Laser‑Induced and MOF‑Derived Metal Oxide/Carbon Composite for Synergistically Improved Ethanol Sensing at Room temperature

Advancements in sensor technology have significantly enhanced atmospheric monitoring.Notably,metal oxide and carbon(MO_(x)/C)hybrids have gained attention for their exceptional sensitivity and room-temperature sensing performance.However,previous methods of synthesizing MO_(x)/C composites suffer from problems,including inhomogeneity,aggregation,and challenges in micropatterning.Herein,we introduce a refined method that employs a metal–organic framework(MOF)as a precursor combined with direct laser writing.The inherent structure of MOFs ensures a uniform distribution of metal ions and organic linkers,yielding homogeneous MO_(x)/C structures.The laser processing facilitates precise micropatterning(<2μm,comparable to typical photolithography)of the MO_(x)/C crystals.The optimized MOF-derived MO_(x)/C sensor rapidly detected ethanol gas even at room temperature(105 and 18 s for response and recovery,respectively),with a broad range of sensing performance from 170 to 3,400 ppm and a high response value of up to 3,500%.Additionally,this sensor exhibited enhanced stability and thermal resilience compared to previous MOF-based counterparts.This research opens up promising avenues for practical applications in MOF-derived sensing devices.

Proton‑Prompted Ligand Exchange to Achieve High‑Efficiency CsPbI_(3) Quantum Dot Light‑Emitting Diodes

CsPbI_(3)perovskite quantum dots(QDs)are ideal materials for the next generation of red light-emitting diodes.However,the low phase stability of CsPbI_(3)QDs and long-chain insulating capping ligands hinder the improvement of device performance.Traditional in-situ ligand replacement and ligand exchange after synthesis were often difficult to control.Here,we proposed a new ligand exchange strategy using a proton-prompted insitu exchange of short 5-aminopentanoic acid ligands with long-chain oleic acid and oleylamine ligands to obtain stable small-size CsPbI_(3)QDs.This exchange strategy maintained the size and morphology of CsPbI_(3)QDs and improved the optical properties and the conductivity of CsPbI_(3)QDs films.As a result,high-efficiency red QD-based light-emitting diodes with an emission wavelength of 645 nm demonstrated a record maximum external quantum efficiency of 24.45%and an operational half-life of 10.79 h.

Suitability of SDN and MEC to facilitate digital twin communication over LTE-A

Haptic is the modality that complements traditional multimedia,i.e.,audiovisual,to evolve the next wave of innovation at which the Internet data stream can be exchanged to enable remote skills and control applications.This will require ultra-low latency and ultra-high reliability to evolve the mobile experience into the era of Digital Twin and Tactile Internet.While the 5th generation of mobile networks is not yet widely deployed,Long-Term Evolution(LTE-A)latency remains much higher than the 1 ms requirement for the Tactile Internet and therefore the Digital Twin.This work investigates an interesting solution based on the incorporation of Software-defined networking(SDN)and Multi-access Mobile Edge Computing(MEC)technologies in an LTE-A network,to deliver future multimedia applications over the Tactile Internet while overcoming the QoS challenges.Several network scenarios were designed and simulated using Riverbed modeler and the performance was evaluated using several time-related Key Performance Indicators(KPIs)such as throughput,End-2-End(E2E)delay,and jitter.The best scenario possible is clearly the one integrating MEC and SDN approaches,where the overall delay,jitter,and throughput for haptics-attained 2 ms,0.01 ms,and 1000 packets per second.The results obtained give clear evidence that the integration of,both SDN and MEC,in LTE-A indicates performance improvement,and fulfills the standard requirements in terms of the above KPIs,for realizing a Digital Twin/Tactile Internet-based system.

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

De-biased knowledge distillation framework based on knowledge infusion and label de-biasing techniques

Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various domains.However,the problem of student model performance being limited due to inherent biases in the teacher model during the distillation process still persists.To address the inherent biases in knowledge distillation,we propose a de-biased knowledge distillation framework tailored for binary classification tasks.For the pre-trained teacher model,biases in the soft labels are mitigated through knowledge infusion and label de-biasing techniques.Based on this,a de-biased distillation loss is introduced,allowing the de-biased labels to replace the soft labels as the fitting target for the student model.This approach enables the student model to learn from the corrected model information,achieving high-performance deployment on lightweight student models.Experiments conducted on multiple real-world datasets demonstrate that deep learning models compressed under the de-biased knowledge distillation framework significantly outperform traditional response-based and feature-based knowledge distillation models across various evaluation metrics,highlighting the effectiveness and superiority of the de-biased knowledge distillation framework in model compression.

Hybrid Optimization Algorithm for Handwritten Document Enhancement

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

Sim-to-Real: A Performance Comparison of PPO, TD3, and SAC Reinforcement Learning Algorithms for Quadruped Walking Gait Generation

The performance of the state-of-the-art Deep Reinforcement algorithms such as Proximal Policy Optimization, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic for generating a quadruped walking gait in a virtual environment was presented in previous research work titled “A Comparison of PPO, TD3, and SAC Reinforcement Algorithms for Quadruped Walking Gait Generation”. We demonstrated that the Soft Actor-Critic Reinforcement algorithm had the best performance generating the walking gait for a quadruped in certain instances of sensor configurations in the virtual environment. In this work, we present the performance analysis of the state-of-the-art Deep Reinforcement algorithms above for quadruped walking gait generation in a physical environment. The performance is determined in the physical environment by transfer learning augmented by real-time reinforcement learning for gait generation on a physical quadruped. The performance is analyzed on a quadruped equipped with a range of sensors such as position tracking using a stereo camera, contact sensing of each of the robot legs through force resistive sensors, and proprioceptive information of the robot body and legs using nine inertial measurement units. The performance comparison is presented using the metrics associated with the walking gait: average forward velocity (m/s), average forward velocity variance, average lateral velocity (m/s), average lateral velocity variance, and quaternion root mean square deviation. The strengths and weaknesses of each algorithm for the given task on the physical quadruped are discussed.

Large-scale and high-quality III-nitride membranes through microcavity-assisted crack propagation by engineering tensile-stressed Ni layers

Epitaxially grown III-nitride alloys are tightly bonded materials with mixed covalent-ionic bonds.This tight bonding presents tremendous challenges in developing III-nitride membranes,even though semiconductor membranes can provide numerous advantages by removing thick,inflexible,and costly substrates.Herein,cavities with various sizes were introduced by overgrowing target layers,such as undoped GaN and green LEDs,on nanoporous templates prepared by electrochemical etching of n-type GaN.The large primary interfacial toughness was effectively reduced according to the design of the cavity density,and the overgrown target layers were then conveniently exfoliated by engineering tensile-stressed Ni layers.The resulting III-nitride membranes maintained high crystal quality even after exfoliation due to the use of GaN-based nanoporous templates with the same lattice constant.The microcavity-assisted crack propagation process developed for the current III-nitride membranes forms a universal process for developing various kinds of large-scale and high-quality semiconductor membranes.

Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering

Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.

Modelling individual head-related transfer function(HRTF)based on anthropometric parameters and generic HRTF amplitudes

The head-related transfer function(HRTF)plays a vital role in immersive virtual reality and augmented reality technologies,especially in spatial audio synthesis for binaural reproduction.This article proposes a deep learning method with generic HRTF amplitudes and anthropometric parameters as input features for individual HRTF generation.By designing fully convolutional neural networks,the key anthropometric parameters and the generic HRTF amplitudes were used to predict each individual HRTF amplitude spectrum in the full-space directions,and the interaural time delay(ITD)was predicted by the transformer module.In the amplitude prediction model,the attention mechanism was adopted to better capture the relationship of HRTF amplitude spectra at two distinctive directions with large angle differences in space.Finally,with the minimum phase model,the predicted amplitude spectrum and ITDs were used to obtain a set of individual head-related impulse responses.Besides the separate training of the HRTF amplitude and ITD generation models,their joint training was also considered and evaluated.The root-mean-square error and the log-spectral distortion were selected as objective measurement metrics to evaluate the performance.Subjective experiments further showed that the auditory source localisation performance of the proposed method was better than other methods in most cases.

Engineering for Inclusion:Empowering Individuals with Physical and Neurological Differences through Engineering Invention,Research,and Development

1.Background The use of engineering tools,design,research,and thinking to create environments and capabilities whereby individuals who are currently under-employed or unemployed due to a physical disability(e.g.,amputation or spinal cord injury)or neurological difference(e.g.,autism)are enabled to become fully productive and employed members of society has been the implicit goal of decades of research at Vanderbilt University and elsewhere.At Vanderbilt University,progress in these areas has been greatly facilitated by the proximity of the School of Engineering to the world-class Vanderbilt University Medical Center and the resulting close collaboration between engineering and medical researchers.

Developing a Secure Framework Using Feature Selection and Attack Detection Technique

Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.

Digital Image Encryption Algorithm Based on Double Chaotic Map and LSTM

In the era of network communication,digital image encryption(DIE)technology is critical to ensure the security of image data.However,there has been limited research on combining deep learning neural networks with chaotic mapping for the encryption of digital images.So,this paper addresses this gap by studying the generation of pseudo-random sequences(PRS)chaotic signals using dual logistic chaotic maps.These signals are then predicted using long and short-term memory(LSTM)networks,resulting in the reconstruction of a new chaotic signal.During the research process,it was discovered that there are numerous training parameters associated with the LSTM network,which can hinder training efficiency.To overcome this challenge and improve training efficiency,the paper proposes an improved particle swarm optimization(IPSO)algorithm to optimize the LSTM network.Subsequently,the obtained chaotic signal from the optimized model training is further scrambled,obfuscated,and diffused to achieve the final encrypted image.This research presents a digital image encryption(DIE)algorithm based on a double chaotic map(DCM)and LSTM.The algorithm demonstrates a high average NPCR(Number of Pixel Change Rate)of 99.56%and a UACI(Unified Average Changing Intensity)value of 33.46%,indicating a strong ability to resist differential attacks.Overall,the proposed algorithm realizes secure and sensitive digital image encryption,ensuring the protection of personal information in the Internet environment.

Using Biomass in Power Generation for Supplying Electrical and Thermal Energy in Iran and Evaluation of Environmental Pollution Spread

The main objective of this study is estimating environmental pollution of hybrid biomass and co-generation power plants. Efficiency of direct tapping of biomass is about 15%-20%. Consequently, about 80% of energy would be waste in this method. While in co-generation power plant, this number could improve to more than 50%. Therefore, to achieve higher efficiency in utilizing biomass energy, co-generation power plants is proposed by using biogas as fuel instead of natural gas. Proposed system would be supplied thermal and electrical energy for non-urban areas of Iran. In this regard, process of fermentation and gas production from biomass in a vertical digester is studied and simulated using analytic methods. Various factors affecting the fermentation, such as temperature, humidity, PH and optimal conditions for the extraction of gas from waste agriculture and animal are also determined. Comparing between the pollution emission from fossil fuel power plants and power plants fed by biomass shows about 88% reduction in greenhouse emission which significant number.

Effect of thickness of antimony selenide film on its photoelectric properties and microstructure

Antimony selenide(Sb2Se3) films are widely used in phase change memory and solar cells due to their stable switching effect and excellent photovoltaic properties. These properties of the films are affected by the film thickness. A method combining the advantages of Levenberg–Marquardt method and spectral fitting method(LM–SFM) is presented to study the dependence of refractive index(RI), absorption coefficient, optical band gap, Wemple–Di Domenico parameters, dielectric constant and optical electronegativity of the Sb2Se3films on their thickness. The results show that the RI and absorption coefficient of the Sb2Se3films increase with the increase of film thickness, while the optical band gap decreases with the increase of film thickness. Finally, the reasons why the optical and electrical properties of the film change with its thickness are explained by x-ray diffractometer(XRD), energy dispersive x-ray spectrometer(EDS), Mott–Davis state density model and Raman microstructure analysis.

Abusive adversarial agents and attack strategies in cyber-physical systems

The exponential increase in IoT device usage has spawned numerous cyberspace innovations.IoT devices,sensors,and actuators bridge the gap between physical processes and the cyber network in a cyber-physical system(CPS).Cyber-physical system is a complex system from a security perspective due to the heterogeneous nature of its components and the fact that IoT devices can serve as an entry point for cyberattacks.Most adversaries design their attack strategies on systems to gain an advantage at a relatively lower cost,whereas abusive adversaries initiate an attack to inflict maximum damage without regard to cost or reward.In this paper,a sensor spoofing attack is modelled as a malicious adversary attempting to cause system failure by interfering with the feedback control mechanism.It is accomplished by feeding spoofed sensor values to the controller and issuing erroneous commands to the actuator.Experiments on a Simulink-simulated linear CPS support the proof of concept for the proposed abusive ideology,demonstrating three attack strategies.The impact of the evaluations stresses the importance of testing the CPS security against adversaries with abusive settings for preventing cyber-vandalism.Finally,the research concludes by highlighting the limitations of the proposed work,followed by recommendations for the future.

Isolation Enhancement in a Compact Four-Element MIMO Antenna for Ultra-Wideband Applications

Mutual coupling reduction or isolation enhancement in antenna arrays is an important area of research as it severely affects the performance of an antenna.In this paper,a new type of compact and highly isolated Multiple-Input-Multiple-Output(MIMO)antenna for ultra-wideband(UWB)applications is presented.The design consists of four radiators that are orthogonally positioned and confined to a compact 40×40×0.8 mm3 space.The final antenna design uses an inverted L shape partial ground to produce an acceptable reflection coefficient(S11<−10 dB)in an entire UWB band(3.1–10.6)giga hertz(GHz).Moreover,the inter-element isolation has also been enhanced to>20 db for majority of the UWB band.The antenna was fabricated and tested with the vector network analyzer(VNA)and in an anechoic chamber for scattering parameters and radiation patterns.Furthermore,different MIMO diversity performance metrics are also measured to validate the proposed model.The simulation results and the experimental results from the constructed model agree quite well.The proposed antenna is compared with similar designs in recently published literature for various performance metrics.Because of its low envelope correlation coefficient(ECC<0.1),high diversity gain(DG>9.99 dB),peak gain of 4.6 dB,reduced channel capacity loss(CCL<0.4 b/s/Hz),and average radiation efficiency of over 85%,the proposed MIMO antenna is ideally suited for practical UWB applications.

GWAS identifies candidate genes controlling adventitious rooting in Populus trichocarpa

Adventitious rooting(AR)is critical to the propagation,breeding,and genetic engineering of trees.The capacity for plants to undergo this process is highly heritable and of a polygenic nature;however,the basis of its genetic variation is largely uncharacterized.To identify genetic regulators of AR,we performed a genome-wide association study(GWAS)using 1148 genotypes of Populus trichocarpa.GWASs are often limited by the abilities of researchers to collect precise phenotype data on a high-throughput scale;to help overcome this limitation,we developed a computer vision system to measure an array of traits related to adventitious root development in poplar,including temporal measures of lateral and basal root length and area.GWAS was performed using multiple methods and significance thresholds to handle non-normal phenotype statistics and to gain statistical power.These analyses yielded a total of 277 unique associations,suggesting that genes that control rooting include regulators of hormone signaling,cell division and structure,reactive oxygen species signaling,and other processes with known roles in root development.Numerous genes with uncharacterized functions and/or cryptic roles were also identified.These candidates provide targets for functional analysis,including physiological and epistatic analyses,to better characterize the complex polygenic regulation of AR.

Construction and Implementation of a Privacy-Preserving Identity-Based Encryption Architecture

A recent proposal by Adams integrates the digital credentials (DC) technology of Brands with the identity-based encryption (IBE) technology of Boneh and Franklin to create an IBE scheme that demonstrably enhances privacy for users. We refer to this scheme as a privacy-preserving identity-based encryption (PP-IBE) construction. In this paper, we discuss the concrete implementation considerations for PP-IBE and provide a detailed instantiation (based on q-torsion groups in supersingular elliptic curves) that may be useful both for proof-of-concept purposes and for pedagogical purposes.

Appraising the Manifestation of Optimism Bias and Its Impact on Human Perception of Cyber Security: A Meta Analysis

Cyber threats and risks are increasing exponentially with time. For preventing and defense against these threats and risks, precise risk perception for effective mitigation is the first step. Risk perception is necessary requirement to mitigate risk as it drives the security strategy at the organizational level and human attitude at individual level. Sometime, individuals understand there is a risk that a negative event or incident can occur, but they do not believe there will be a personal impact if the risk comes to realization but instead, they believe that the negative event will impact others. This belief supports the common belief that individuals tend to think of themselves as invulnerable, i.e., optimistically bias about the situation, thus affecting their attitude for taking preventive measures due to inappropriate risk perception or overconfidence. The main motivation of this meta-analysis is to assess that how the cyber optimistic bias or cyber optimism bias affects individual’s cyber security risk perception and how it changes their decisions. Applying a meta-analysis, this study found that optimistic bias has an overall negative impact on the cyber security due to the inappropriate risk perception and considering themselves invulnerable by biasing that the threat will not occur to them. Due to the cyber optimism bias, the individual will sometimes share passwords by considering it will not be maliciously used, lack in adopting of preventive measures, ignore security incidents, wrong perception of cyber threats and overconfidence on themselves in the context of cyber security.