paradigm (one feature serves as secondary while the other is primary). However, these paradigms are incapable of fully utilizing different features. In this paper, we propose a parallel paradigm and present the Two rOund Parallel matchIng meChanism (TOPIC) to implement it. The TOPIC leverages both motion and appearance features and can adaptively select the preferable one as the assignment metric based on motion level. Moreover, we provide an Attention-based Appearance Reconstruct Module (AARM) to reconstruct appearance feature embeddings, thus enhancing the representation of appearance features. Comprehensive experiments show that our approach achieves state-of-the-art performance on four public datasets and BEE23. Notably, our proposed parallel paradigm surpasses the performance of existing association paradigms by a large margin, e.g., reducing false negatives by 12% to 51% compared to the single-feature association paradigm. The introduced dataset and association paradigm in this work offer a fresh perspective for advancing the MOT field. The source code and dataset are available at https://github.com/holmescao/TOPICTrack.

Due to the presence of the roadâ\euro™s curvature and sensorsâ\euro™ limited field of view, as-built highway curves designed based on traditional human-driven vehiclesâ\euro™ characteristics pose a challenge to the adaptive cruise control (ACC) system and its shared control. However, very few efforts in the literature were expended on exploring the adaptability of the ACC system-dedicated vehicle (V-ACC) from the perspective of vehicle-road geometry interaction. Therefore, the objectives of this study are threefold: (i) to investigate the implications of existing horizontal curves on V-ACC dynamic and kinematic characteristics; (ii) to unravel the impact mechanism of curve geometric features; and (iii) to evaluate the ACC systemâ\euro™s adaptability from different aspects and extract the critical curve geometric features. To this end, a virtual co-simulation platform was established and validated by the OpenACC database. A series of tests featuring circular curve radius (RC), desired speed (Vde), and desired clearance were created, and V-ACC characteristics were output. The results show that: (i) a smaller RC causes V-ACC characteristics toward the margins of safety, comfort, and speed consistency, but neither sideslip nor rollover occurs, and speed consistency is good; (ii) the driver and passengers (if any) feel comfortable at Vde = 40, 80-100 km/h following the leading car, but they may feel â\euro˜moderately uncomfortableâ\euro™ at Vde = 50-70 km/h when RC decreases toward its lower limit; and (iii) asymmetrical maneuvers and discomfort would exist before and after the circular curve. These findings could help road administrators regulate V-ACCâ\euro™s behaviors and improve its road-oriented operation design domain.