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2026 EN
Sushanta Meher · Bharat Jyoti Ranjan Sahu · Bijayini Mohanty
+1 more
The exponential growth of mobile connectivity demands adaptive and energy-aware communication frameworks capable of sustaining Quality of Service (QoS) in dynamic 5G environments. Recent reports from the International Telecommunication Union (ITU) and Ericsson Mobility (2024) indicate that global data traffic has risen by over 60%. Such trends intensify paging activity and energy consumption in radio access networks. To address these challenges, this study introduces the Dynamic Traffic Demand Prediction (DTDP) framework, which integrates linear ( ARIMA ) and non-linear ( XGBoost ) forecasting models with an adaptive paging mechanism. DTDP predicts user traffic demand and dynamically adjust paging intervals to minimize signaling overhead and power usage. Experimental analysis confirms that DTDP enhances the energy efficiency up to 68.4% while maintaining acceptable QoS in terms of latency, packet delivery ratio, and paging success rate.
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2026 EN
Satyam Mittal · Ishita Malhotra
Course Recommendation Systems (CRS) help learners choose courses aligned with goals, yet face cold-start, limited interpretability, prerequisite feasibility, and fairness challenges. This review synthesizes peer-reviewed course recommendation research from 2022–2025 and reports improvements strictly within each study (baseline→best) to avoid invalid cross-paper pooling across datasets and protocols. The surveyed methods span session/set recommendation, temporal and knowledge graphs (KGs) augmented sequence models, and fairness-aware multi-graph learning, alongside CRS-adjacent large language models (LLMs)-based retrieval and re-ranking patterns discussed in recent education recommendation pipelines. We further refine the taxonomy with peer-reviewed evidence on prerequisite-aware recommendation, data-quality-aware KGs, and interpretable CRS grounded in catalog text or graph paths. Lastly, we summarize reported deployment considerations (retrieval→re-ranking, indexing, summarization, quantization) and identify open challenges in offline–online alignment, feasibility/data-quality reporting, and privacy-preserving, auditable deployments.
Resource
2026 EN
Ishita Sharma · Satyam Agarwal · Shashi Shekhar Jha
+1 more
Attacks on network components and devices pose a significant threat to service continuity, necessitating robust detection mechanisms. This paper presents a Distributed Denial of Service (DDoS) attack detection framework tailored for heterogeneous mobile wireless networks within a Software-Defined Networking architecture. A two-tier model is proposed: localized attack detection at access points (APs) using a Multi-Layer Perceptron (MLP) classifier, and centralized detection under mobility at the controller using a Long Short-Term Memory (LSTM) model. The system incorporates novel traffic features such as flow count, speed of source IP, source and destination IP address entropy, proportion of bidirectional flows, and handover frequency, which together enhance detection in mobile environments. An LSTM model analyzes inter-AP traffic correlation over time to address mobility-driven DDoS attack amplification. The proposed approach is evaluated under diverse traffic types (TCP, UDP, ICMP) and varying attack intensities. The MLP model selected for integration into the framework demonstrates consistently strong detection capability across the evaluated scenarios, achieving accuracy values in the range of 95%–99% and showing improved performance relative to existing state-of-the-art schemes. Furthermore, multi-run statistical validation confirms stable behavior under randomized initialization and mobility-driven conditions, while controller-level correlation analysis enhances robustness against mobility-driven attack propagation.
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2026 EN
MArco Barozzi · Praveen Sahu · Elisabetta Sieni
+5 more
Electroporation of biological tissues induced by high-intensity, short-duration voltage pulses modify tissue permeability to molecules and ions allowing applications like drug delivery and gene therapy. Voltage pulses generate an electric field inside the tissue that increases its electrical conductivity. The value of the tissue conductivity is used to assess the efficacy of the adopted experimental electroporation protocol that depends on voltage amplitude, pulse duration and frequency and number of pulses. Different protocols may lead to huge variation of conductivity. However, protocols are often compared without taking into account uncertainties associated to the measurement system. This is addressed using potato tissue as a typical biological model to assess the efficacy of the pulse characteristics and application protocol. In this research, the variation of potato tissue conductivity is examined along with comprehensive uncertainty analyses to address reliability and evidence differences in adopted electroporation protocols. The uncertainty assessment accounts for all elements in the measurement chain, including signal generation, environmental factors, and data acquisition systems. Using statistical analysis based on a Welch's test (based on Student’s test) the electroporation onset was discerned. The introduced uncertainty framework, could lead to a robust evaluation of the estimated conductivity, and support the scientific findings with respect to the comparison of the efficacy of different protocols and it can be used to optimize the design of electroporation-based applications in medical and biotechnological fields.
Resource
2026 EN
Sharief Saleh · Satyam Dwivedi · Russ Whiton
+9 more
The rapid advancement of connected and autonomous vehicles has driven a growing demand for precise and reliable positioning systems capable of operating in complex environments. Meeting these demands requires an integrated approach that combines multiple positioning technologies, including wireless-based systems, perception-based technologies, and motion-based sensors. This paper presents a comprehensive survey of wireless-based positioning for vehicular applications, with a focus on satellite-based positioning (such as global navigation satellite systems (GNSS) and low-Earth-orbit (LEO) satellites), cellular-based positioning (5G and beyond), and IEEE-based technologies (including Wi-Fi, ultrawideband (UWB), Bluetooth, and vehicle-to-vehicle (V2V) communications). First, the survey reviews a wide range of vehicular positioning use cases, outlining their specific performance requirements. Next, it explores the historical development, standardization, and evolution of each wireless positioning technology, providing an in-depth categorization of existing positioning solutions and algorithms, and identifying open challenges and contemporary trends. Finally, the paper examines sensor fusion techniques that integrate these wireless systems with onboard perception and motion sensors to enhance positioning accuracy and resilience in real-world conditions. This survey thus offers a holistic perspective on the historical foundations, current advancements, and future directions of wireless-based positioning for vehicular applications, addressing a critical gap in the literature.
Resource
2026 EN
Jyothisri Kodela · Hemanta Kumar Sahu
This paper investigates the performance of coherent-state quantum optical wireless communication systems operating over atmospheric turbulence channels with pointing errors (PEs). The average error probability (AEP) and average achievable rate (AAR) are analyzed for on–off keying (OOK) and binary phase-shift keying (BPSK) signaling under photon-counting (PC), homodyne, and optimal Helstrom detection. The proposed framework jointly incorporates deterministic path loss, atmospheric turbulence, and PEs, and accommodates multiple statistical channel models, including Gamma-Gamma (GG), doubly inverted Gamma-Gamma (IGGG), and Fisher-Snedecor F distributions, within a unified analytical formulation. Closed-form expressions for AEP and AAR are derived via direct probability density function (PDF)-based integration of the conditional error probability, along with high-photon-number asymptotic approximations that provide insight into performance scaling behavior. Numerical results validate the analytical expressions and demonstrate the impact of PEs on system performance. The analysis further enables systematic comparison between classical and optimal quantum detection schemes and quantifies the achievable performance gains under different channel conditions. These results provide useful design insights for modulation and receiver selection in turbulent free-space optical (FSO) communication systems.
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2026 EN
Arhum Ahmad · Krishna Kumar · Satyam Agarwal
+1 more
We present WISDOM, a hardware-aware, blind wideband spectrum-sensing framework designed to bridge the gap between theoretical detection algorithms and the reality of low-cost radio front-ends. Unlike conventional detectors that suffer from the SNR wall, WISDOM operates entirely in the digital domain using a multi-stage statistical pipeline. The framework introduces three core novelties: (i) a Noise-Avoidance Criterion (η n ) derived via Ordered-Statistic Constant False Alarm Rate on a guaranteed noise-only spectral region manufactured by the pre-processing chain; (ii) an inverted “Count-and-Slope” decision rule that identifies signals by detecting structured local spectral deviations rather than absolute power thresholds; and (iii) an analytically scalable Selective Spectral Refinement. Extensive validation on Software Defined Radio testbeds demonstrates sensitivity of -85 dBm across a 50 MHz sensing bandwidth and acceptable performance against hardware impairments.
Journals
2026 EN
Lescinsky Haley · Sahu Maitreyi · Beauchamp Meera
+9 more
ABSTRACT Objective To examine trends in state‐level health care value over three decades, defined using statewide health care spending and cause‐specific mortality, and to explore its associations with potentially modifiable state attributes. Study Setting and Design We use stochastic frontier analysis to identify the “inefficiency” of each state's delivery system in converting health care spending into lower mortality–incidence or mortality–prevalence rates, adjusting for underlying population risk (age, smoking, obesity, etc.). We combine these inefficiency scores to score and compare delivery system value for each state and track change over three decades. Then, we use linear regression to look across states and identify state‐level attributes significantly associated with greater health care value. Data Sources and Analytic Sample For each US state and year from 1991 to 2020, we extracted mortality–incidence or mortality–prevalence rates for 67 high‐mortality health conditions from the Global Burden of Disease 2021 Study and state health care spending from the State Health Expenditure Accounts. Principal Findings Across US states, value on average increased from 1991 to 2000, remained relatively constant from 2001 to 2010, and then declined from 2011 to 2020 by 16.7% (95% uncertainty interval [UI]: 14.7–20.1) or 13.6 (95% UI: 11.3–15.9) value points. The percentage of state populations with insurance was positively associated with health delivery system value. In contrast, market consolidation among hospitals and among health insurers of small and large groups, and increased for‐profit hospital ownership were each associated with a lower health care value. The net effect of these associations was a reduction in the national value score for the decade ending in 2020. Conclusions In contrast to the prior two decades, health care delivery system value scores declined over the last decade. This decline was associated with reduced competition among hospitals and health insurers, increased for‐profit hospital ownership, and was partly mitigated by wider insurance coverage.
Journals
2026 EN
Pradhan Nupur · Sahu Saumya · Dey Pranab
ABSTRACT Aims and Objectives This study applied an ensemble learning model combining six transfer learning architectures to detect malignancy in effusion cytology. Materials and Methods In this current study, we had a total of 110 cases of effusion cytology consisting of 59 benign and 51 malignant cases. We took a total of 755 representative microphotographs from the Papanicolaou's stained smear. The ensemble learning model consists of DenseNet121, Xception, ResNet50, MobileNetV2, InceptionV3, and VGG16 with a soft voting technique. After initial feature extraction, fine‐tuning was performed by unfreezing the final layers of each backbone. The neural network was implemented in Jupyter Notebook. Result The model achieved sensitivity, specificity, accuracy, precision, negative predictive value, F1 score, and AUROC of 0.92, 0.89, 0.90, 0.89, 0.92, 0.91, and 0.96, respectively. Conclusions To our knowledge, this is the first study applying a six‐model ensemble deep learning approach in effusion cytology. The combined transfer learning framework demonstrated excellent diagnostic performance and may serve as a future tool for carcinoma detection in effusion cytology.
Journals
2026 EN
Sarkar Ritwik · Gochhayat Amrit Kumar · Kumar Satyam
Abstract Four different binders, namely, calcium aluminate cement, silica sol, hydratable alumina (HA), and mono‐aluminum phosphate, are used separately in a high alumina castable composition to compare their effects on the properties development. The castables were prepared using commercial‐grade materials using a distribution coefficient of 0.23, as per the Dinger and Funk model. Cement‐bonded castable was added with additives, like flow modifier (fume silica), deflocculant, and anti‐setting agent, and HA‐bonded castable was added with deflocculant. All the castables with different bonding systems were processed as per conventional manufacturing technique and heat treated at three different temperatures. The castables showed corundum as the major phase post‐firing with a minor anorthite phase for a cement‐bonded one and a mullite phase for the silica sol–bonded one. Density and cold crushing strength were found to be higher for the cement‐bonded one, whereas the hot strength was higher for the HA‐bonded one, and thermal shock resistance was higher for the silica sol–bonded castable.