bluuebunny/crossref_metadata_2025 huggingface dataset:
A brain tumor identification using fully convolution neural networks in the deep learning
Authors: Krishna Bandi , Vankdothu Ramdas , Veeru Banothu , Chaitanya Jampala
Published: March 2024

We used post dispensation to flat out the segmentations generated via our model. And the beautiful meaning of the medical image analysis and in the direction of enhancing the identification of brain tumors MRI is considered to be outstanding within the current time towards the increased need to qualify with reliable information using semantic segmentation. CNN is being used to detect brain tumors efficiently and precisely. In evaluating and recognizing tumors, in its place of with 2D detection and dice cutting, we can use 3Dimension segmentation for identification, which makes it additionally precise. Similar algorithms' effort better for unlike sub regions are the fusion of some of the best algorithms that can produce a high-quality result in complete segmentation with the aid of FCN. Medical imaging is an area of increasing interest because there is a growing need for automated, fast, and efficient diagnostics to provide imaging capabilities and better quality compared to human eyes. Brain tumors, which are the second largest cause of death due to cancer-related diseases in males aged between 20 and 39, and the fifthlargest cause of cancer, have caused death in females aged in the same category. 1 Introduction
Resource management system database maintenance in cloud computing
Authors: Ladda Ashish , Devunuri Sharanya , Vankdothu Ramdas
Published: March 2024

The Resource Management System (RMS) is a comprehensive solution designed to optimize resource allocation, enhance project efficiency, and streamline customer interactions within a dynamic business environment. The system encompasses four key components: projects, resources, customers, and business units. It offers a range of functionalities including create, update, retrieve, and delete operations to facilitate seamless operations and data management. The RMS is built using Spring Boot (Java) for the backend, providing a robust and scalable foundation. The front end is developed using React, ensuring a modern and user-friendly interface. Data is managed in a MySQL database, offering reliability and data integrity. Communication between frontend and backend is achieved through RESTful APIs. Resource Management system typically manage and allocate non-human resources and as well human resources throughout an organization, However, if we choose to pursue human resources management, we will recruit, hire, train, manage employees and staff which is also a variation of resources. It provides comprehensive solution designed to optimize resource allocation, enhance project efficiency, and streamline customer interactions within a dynamic business environment.
Breast cancer classification and prediction methods by employing machine and deep learning approaches-A survey
Authors: Yerraboina Sirisha , Bommala Harikrishna , Madireddy Vineela
Published: March 2024

Breast carcinoma stands as one of the most perilous afflictions affecting females, lacking an effective treatment to date. Recent advancements in deep learning techniques, coupled with artificial intelligence (AI), have demonstrated promising results in breast cancer identification. This innovation facilitates early detection, consequently enhancing patient survival rates. Deep learning necessitates minimal human intervention for feature extraction, contrasting with traditional machine learning methods. The ML and DL techniques are practised and comparison of all these techniques were shown. Specifically, emphasis is placed on genomic and histopathologic imaging data. Various algorithms, including R, SVM, logistic regression, KNN, Naïve Bayes, CNN, and ANN, are thoroughly researched and valuated to gauge their efficacy. Furthermore, many screening protocols were deployed to identify and examine the datasets. Lastly, the paper explores the challenges encountered and the most possible directions to detect the breast cancer .Hence researchers and clinicians with a thorough understanding and insights into this deep learning domain.
Revolutionary building approach for maximal photovoltaic system results to improve maximum power point tracking in solar inverter
Authors: Sridhar P. , Sri Nandhini Kowsalya S. , Venkatasudhahar M. , Sathish Kumar T. , Gangopadhyay Amit , Gurnadha Gupta Koppuravuri , Manikandan G.
Published: March 2024

Due to the inherent frequency ripple in single-phase photovoltaic (PV) grid-connected solar inverters, the Maximum Power Point Tracking (MPPT) will inevitably be affected. To improve the MPPT performances, a passive LC power decoupling circuit with a Robust Sliding-Mode Control (RSMC) is proposed in this article. The frequency pulsation on the DC link is effectively canceled with the passive LC decoupling path. Thus, the MPPT accuracy is significantly enhanced, and the utilization of a small DC-link capacitor becomes possible. The resonance between the LC circuit and the main DC-link capacitor appears, which can be damped through an active damping method. The proposed RSMC offers good steady-state, dynamic performance (voltage fluctuation and settling time), and the robustness of the DC-link voltage, which is also beneficial to MPPT control in terms of high accuracy and fast dynamics. The systematic design of RSMC is presented, and a detailed parameter optimization design of the LC decoupling circuit is discussed. Experimental tests are performed on a 2.5-kW single-phase grid-connected solar inverter, and the results validate the effectiveness of the proposed strategy.
IoT enabled smart agriculture
Authors: Karthik S.L. , Kurella Prudhvi , Jinith Reddy L.
Published: March 2024

Individuals in India get by off agribusiness. Significant job in nation's economy. Relocation from provincial to metropolitan regions impedes farming today. Observing the climate alone won't support farming efficiency. No significant factors lessen efficiency. To settle these issues, agribusiness needs robotization. An independent water system framework saves ranchers time, cash, and power. Customary homestead water system requires actual inclusion. With programmed water system, human association is diminished. Sensors and the Internet of Things (IOT) can persistently screen crops and illuminate ranchers about crop development and gather time, expanding crop efficiency and guaranteeing ideal item conveyance to purchasers. We use IOT for brilliant agriculture to tackle this issue. This venture gathers and cycles field information from temperature, humidity, soil moisture, and rain sensors. These sensors use web innovations to remotely control and screen sensor information by means of a wireless sensor network.
Personalized Job Opportunity Finder powered by Web Scraping
Authors: Akshay Budharapu , Arun Kumar Ravula , Ramyasri Thudum , Arpitha Kosuna
Published: March 2024

"Personalized Job Opportunity Finder powered by Web Scraping" research presents a comprehensive exploration the development and implementation of an innovative Job Portal, aiming to redefine the traditional job search experience. The project leverages cutting-edge techniques and integration with prominent communication tools. The experimental setup involves meticulous web scraping from renowned job portals, Google and Apple, using Puppeteer and Cheerio for data extraction. The user interface is thoughtfully designed, featuring an intuitive registration form and a dynamic home page that showcases personalized job recommendations. The research delves into the advantages of scraping job data from top companies, showcasing its efficacy compared to traditional methods such as partnerships and direct job postings. Big companies such as Google, Apple and Meta don't use partnerships for most of its hiring, that makes it difficult for candidates who aspire careers at such companies. Direct partnerships may have delays in updating job listings, whereas scraping allows for more immediate access to new postings.
ISpot: An intelligent real-time pothole spot identification model using a CNN algorithm
Authors: Patthi Sridhar , Padhy Neelamadhab
Published: March 2024

Maintaining roads is a very intricate and significant global concern. Detecting road abnormalities, including potholes, is crucial in road monitoring and management. Identifying potholes is essential to minimize road accidents and car damage and improve travel comfort. Authorities have long seen road maintenance as a significant concern. However, the absence of accurate identification and connecting of road potholes exacerbates the problem. An end-to-end system named Intelligent Spotting (iSpot) of Pathole has been developed to address this issue by providing real-time identification, tracking, and geographical mapping of potholes around the city. A Convolutional Neural Network (CNN) framework is suggested and assessed using a real-world dataset for detecting potholes. Real-time maps displaying pothole locations are created using the Google Maps Application Programming Interface (API). Both pothole identification and mapping are combined into an Android application to offer a comprehensive service via this technology. The suggested model outperforms the baseline techniques regarding accuracy, precision, and F score.
Deep feature analysis, classification with AI-driven gastrointestinal diagnostics
Authors: Srividya Janagama , Bommala Harikrishna
Published: March 2024

Several AI-based methods have substantially progressed the area of medical image and video-based diagnostics, which encompasses radiography, pathology, endoscopy, and the categorization of gastrointestinal (GI) diseases. When it comes to classifying numerous GI disorders, the majority of prior research that relies solely on spatial cues performs poorly. While some prior research has made use of temporal features trained on a 3D convolution neural network, these studies have focused on a very small subset of the gastrointestinal system and have used very few classes. To address these concerns, we introduce an all-inclusive AI-based system for classifying different GI illnesses using endoscopic recordings. This system can extract spatial and temporal data concurrently, leading to improved classification performance. For temporal variables, we employ a long short-term memory model; for spatial features, we employ two independent residual networks in cascade mode.
Enhancing electric vehicle efficiency through model predictive control of power electronics
Authors: Ivanovich Vatin Nikolai , Madhavi Arelli
Published: March 2024

This study examines the improvement of electric vehicle (EV) economy by using Model Predictive Control (MPC) in power electronics, with the goal of optimizing system performance. Experimental assessments done on different battery parameters have identified a spectrum of capacities, ranging from 55 kWh to 75 kWh, and voltages, ranging from 380V to 450V, that impact the total energy storage and power production capabilities. The efficiency percentages recorded in the battery systems ranged from 90% to 95%, suggesting differences in energy losses throughout the operations of charging and discharging. Furthermore, examinations of power electronics control configurations highlighted the significance of PWM frequencies (varying from 8 kHz to 12 kHz) and modulation indices (0.75 to 0.85) on the efficiency of power conversion. The results indicated efficiency rates ranging from 94% to 97%, emphasizing the efficacy of MPC-based techniques in improving power flow. The assessment of electric vehicle (EV) performance parameters demonstrated driving ranges ranging from 140 km to 180 km, with energy consumption rates ranging from 50 kWh to 60 kWh. The efficiency metrics ranged from 2.5 km/kWh to 3.0 km/kWh, and were directly affected by the battery properties and improvements in power electronics. Moreover, there was a little change in the link between temperature variations (ambient temperature ranging from 23°C to 29°C and battery temperature from 32°C to 40°C) and efficiency. This highlights the system's sensitivity to external variables. In summary, this relationship between battery characteristics, power electronics control, and environmental conditions in determining the efficiency of electric vehicles (EVs). The results emphasize the importance of customized setups and control techniques based on model predictive control (MPC) in optimizing energy use and increasing the distance electric cars can travel. These findings provide valuable knowledge for the development of sustainable transportation solutions in the electric vehicle industry.
Hybrid Renewable Energy Microgrids: A Genetic Algorithm Approach to System Design
Authors: Sobti Rajeev , Anjaneyulu M.
Published: March 2024

The paper examines the use of genetic algorithm (GA) methods to optimize hybrid renewable energy microgrids by merging various renewable sources and energy storage technologies. An examination of meteorological data over many days reveals fluctuations in solar irradiance ranging from 4.8 kW/m² to 5.5 kW/m² and wind speed oscillating between 3.9 m/s and 4.5 m/s, indicating the presence of dynamic weather conditions. An analysis of energy generating capabilities reveals a wide range of potentials, with solar capacities varying from 80 kW to 150 kW and wind capacities ranging from 60 kW to 120 kW across different sources. An analysis of Energy Storage System (ESS) specifications shows a range of values for maximum capacities, charge/discharge efficiencies (ranging from 85% to 96%), and maximum charge/discharge rates (from 60 kW to 100 kW), highlighting the need for flexible energy storage systems. The examination of microgrid load profiles reveals the presence of diverse energy needs, with residential loads oscillating between 48 kW and 55 kW, commercial loads ranging from 40 kW to 47 kW, and industrial loads spanning from 30 kW to 36 kW. A percentage change study reveals the ability to adapt, with solar irradiance and wind speed showing mild fluctuations of roughly 14% and nearly 15% respectively. In contrast, renewable source capacity demonstrate significant percentage changes ranging from around 40% to 50%. These results highlight the ever-changing characteristics of renewable energy sources, underlining the need for strong optimization tactics in microgrid systems. The study emphasizes the potential of GA-based approaches in developing efficient microgrids, promoting sustainable and dependable energy solutions in the face of changing environmental circumstances and varied energy requirements.
Advanced battery management systems: An in-depth comparative study
Authors: Nijhawan Ginni , Annapurna T.
Published: March 2024

The research focuses on doing a thorough comparative analysis of different Battery Management Systems (BMS) used in modern battery technologies. Its goal is to completely examine and identify performance differences across several important parameters. Empirical data analysis reveals substantial disparities across batteries for crucial factors. The battery specs showed a range of capacities, with Battery B003 having the maximum capacity of 120 Ah and Battery B002 having the lowest capacity of 85 Ah. The temperature performance testing revealed significant variances in operating temperatures, with B003 exhibiting the widest temperature range of -20 to 50°C. Significant variations in the rates of charging and discharging were noted, with B004 exhibiting the greatest rates. Furthermore, there were significant differences in efficiency and aging properties. Specifically, B005 had the greatest efficiency, reaching 97%, and the lowest deterioration rate, which was just 0.09%. These data highlight significant variations across batteries, stressing the need for customized BMS techniques. The comparison study provides crucial insights into the complex intricacies of battery behavior, offering fundamental information essential for creating efficient BMS designs. Comprehending these differences is crucial for improving battery management techniques, guaranteeing effective and secure battery operations in various applications, and directing future progress in energy storage systems for electric cars, renewable energy, and portable devices.
Genetic algorithms for optimizing the layout of wireless charging networks
Authors: Mittal Vaibhav , Shamila M.
Published: March 2024

This study explores the improvement of wireless charging network configurations for electric cars (EVs) using genetic algorithms, with the goal of increasing charging efficiency and network performance. The network optimization process takes into account the starting characteristics of include their geographical coordinates, power capacity, and beginning energy levels. Examination of the distance matrix exposes diverse distances between nodes, which impact energy consumption and charging efficiency. The energy consumption estimates between pairs of nodes illustrate the charging needs across the network, revealing that nodes that are farther away have greater energy consumption. The use of genetic algorithms yields a wide range of layouts that are assessed based on their fitness ratings, indicating the excellence of configurations in terms of coverage and connection. Percentage change study demonstrates the modifications in power capacity and node energy levels after optimization, showing prospective improvements in charging capabilities and efficiency. The correlation between node location and energy use is apparent, as nodes in closer proximity demonstrate decreased energy utilization. The convergence of fitness scores demonstrates the algorithm's effectiveness in achieving solutions that are very close to ideal, resulting in significant improvements in charging coverage and energy efficiency. The study highlights the effectiveness of genetic algorithms in improving wireless charging networks, providing valuable information on spatial optimization tactics, energy use patterns, and the resulting improvements in network performance. These results have implications for creating wireless charging infrastructures that are more efficient and long-lasting, in order to satisfy the changing needs of electric car charging networks.
Annotation de la cohérence dans des textes d’élèves et jugements de cohérence d’enseignants du primaire
Authors: Garcia-Debanc Claudine , Bras Myriam , Vieu Laure
Published: March 2024

Au croisement des tâches « cohérence » et « formation des enseignants » de l’ANR ECalm, cette contribution porte sur la cohérence de textes d’élèves d’école primaire et de début de collège. Elle met en regard des annotations discursives prenant appui sur une extension de la SDRT (Segmented Discourse Representation Theory) permettant l’annotation de points d’incohérence et des jugements de cohérence formulés par des enseignant.e.s du premier degré dans le cadre d’une session de formation continue. La cohérence est envisagée comme une propriété de la représentation de discours que se construit celui qui interprète le texte tout au long de sa lecture. Le corpus analysé est issu du corpus RESOLCO, l’un des corpus de textes scolaires collectés, transcrits et annotés dans le cadre de l’ANR E-Calm. Ce corpus est constitué de textes en réponse à une même consigne d’écriture : une tâche-problème demandant aux élèves la rédaction d’un texte narratif impliquant la résolution d’anaphores de divers types. Cette analyse comparative vise tout d’abord à mettre à l’épreuve le modèle d’annotation discursive. Les incohérences visualisées dans les représentations sémantiques issues de l’analyse linguistique sous forme de graphes sont également perçues par les enseignant.e.s confronté.e.s à la lecture de ces mêmes textes, ce qui permet de valider le modèle. L’analyse des échanges à l’intérieur d’un groupe d’enseignants fait apparaitre que l’un des deux dispositifs proposés favorise la mise en place chez les enseignants d’une posture de lecteur à la recherche de la construction de la cohérence et non d’évaluateur en référence à une norme linguistique et leur permet d’interroger les calculs interprétatifs permettant de formuler des jugements de cohérence. La présentation des graphes aux enseignant.e.s les aide ensuite à objectiver leurs intuitions linguistiques et leurs jugements de cohérence.
La faute à qui ? D’une enquête sur les idées et les pratiques de correction à la formation en service
Authors: Ujcich Veronica
Published: March 2024

Cet article est issu des réflexions sur le rôle de la recherche dans la formation des enseignants pour ce qui concerne la pratique didactique de la correction du texte écrit. Nous avons voulu nous positionner « du côté des enseignants », en recueillant des informations sur leurs perceptions et leurs pratiques déclarées. Les informations ont été recueillies à l’aide d’un questionnaire de 48 questions distribué en ligne (Google form), qui a été rempli par 200 enseignants du primaire (en France, niveau CM2) de différentes régions d’Italie. Lors de la compilation des réponses, il était demandé aux enseignants de garder la même posture adoptée à l’égard des textes produits par des enfants à la fin de l’école primaire. L’analyse des réponses a été le point de départ pour la conception de cours de formation organisés par la suite dans trois contextes différents. Les expériences de formation ont permis de discuter les données collectées et de les interpréter à la lumière des activités de groupe, sous forme de recherche-action.
Positive effects of public breeding on US rice yields under future climate scenarios
Authors: Wang Diane R. , Jamshidi Sajad , Han Rongkui , Edwards Jeremy D. , McClung Anna M. , McCouch Susan R.
Published: March 2024

In this study, we model and predict rice yields by integrating molecular marker variation, varietal productivity, and climate, focusing on the Southern U.S. rice-growing region. This region spans the states of Arkansas, Louisiana, Texas, Mississippi, and Missouri and accounts for 85% of total U.S. rice production. By digitizing and combining four decades of county-level variety acreage data (1970 to 2015) with varietal information from genotyping-by-sequencing data, we estimate annual historical county-level allele frequencies. These allele frequencies are used together with county-level weather and yield data to develop ten machine learning models for yield prediction. A two-layer meta-learner ensemble model that combines all ten methods is externally evaluated against observations from historical Uniform Regional Rice Nursery trials (1980 to 2018) conducted in the same states. Finally, the ensemble model is used with forecasted weather from the Coupled Model Intercomparison Project across the 110 rice-growing counties to predict production in the coming decades for Composite Variety Groups assembled based on year of release, breeding program, and several breeding trends. Results indicate positive effects over time of public breeding on rice resilience to future climates, and potential reasons are discussed.
Sleep deprivation drives brain-wide changes in cholinergic presynapse abundance in
Drosophila melanogaster
Authors: Weiss Jacqueline T. , Blundell Mei Z. , Singh Prabhjit , Donlea Jeffrey M.
Published: March 2024

Sleep is an evolutionarily conserved state that supports brain functions, including synaptic plasticity, in species across the animal kingdom. Here, we examine the neuroanatomical and cell-type distribution of presynaptic scaling in the fly brain after sleep loss. We previously found that sleep loss drives accumulation of the active zone scaffolding protein Bruchpilot (BRP) within cholinergic Kenyon cells of the
Drosophila melanogaster
mushroom body (MB), but not in other classes of MB neurons. To test whether similar cell type–specific trends in plasticity occur broadly across the brain, we used a flp-based genetic reporter to label presynaptic BRP in cholinergic, dopaminergic, GABAergic, or glutamatergic neurons. We then collected whole-brain confocal image stacks of BRP intensity to systematically quantify BRP, a marker of presynapse abundance, across 37 neuropil regions of the central fly brain. Our results indicate that sleep loss, either by overnight (12-h) mechanical stimulation or chronic sleep disruption in
insomniac
mutants, broadly elevates cholinergic synapse abundance across the brain, while synapse abundance in neurons that produce other neurotransmitters undergoes weaker, if any, changes. Extending sleep deprivation to 24 h drives brain-wide upscaling in glutamatergic, but not other, synapses. Finally, overnight male–male social pairings induce increased BRP in excitatory synapses despite male–female pairings eliciting more waking activity, suggesting experience-specific plasticity. Within neurotransmitter class and waking context, BRP changes are similar across the 37 neuropil domains, indicating that similar synaptic scaling rules may apply across the brain during acute sleep loss and that sleep need may broadly alter excitatory–inhibitory balance in the central brain.
Information decomposition in complex systems via machine learning
Authors: Murphy Kieran A. , Bassett Dani S.
Published: March 2024

One of the fundamental steps toward understanding a complex system is identifying variation at the scale of the system’s components that is most relevant to behavior on a macroscopic scale. Mutual information provides a natural means of linking variation across scales of a system due to its independence of functional relationship between observables. However, characterizing the manner in which information is distributed across a set of observables is computationally challenging and generally infeasible beyond a handful of measurements. Here, we propose a practical and general methodology that uses machine learning to decompose the information contained in a set of measurements by jointly optimizing a lossy compression of each measurement. Guided by the distributed information bottleneck as a learning objective, the information decomposition identifies the variation in the measurements of the system state most relevant to specified macroscale behavior. We focus our analysis on two paradigmatic complex systems: a Boolean circuit and an amorphous material undergoing plastic deformation. In both examples, the large amount of entropy of the system state is decomposed, bit by bit, in terms of what is most related to macroscale behavior. The identification of meaningful variation in data, with the full generality brought by information theory, is made practical for studying the connection between micro- and macroscale structure in complex systems.
Cascaded momentum-space polarization filters enabled label-free black-field microscopy for single nanoparticles analysis
Authors: Liu Yang , Chen Qiankun , Zhang Hongli , Feng Zeyu , Zou Gang , Zhang Douguo
Published: March 2024

Label-free optical imaging of single-nanometer-scale matter is extremely important for a variety of biomedical, physical, and chemical investigations. One central challenge is that the background intensity is much stronger than the intensity of the scattering light from single nano-objects. Here, we propose an optical module comprising cascaded momentum-space polarization filters that can perform vector field modulation to block most of the background field and result in an almost black background; in contrast, only a small proportion of the scattering field is blocked, leading to obvious imaging contrast enhancement. This module can be installed in various optical microscopies to realize a black-field microscopy. Various single nano-objects with dimensions smaller than 20 nm appear distinctly in the black-field images. The chemical reactions occurring on single nanocrystals with edge lengths of approximately 10 nm are in situ real-time monitored by using the black-field microscopy. This label-free black-field microscopy is highly promising for a wide range of future multidisciplinary science applications.
Ultrafast Carrier Dynamics in Ba6Cr2S10 Modified by Toroidal Magnetic Phase Transition
Authors: Jiang 姜 Litong 丽桐 , Jiang 姜 C. Y. 聪颖 , Tian 田 Y. C. 义超 , Zhao 赵 H. 惠 , Zhang 张 J. 俊 , Tian 田 Z. Y. 珍耘 , Fu 付 S. H. 少华 , Liang 梁 E. J. 二军 , Wang 望 X. C. 贤成 , Jin 靳 Changqing 常青 , Zhao 赵 Jimin 继民
Published: March 2024

Ba6Cr2S10 is a recently discovered magnetic material, in which the spins are aligned ferromagnetically in the ab-plane and anti-parallelly in a paired form along the c-axis. It is characterized as a quasi-one dimensional (1D) dimerized structure with a ferrotoroidic order, forming the simplest candidate toroidal magnetic (TM) order and exhibiting an anti-ferromagnetic-like transition at around 10 K. Time-resolved ultrafast dynamics investigation of the novel A–Cr–S (A: metal elements) family of quantum materials has rarely been reported. Here, we investigate the time-resolved pump-probe ultrafast dynamics of a Ba6Cr2S10 single crystal. A prominent change in the photo-excited carrier dynamics is observed at T
c = 10 K, corresponding to the reported TM-paramagnetic phase transition. A potential unknown magnetic transition is also found at T
* = 29 K. Our results provide new evidence for the TM magnetic transition in Ba6Cr2S10, and shed light on phase transitions in TM quantum materials.
Active manipulation of the plasmonic induced asymmetric photonic spin Hall effect
Authors: Jiang Ting , Shu Yetao , Song Yifei , Zhang Yong , Wen Zhaoxin , Luo Zhaoming
Published: March 2024

The asymmetric photonic spin Hall effect (APSHE) induced by surface plasmon polaritons in a graphene-based structure is actively manipulated by external magnetic field and electric field. It is revealed that the spin-dependent splitting exhibits spatio-temporal asymmetric property due to the involvement of the anisotropic graphene. The peak of asymmetry degree in APSHE at the position of reflectance valley corresponds toward a smaller incident angle with the increase of magnetic field intensity or Fermi energy, which is attributed to the tunability of reflectance for the graphene-based structure. Based on the asymmetric splitting shift, a potential application is proposed for detecting low concentration gas molecules and the detection resolution can be dynamically tunable by changing the magnetic field intensity and Fermi energy. This study may provide a new reference in the fabrication of graphene-based plasmonic sensor devices.
Strain induced modulations in the thermoelectric properties of 2D SiH and GeH monolayers: insights from first-principle calculations
Authors: Banik Rati Ray , Ghosh Swarup , Chowdhury Joydeep
Published: March 2024

The present paper is primarily focused to understand the strain driven alterations in thermoelectric (TE) properties of two-dimensional SiH and GeH monolayers from first-principle calculations. Electronic band structures and the associated TE properties of the compounds under ambient and external strains have been critically unveiled in terms of Seebeck coefficients, electrical conductivities, power factors and electronic thermal conductivities. The phonon dispersion relations have also been investigated to estimate the lattice thermal conductivities of the systems. The TE figure of merits of SiH and GeH monolayers under ambient and external strains have been explored from the collective effects of their Seebeck coefficients, electrical conductivities, electronic and lattice thermal conductivities. The present study will be helpful in exploring the strain induced TE responses of SiH and GeH compounds which in turn may bear potential applications in clean and global energy conservation.
Theory of tensorial Gilbert damping in antiferromagnets
Authors: Dhali Prasad , Mondal Ritwik
Published: March 2024

Although the magnetic Gilbert damping was considered as a scalar quantity in micromagnetic and atomistic spin simulations, recent investigations show that the Gilbert damping parameter is a tensor. Here, we investigate the effect of anisotropic and chiral damping in one-sublattice ferromagnets and two-sublattice antiferromagnets. We employ linear response theory to calculate the susceptibility with the damping tensor and determine the ferromagnetic and antiferromagnetic resonance frequencies together with the effective damping. Our results show that apart from the scalar Gilbert damping, the antisymmetric chiral damping has a significant contribution to the spin dynamics that it breaks the antiparallel alignment of two sublattices in antiferromagnets even in the absence of an applied field. To this end, we also compare the tensorial damping and cross-sublattice scalar damping in antiferromagnets.
Compound fault diagnosis of rolling bearings based on AVMD and IMOMEDA
Authors: Lu Zhijie , Yan Xiaoan , Wang Zhiliang , Zhang Yuyan , Sun Jianjun , Ma Chenbo
Published: March 2024

The intricate nature of compound fault diagnosis in rolling bearings during nonstationary operations poses a challenge. To address this, a novel technique combines adaptive variational mode decomposition (AVMD) with improved multipoint optimal minimum entropy deconvolution adjustment (IMOMEDA). The compound fault signal is isolated through AVMD, with internal parameters obtained via a new indicator termed integrated fault-impact measure index guiding the improved dung beetle optimizer. An adaptive selection method, using a weight factor, chooses the intrinsic mode function containing principal fault data. IMOMEDA whose key parameters are determined by a novel combinatorial strategy is then employed to deconvolute selected fault components, enhancing periodic fault impulses by removing complex interferences and ambient noise. The deconvoluted signal undergoes enhanced envelope spectrum processing to extract fault frequencies and identify fault types. Numerical simulations and experimental data confirm the method’s effectiveness and feasibility for compound faults diagnosis of rolling bearings, showcasing its superiority over existing techniques.
A novel robust moving horizon estimator for discrete-time linear systems subject to measurement outliers
Authors: Wang Zhongxin , Liu Zhilin , Yuan Shouzheng , Ma Yingkai , Song Simeng
Published: March 2024

State estimation is a crucial problem in modern industries and has been widely applied across various fields. The performance of the estimator depends on the quality of the measurement data. Measurements being corrupted by outliers is becoming an unavoidable phenomenon that leads to degradation of estimator performance. It is critical to develop estimators with outlier suppression capabilities to mitigate the adverse impact of measurement outliers. In this paper, we propose an effective outlier suppression technique for discrete-time linear systems within the framework of moving horizon estimation (MHE). The proposed estimator solves the issues of poor estimation accuracy and low computational efficiency among the existing MHE-based outlier-robust estimators. Moreover, the proposed method allows us to not only achieve robust state estimation but also detect outliers. Specifically, we propose a set of least-squares cost functions and an outlier identification mechanism to implement the estimation process. Subsequently, the stability of the estimation error of the proposed estimator is demonstrated. The estimation error can achieve exponential convergence by choosing appropriate design parameters. Lastly, the proposed estimator is applied to target tracking simulations and compared with state-of-the-art outlier-robust estimation methods, confirming the effectiveness and superiority of the proposed estimator.
Flexible light-stimulated artificial synapse based on detached (In,Ga)N thin film for neuromorphic computing
Authors: Zhang Qianyi , Hou Binbin , Zhang Jianya , Gu Xiushuo , Huang Yonglin , Pei Renjun , Zhao Yukun
Published: March 2024

Because of wide range of applications, the flexible artificial synapse is an indispensable part for next-generation neural morphology computing. In this work, we demonstrate a flexible synaptic device based on a lift-off (In,Ga)N thin film successfully. The synaptic device can mimic the learning, forgetting, and relearning functions of biological synapses at both flat and bent states. Furthermore, the synaptic device can simulate the transition from short-term memory to long-term memory successfully under different bending conditions. With the high flexibility, the excitatory post-synaptic current of the bent device only shows a slight decrease, leading to the high stability. Based on the experimental conductance for long-term potentiation and depression, the simulated three-layer neural network can achieve a high recognition rate up to 90.2%, indicating that the system comprising of flexible synaptic devices could have a strong learning-memory capability. Therefore, this work has a great potential for the development of wearable intelligence devices and flexible neuromorphic systems.
Stability for a system of the 2D incompressible magneto-micropolar fluid equations with partial mixed dissipation
Authors: Lin Hongxia , Liu Sen , Zhang Heng , Sun Qing
Published: March 2024

This paper focuses on the 2D incompressible anisotropic magneto-micropolar fluid equations with vertical dissipation, horizontal magnetic diffusion, and horizontal vortex viscosity. The goal is to investigate the stability of perturbations near a background magnetic field in the 2D magneto-micropolar fluid equations. Two main results are obtained. The first result is based on the linear system. Global existence for any large initial data and asymptotic linear stability are established. The second result explores stability for the nonlinear system. It is proven that if the initial data are sufficiently small, then the solution for some perturbations near a background magnetic field remains small. Additionally, the long-time behaviour of the solution is presented. The most challenging terms in the proof are the linear terms in the velocity equation and the micro-rotation equation that will grow with respect to time t. We are able to find some background fields to control the growth of the linear terms. Our results reveal that some background fields can stabilise electrically conducting fluids.
Schrödinger equation as a confluent Heun equation
Authors: Figueiredo Bartolomeu Donatila Bonorino
Published: March 2024

This article deals with two classes of quasi-exactly solvable (QES) trigonometric potentials for which the one-dimensional Schrödinger equation reduces to a confluent Heun equation (CHE) where the independent variable takes only finite values. Power series for the CHE are used to get polynomial and nonpolynomial eigenfunctions. Polynomials occur only for special sets of parameters and characterize the quasi-exact solvability. Nonpolynomial solutions occur for all admissible values of the parameters (even for values which give polynomials), and are bounded and convergent in the entire range of the independent variable. Moreover, throughout the article we examine other QES trigonometric and hyperbolic potentials. In all cases, for a polynomial solution there is a convergent nonpolynomial solution.
Investigation of illumination-dependent electrical and photodiode properties and conduction mechanism of the Al/p-Si contact with Schiff base compound (Pyr-Pic) interlayer
Authors: Oruç Pınar , Tezcan Ali Osman , Tuğluoğlu Nihat , Eymur Serkan
Published: March 2024

In this paper, an organic semiconductor, N-(2-((pyren-4-yl)methyleneamino)ethyl)-5- nitropyridin-2-amine (Pyr-Pic), was synthesized and was used as an interfacial organic layer to fabrication of Al/Pyr-Pic/p-Si/Al diode. The device’s characteristic parameters were determined under various operating situations using current–voltage (I-V) measurements. Thermionic emission theory (TE), the Cheung methodology, and Norde functions were the three methods used to determine the device’s electrical properties, such as the ideality factor, barrier height, and series resistance. Based on the I-V measurements conducted under illumination circumstances, the fabricated device exhibits photoresponse properties in the reverse bias region. An examination of the forward log(I)-log(V) plot of the Al/Pyr-Pic/p-Si diode indicated that ohmic conduction dominates carrier transport in the lower bias regions, while the space-charge-limited current (SCLC) governs transport in the medium bias regions, and the trap-charge limit current (TCLC) mechanism is responsible for transport in the higher bias regions. The current voltage mechanisms in the reverse bias area can be characterized by using Poole–Frenkel emission (PFE) and Schottky emission (SE) theories. The device exhibits photovoltaic characteristics when it is illuminated through an Al electrode. The typical photovoltaic parameters were estimated at room temperature and under illumination conditions.
Stretchable and recyclable In3SnBi/PU nanofiber composite as flexible sensors for human motion monitoring
Authors: Zhang Cong , Luo Yuze , Hu Xiaotao
Published: March 2024

Low-melting-point metal alloys (LMMAs) have great potential for application in soft sensors. However, the high cost limits the application of gallium-based liquid metals. In this paper, LMMA In3SnBi has been designed and prepared through a facile melt-mixing process followed by an ultrasonic dispersion technique. To overcome the fragility of low-melting-point alloys, flexible electrospun polyurethane (PU) matrix is used as structural support. When used as a sensor, the composite film exhibits a response time of 54.37 ms within a strain range of 15%. In addition, the composite film exhibits self-healing properties, and cracks generated by cyclic stretching can be repaired through a hot-pressing process. The current work presents a versatile fabrication strategy for LMMA-based flexible sensors, which holds great promise in the field of wearable electronic devices.
Preparation and luminescent properties of SiC nanoparticles by strong pulse discharge in organic liquid phase environment
Authors: Du Kai , Wei Ronghui , Li Gaojie
Published: March 2024

SiC nanoparticles with relatively uniform particle distribution were successfully prepared using a continuous strong pulse discharge method using hexamethyldisilane as the organic liquid phase environment. The samples were subjected to x-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), and Raman spectroscopy (Raman) tests. XRD and Raman data indicate that the particle composition is mainly composed of β-SiC. The electron microscope image illustrates particle sizes ranging from 5–20 nm and existing in an agglomerated state. Further photoluminescence (PL) results indicate that the annealing temperature under vacuum conditions has a significant positive effect on the luminescence intensity.
Digital light 3D printing of artificial bone with star-shaped polycaprolactone-based polyurethane acrylate
Authors: Yang Xinmeng , Qiu Shuiwei , Du Zhanhui , Xing Quansheng
Published: March 2024

Advanced medical materials and manufacturing technologies are highly in demand in artificial bones. Herein, a four-arm star-shaped polycaprolactone polyurethane acrylate (FPCLA) was designed and synthesized. The photosensitive character of FPCLA contributed to the rapid prototyping and personalized customization under digital light processing (DLP) 3D printing technology. The FPCLA was prepared by introducing unsaturated double bonds into polycaprolactone tetraethyl alcohol (PCLT). We characterized the physico-chemical properties of the material through FTIR, H-NMR, GPC, DSC and SEM. Cell behaviors on material were observed in vitro. In addition, we employed a DLP 3D printer to evaluate the feasibility of FPCLA to fabricate artificial bone model. The photocuring star polycaprolactone was confirmed in detail by detection method. SEM analyses demonstrated that FPCLA has good tenacity. The material can be used to fabricated artificial bone with a diameter of 3.02 mm at its narrowest by DLP 3D printing technology. The cell survival rates of CCK-8 and Live/Dead fluorescence staining experiments were both above 90%, which indicated safety and feasibility of such new-generation artificial bone made of synthetic polymers.
Plant pathogen infection risk and climate change in the Nordic and Baltic countries
Authors: Strandberg G , Andersson B , Berlin A
Published: March 2024

Climate change and global warming are already affecting food production, and the impact is predicted to intensify in the future. Previous studies have been based on global data and have provided general information about climate change effects on food production. Regional high-resolution data are, however, needed to evaluate the effect of future scenarios of climate change to support strategic and tactical planning to safeguard food production. Here, we provide results on the future potential distribution range of fungal plant pathogens in the Nordic and Baltic countries. This is done using regional climate model data at 12.5 km horizontal resolution. The temperature dependent infection risk and species richness are calculated using data for 80 plant pathogens. Within the region the studied pathogens will in most cases thrive more and be more abundant in a warmer climate; leading to a longer infection risk season and the introduction of new pathogens. This applies to all emissions scenarios, even though the effects are stronger with high emissions. Our results indicate that plant diseases will increase, and this will negatively affect crop production and food security.
Assessing urban sustainability in Saudi Arabia: an empirical evidence from Al-Medina Al-Munawwarah
Authors: Aldegheishem Abdulaziz
Published: March 2024

This study aims to assess the progress towards Sustainable Development Goal 11 (SDG 11) in Al-Madinah Al-Munawwarah, Saudi Arabia. The study also examines challenges that encounter SDG 11. To this end, six targets consisting of 40 indicators of SDG 11 have been adopted, relying on literature, international and national technical reports, and the personal perspectives of twelve experts. Overall progress towards SDG 11 has shown significant improvement considerably, with a middle level, particularly after Saudi Vision 2030 was approved. The findings show that three targets including affordable housing, sustainable transportation, and sustainable urbanization, have achieved average progress, and two targets, including air quality, and waste management, and access to green areas have been demonstrated low progress, as well as a single target, which is the preservation and protection of natural and cultural heritage, which has achieved optimal progress. The results highlight several challenges that hinder progress towards SDG 11, but the level of these challenges varies from one target to another target, ranging from moderate to major challenges. These challenges should be considered in continuing urban strategies and could be reduced by establishing resource-saving and innovative community urban renewal programs.
Intelligent passive infrared sensor based on learning vector quantization for human detection
Authors: Doan Tuan Kiet , Nguyen Thi Nhat Linh , Hoang Ngoc Chau , Le Minh Thuy , Nguyen Quoc Cuong
Published: March 2024

Passive Infrared (PIR) Sensors have been used widely in human detection indoors nowadays due to their low cost and range. However, traditional PIR sensors may get fault detection, especially when the human is in a static pose. To overcome this limitation, a Machine Learning (ML)-based PIR sensor is proposed in this work for detection accuracy enhancement. The Learning Vector Quantization (LVQ) approach is used to be easily implemented in the embedded device (which requires a low computational complexity) to provide a real-time response. The experimental scenarios to create the datasets are conducted in two distinct locations for training and testing purposes. In each location, participants performed a series of different activities and left the room unoccupied. Data is collected via a PIR sensor and then wireless transmitted to a computer for training and testing. In the test set, the presence of humans with an accuracy of 89.25 % is obtained using the proposed LVQ algorithm prediction. Finally, the LVQ is implemented on an embedded device based on Xtensa Dual-Core 32-bit LX6 CPU to form an intelligent PIR (iPIR)-based LVQ sensor, this novel iPIR sensor then is evaluated and tested with a remarkable result.