Search Results (276)

In this study, the neutron and gamma radiation-shielding properties of serpentinites from the Guleman ophiolite complex were investigated, and results were evaluated in comparison with rare earth element (REE) content. The linear and mass attenuation coefficients (LAC and MAC), half-value layer (HVL), mean free path (MFP), and effective atomic numbers (Z_eff) of serpentinite samples were experimentally measured in the energy range of 80.99–383.85 keV. Theoretical MAC values were calculated. Additionally, fast neutron removal cross-sections, as well as thermal and fast neutron macroscopic cross-sections, were theoretically determined. The absorbed equivalent dose rates of serpentinite samples were also measured. The radiation protection efficiency (RPE) for gamma rays and neutrons were determined. It was observed that the presence of rare earth elements within serpentinite structure has a significant impact on thermal neutron cross-sections, while crystalline water content (LOI) plays an influential role in fast neutron cross-sections. Moreover, it has been observed that the concentration of gadolinium exerts a more substantial influence on the macroscopic cross-sections of thermal neutrons than on those of fast neutrons. The research results reveal the mineralogical, geochemical, morphological and radiation-shielding properties of serpentinite rocks contribute significantly to new visions for the use of this naturally occurring rock as a geological repository for nuclear waste or as a wall-covering material in radiotherapy centers and nuclear facilities instead of concrete. Full article

This study presents a hybrid computational investigation into the radiation shielding behavior of Fe-enriched Al-based alloys (Al-Fe-Mo-Si-Zr) for potential use in nuclear applications. Four alloy compositions with varying Fe contents (7.21, 6.35, 5.47, and 4.58 wt%) were analyzed using a combination of Monte Carlo simulations, machine learning (ML) predictions based on multilayer perceptrons (MLPs), EpiXS, and SRIM-based charged particle transport modeling. Key photon interaction parameters—including mass attenuation coefficient (MAC), half-value layer (HVL), buildup factors, and effective atomic number (Z_eff)—were calculated across a wide energy range (0.015–15 MeV). Results showed that the 7.21Fe alloy exhibited a maximum MAC of 12 cm²/g at low energies and an HVL of 0.19 cm at 0.02 MeV, indicating improved gamma attenuation with increasing Fe content. The ML model accurately predicted MAC values in agreement with Monte Carlo and XCOM data, validating the applicability of AI-assisted modeling in material evaluation. SRIM calculations demonstrated enhanced charged particle shielding: the projected range of 10 MeV protons decreased from ~55 µm (low Fe) to ~50 µm (high Fe), while alpha particle penetration reduced accordingly. In terms of fast neutron attenuation, the 7.21Fe alloy reached a maximum removal cross-section (Σ_R) of 0.08164 cm⁻¹, showing performance comparable to conventional materials like concrete. Overall, the results confirm that Fe-rich Al-based alloys offer a desirable balance of lightweight design, structural stability, and dual-function radiation shielding, making them strong candidates for next-generation protective systems in high-radiation environments. Full article

An outstanding adsorbent, such as the metal–organic framework (MOF) MIL-101 (Cr), was employed to study the adsorption of two dyes, namely reactive red 2 (RR2) and reactive blue 19 (RB19). Experimental adsorption data were retrieved at T = 25, 35 and 45 °C and analyzed to define the adsorption mechanism of these dyes. A modeling approach based on a double-layer model derived from statistical physics was used. The maximum adsorption capacity (MAC) was found to be 875, 954 and 1002 mg/g for RR2 and 971, 1093 and 1148 mg/g for RB19, at T = 25, 35 and 45 °C, respectively. These values indicate that MIL-101 (Cr) exhibits outstanding performance in removing potential water pollutants such as the RR2 and RB19 dyes. The possible orientations of the RR2 and RB19 dyes upon adsorption were determined by analyzing the number of dye molecules bound per MIL-101 (Cr) active sites during the adsorption process. It was found that the RR2 dye was removed via a mixed parallel and non-parallel orientation on MIL-101 (Cr), while RB19 was removed via an inclined orientation at higher temperatures. The adsorption mechanism suggested that MIL-101 (Cr) site density was reduced due to an exothermic effect, which decreases the number of active sites participating in dye adsorption, even though the reduction in water adsorption may be attributed to the overall endothermic behavior. From the adsorption energy (AE) and the chemical structure of MIL-101 (Cr) and both dyes, it was concluded that hydrogen bonds, Van der Waals forces and π-π stacking are involved in the dye removal process. This research provides new physical insights into the adsorption mechanism of two relevant dyes on an outstanding adsorbent such as the MIL-101 (Cr) MOF. Full article

This study employs simulation tools to design and evaluate lightweight, lead-free polymer composites incorporating polytetrafluoroethylene (PTFE), polyethylene (PE), and polyetherimide (PEI) for gamma radiation shielding in nuclear medicine. Targeting clinically relevant photon energies from Tc-99m (140 keV), I-131 (364 keV), and Cs-137 (662 keV), composites’ structural and shielding performance with Bi₂O₃ and WO₃ was assessed using XCOM and Phy-X/PSD. PEI emerged as the most suitable polymer for load-bearing and thermally exposed applications, offering superior mechanical stability and dimensional integrity. Bi₂O₃-WO₃ fillers for Tc-99m achieved a ~7000-fold increase in MAC, I-131 ~2063-fold, and Cs-137 ~1370-fold compared to PbO₂. The PEI-75Bi₂O₃-25WO₃ achieved a ~21-fold reduction in half-value layer (HVL) compared to lead for Tc-99m. For higher-energy isotopes of I-131 and Cs-137, HVL reductions of ~0.44-fold and ~0.08-fold, respectively, were achieved. The results demonstrate that high-Z dual filler polymer composites have an equal or enhanced attenuation properties to lead-based shielding, whilst also enhancing the polymer composites’ mechanical and thermal characteristics. As the use of ionizing radiation increases, so does the potential risks; high-Z dual filler polymer composites provide a sustainable, lightweight, non-toxic alternative to conventional lead shielding. Full article

The effects of plant configuration modes on soil organic carbon fractions are mainly reflected in plant species, root structure, apoplastic input, and microbial activity, and different plant configuration modes affect the accumulation and stability of soil organic carbon by changing the input and decomposition processes of organic matter. Considering the common use of local species in ecological restoration and their diverse ecological functions, we selected five different plant configuration modes in the lakeshore zone of Hongze Lake (Metasequoia glyptostroboides-Amorpha fruticosa L. (M-Af), Metasequoia glyptostroboides-Acorus calamus L. (M-Ac), Salix babylonica L.-Amorpha fruticosa L. (S-Af), Magnolia grandiflora L.-Nandina domestica Thunb. (Mg-N), and Pterocarya stenoptera C. DC.-Nandina domestica Thunb. (P-N)) in this study. The objective of the present study was to analyze the carbon content in the vegetation, the content of soil organic carbon and its components in the understorey, and the activity of the soil carbon pool and their interrelationships under different plant configuration modes in the lakeshore zone of Hongze Lake to reveal the dynamic change law in the carbon pool under different plant configuration modes. The findings demonstrated that within the Metasequoia glyptostroboides mode, M-Ac exhibited notable benefits in accumulating soil organic carbon and enhancing the stability of carbon fractions. The soil organic carbon (SOC) content was recorded at 3.93 g·kg⁻¹, the total carbon (TC) content at 4.73 g·kg⁻¹, and the mineral-associated organic carbon (MAOC) content of 2.20 g·kg⁻¹ in the soil layer of 0–20 cm, which were 23.4%–71.6%, 9%–24.5%, and 18.9%–54.3% (p < 0.05), respectively, and were higher than the other configuration modes. Regarding the percentage of inactive carbon (NLC/SOC), the corresponding values for M-Ac and M-Af were 74.21% and 70.33%, respectively, which were significantly higher than the other modes. Redundancy analysis further showed that the soil whole carbon and arbor layer branch carbon content were the pivotal factors driving the accumulation of soil organic carbon fractions (with a cumulative explanation of 71.26%). This study has the potential to provide a theoretical basis and practical reference for optimizing plant allocation and enhancing the carbon sink function in the ecological restoration of the lakeshore zone. Full article

The optical and radiation shielding of PVA have been enhanced through embedding with ZnO/CuO/SWCNT (ZCS) nanocomposites. ZCS polymeric nanocomposites (PNCs) were prepared with the solution casting method. Scanning electron, optical microscopy and FT-IR procedures were performed to examine the surfaces’ morphology and structures’ modifications. UV–visible measurements were carried out to investigate the linear/nonlinear optical properties. The optical investigations show significant alterations in the optical parameters of PVA due to ZCS embedding. The UV–visible analysis shows that the optical parameters, including the transmittance, energy bandgap, refractive index, dielectric constants and optical conductivity of PVA, are tuned through ZCS embedding. The direct and indirect bandgap of PVA shrank from 5.42 eV and 4.99 eV (neat PVA) to 3.20 eV and 2.26 eV (10 wt.% ZCS PNCs). The nonlinear optical (NLO) constants (first order susceptibility (χ⁽¹⁾), third susceptibility (χ⁽³⁾) and refractive index (n₂)) of PVA were improved. Phy-X/PSD software was used to investigate the radiation shielding parameters of all samples. The linear attenuation coefficient (LAC), mean free path (MFP), half value layer (HVL), tenth value layer (TVL) and effective atomic number (Z_eff) of PVA were enhanced through ZCS embedding. It is found that the mass attenuation coefficient (MAC) of the neat PVA increased from 1.14 cm²/g to 7.96 cm²/g at 0.015 MeV. The HVL of PVA decreased from 30.2 cm to 20.6 cm, the TVL decreased from 100.3 cm to 68.5 cm and the MFP decreased from 43.6 cm to 29.8 cm upon embedding 10 wt.% of ZCS NCs at 15 MeV. The samples’ exposure buildup factor (EBF) and energy absorption buildup factor (EABF) in the photon energy range from 0.015 MeV to 15 MeV at 0.5 to 40 MFP values. This study proves that ZCS PNCs are advantageous for applications in optical and radiation shielding fields. Full article

The smart-pixel-based optical convolutional neural network was proposed to improve kernel refresh rates in scalable optical convolutional neural networks (CNNs) by replacing the spatial light modulator with a smart pixel light modulator while preserving benefits such as an unlimited input node size, cascadability, and direct kernel representation. The smart pixel light modulator enhances weight update speed, enabling rapid reconfigurability. Its fast updating capability and memory expand the application scope of scalable optical CNNs, supporting operations like convolution with multiple kernel sets and difference mode. Simplifications using electrical fan-out reduce hardware complexity and costs. An evolution of this system, the smart-pixel-based bidirectional optical CNN, employs a bidirectional architecture and single lens-array optics, achieving a computational throughput of 8.3 × 10¹⁴ MAC/s with a smart pixel light modulator resolution of 3840 × 2160. Further advancements led to the two-mirror-like smart-pixel-based bidirectional optical CNN, which emulates 2n layers using only two physical layers, significantly reducing hardware requirements despite increased time delay. This architecture was demonstrated for solving partial differential equations by leveraging local interactions as a sequence of convolutions. These advancements position smart-pixel-based optical CNNs and their derivatives as promising solutions for future CNN applications. Full article

Vector–matrix multiplication (VMM), which dominates the computational workload in neural networks, accounts for over 99% of all operations, particularly in Convolutional Neural Networks (CNNs). These operations, consisting of multiply-and-accumulate (MAC) functions, are straightforward but demand massive parallelism, often involving billions of operations per layer. This computational demand negatively affects processing time, energy consumption, and memory bandwidth due to frequent external memory access. To efficiently address these challenges, this paper investigates the implementation of a full neural network for image classification, using TensorFlow as a software baseline, and compares it with a hardware counterpart mapped onto resistive RAM-based crossbar arrays, a practical implementation of the memristor concept. By leveraging the inherent ability of RRAM crossbars to perform VMMs in a single step, we demonstrate how RRAM-based neural networks can achieve efficient in-memory analog computing. To ensure realistic and practical results, the hardware implemented utilizes RRAM memory cells characterized through silicon measurements. Furthermore, the design exclusively considers positive weights and biases to minimize the area overhead, resulting in a lightweight hardware solution. This approach achieves an energy consumption of 190 fJ/MAC operation for the crossbar array, highlighting its efficiency in power-constrained applications despite a drop in the prediction confidence of 27.5% compared to the software approach. Full article

Research into, and the usage of, Low-Power Wide-Area Networks (LPWANs) has increased significantly to support the ever-expanding requirements set by IoT applications. Specifically, the usage of Long-Range Wide-Area Networks (LoRaWANs) has increased, due to the LPWAN’s robust physical layer, Long-Range (LoRa), modulation scheme, which enables scalable, low-power consumption, long-range communication to IoT devices. The LoRaWAN Medium Access Control (MAC) protocol is currently limited to only support single-hop communication. This limits the coverage of a single gateway and increases the power consumption of devices which are located at the edge of a gateway’s coverage range. There is currently no standardised and commercialised multi-hop LoRa-based network, and the field is experiencing ongoing research. In this work, we propose a complementary network to LoRaWAN, which integrates mesh networking. An ns-3 simulation model has been developed, and the proposed LoRaMesh network is simulated for a varying number of scenarios. This research focuses on the design decisions needed to design a LoRa-based mesh network which maintains the low-power consumption advantages that LoRaWAN offers while ensuring that data packets are routed successfully to the gateway. The results highlighted a significant increase in the packet delivery ratio in nodes located far from a centralised gateway in a dense network. Nodes located further than 5.8 km from a gateway’s packet delivery ratio were increased from an average of 40.2% to 73.78%. The findings in this article validate the concept of a mesh-type LPWAN network based on the LoRa physical layer and highlight the potential for future optimisation. Full article

Field Programmable Gate Arrays (FPGAs), with their wide portfolio of configurable resources such as Look-Up Tables (LUTs), Block Random Access Memory (BRAM), and Digital Signal Processing (DSP) blocks, are the best option for custom hardware designs. Their low power consumption and cost-effectiveness give them an advantage over Graphics Processing Units (GPUs) and Central Processing Units (CPUs) in providing efficient accelerator solutions for compute-intensive Convolutional Neural Network (CNN) models. CNN accelerators are dedicated hardware modules capable of performing compute operations such as convolution, activation, normalization, and pooling with minimal intervention from a host. Designing accelerators for deeper CNN models requires FPGAs with vast resources, which impact its advantages in terms of power and price. In this paper, we propose the VCONV Intellectual Property (IP), an efficient and scalable CNN accelerator architecture for applications where power and cost are constraints. VCONV, with its configurable design, can be deployed across multiple smaller FPGAs instead of a single large FPGA to provide better control over cost and parallel processing. VCONV can be deployed across heterogeneous FPGAs, depending on the performance requirements of each layer. The IP’s performance can be evaluated using embedded monitors to ensure that the accelerator is configured to achieve the best performance. VCONV can be configured for data type format, convolution engine (CE) and convolution unit (CU) configurations, as well as the sequence of operations based on the CNN model and layer. VCONV can be interfaced through the Advanced Peripheral Bus (APB) for configuration and the Advanced eXtensible Interface (AXI) stream for data transfers. The IP was implemented and validated on the Avnet Zedboard and tested on the first layer of AlexNet, VGG16, and ResNet18 with multiple CE configurations, demonstrating 100% performance from MAC units with no idle time. We also synthesized multiple VCONV instances required for AlexNet, achieving the lowest BRAM utilization of just 1.64 Mb and deriving a performance of 56GOPs. Full article

We explore the integration of smart pixel light modulators (SPLMs) into bidirectional optical neural networks (BONNs), highlighting their advantages over traditional spatial light modulators (SLMs). SPLMs enhance BONN performance by enabling faster light modulation in both directions, significantly increasing the refresh rate of neural network weights to hundreds of megahertz, thus facilitating the practical implementation of the backpropagation algorithm and two-mirror-like BONN structures. The architecture of an SPLM-based BONN (SPBONN) features bidirectional modulation, simplifying hardware with electrical fan-in and fan-out. An SPBONN with an array size of 96 × 96 can achieve high throughput, up to 4.3 × 10¹⁶ MAC/s with 10 layers. Energy assessments showed that the SPLM array, despite its higher power consumption compared to the SLM array, is manageable via effective heat dissipation. Smart pixels with programmable memory in the SPBONN provide a cost-effective solution for expanding network node size and overcoming scalability limitations without the need for additional hardware. Full article

LoRa modulation is a widely used technology known for its long-range transmission capabilities, making it ideal for applications with low data rate requirements, such as IoT-enabled sensor networks. However, its inherent low data rate poses a challenge for applications that require higher throughput, such as video surveillance and disaster monitoring, where large image files must be transmitted over long distances in areas with limited communication infrastructure. In this paper, we introduce the LoRa Resource Allocation (LRA) algorithm, designed to address these limitations by enabling parallel transmissions, thereby reducing the total transmission time (T_tx) and increasing the bit rate (BR). The LRA algorithm leverages the quasi-orthogonality of LoRa’s Spreading Factors (SFs) and employs specially designed end devices equipped with dual LoRa transceivers, each operating on a distinct SF. For experimental analysis we choose an image transmission application and investigate various parameter combinations affecting T_tx to optimize interference, BR, and image quality. Experimental results show that our proposed algorithm reduces T_tx by 42.36% and 19.98% for SF combinations of seven and eight, and eight and nine, respectively. In terms of BR, we observe improvements of 73.5% and 24.97% for these same combinations. Furthermore, BER analysis confirms that the LRA algorithm delivers high-quality images at SNR levels above −5 dB in line-of-sight communication scenarios. Full article

IEEE 802.11 is one of the most common medium access control (MAC) protocols used in wireless networks. The carrier sense multiple access with collision avoidance (CSMA/CA) mechanisms in 802.11 have been designed under the assumption that all nodes in the network are cooperative and trustworthy. However, the potential for non-cooperative nodes exists, nodes that may purposefully misbehave in order to, for example, obtain extra bandwidth, conserve their resources, or disrupt network performance. This issue is further compounded when receivers such as Wi-Fi hotspots, normally trusted by other module nodes, also misbehave. Such issues, their detection, and mitigation have, we believe, not been sufficiently addressed in the literature. This research proposes a novel trust-incorporated MAC protocol (TMAC) which detects and mitigates complex node misbehavior for distributed network environments. TMAC introduces three main features into the original IEEE 802.11 protocol. First, each node assesses a trust level for their neighbors, establishing a verifiable backoff value generation mechanism with an incorporated trust model involving senders, receivers, and common neighbors. Second, TMAC uses a collaborative penalty scheme to penalize nodes that deviate from the IEEE 802.11 protocol. This feature removes the assumption of a trusted receiver. Third, a TMAC diagnosis mechanism is carried out for each distributed node periodically, to reassess neighbor status and to reclassify each based on their trust value. Simulation results in ns2 showed that TMAC is effective in diagnosing and starving selfish or misbehaving nodes in distributed wireless networks, improving the performance of trustworthy well-behaving nodes. The significant feature of TMAC is its ability to detect sender, receiver, and colluding node misbehavior at the MAC layer with a high level of accuracy, without the need to trust any of the communicating parties. Full article

Recent advances in signal processing and AI-based inference enable the exploitation of wireless communication signals to collect information on devices, people, actions, and the environment in general, i.e., to perform Integrated Sensing And Communication (ISAC). This possibility offers exciting opportunities for Internet of Things (IoT) systems, but it also introduces unprecedented threats to the security and privacy of data, devices, and systems. In fact, ISAC operates in the wireless PHY and Medium Access Control (MAC) layers, where it is impossible to protect information with standard encryption techniques or with any other purely digital methodologies. The goals of this paper are threefold. First, it analyzes the threats to security and privacy posed by ISAC and how they intertwine in the wireless PHY layer within the framework of IoT and distributed pervasive communication systems in general. Secondly, it presents and discusses possible countermeasures to protect users’ security and privacy. Thirdly, it introduces an architectural proposal, discussing the available choices and tradeoffs to implement such countermeasures, as well as solutions and protocols to preserve the potential benefits of ISAC while ensuring data protection and users’ privacy. The outcome and contribution of the paper is a systematic argumentation on wireless PHY-layer privacy and security threats and their relation with ISAC, framing the boundaries that research and innovation in this area should respect to avoid jeopardizing people’s rights. Full article

In exploiting large propagation delays in underwater acoustic (UWA) networks, the time-domain interference alignment (TDIA) mechanism aligns interference signals through delay-aware slot scheduling, creating additional idle time for improved transmission at the medium access control (MAC) layer. However, perfect alignment remains challenging due to arbitrary delays. This study enhances TDIA by incorporating power allocation into its transmission scheduling framework across the physical and MAC layers, following the cross-layer design principle. The proposed quasi-interference alignment (QIA) mechanism enables controlled interference on useful signals by jointly optimizing the transmission schedule and power. The formulated optimization problem to maximize network throughput is divided into two sub-problems: one for coarse slot scheduling and another for refining both scheduling and power allocation. The simulation results validate the QIA framework’s superiority over the traditional TDIA and genetic algorithm benchmarks. Full article