Surface Plasmon Resonance (SPR) is an attracting property of certain transition metals when they are synthesized in nano-range giving rise to promising optical applications. However, most SPR and associated applications are limited to the noble metal nanoparticles, which limits their potential due to high production cost. We report surface plasmon resonance in copper-copper oxide core-shell quantum dots synthesized via chemical route studied by using UV-Visible spectrophotometry. Tuning of the plasmonic resonance with respect to the particle diameter is achieved by an inexpensive all chemical route. Photoluminescence measurements also support the data. This size reduction leads to remarkable changes in its optical response as compared to the bulk metal. The results point towards applications of these materials in tunable SPR based biosensors.

An antenna sensor is proposed to execute dual functions of antenna and sensor in the wireless sensor system, in order to reduce data loss and to increase transmission rate by omitting a certain interface. The as-made sensor was test at a center frequency of 46 MHz for measuring human finger postures using principle of dipole antenna. The antenna sensor was attached on a wearable glove. The results showed that the motion sensor can accurately identify finger angles at 0°, 20°, 40°, 60° and 80°.

People with Down syndrome present cognitive difficulties that affect their reading skills. In this study we present results about the use of gestural interaction with Kinect sensor to improve the reading skills of students with Down syndrome. Following a case of study method for small samples with disabilities, measuring different variables related to reading skills in an experimental group and in a control group. We found improvements in the visual association, visual comprehension, sequential memory, and visual integration after this stimulation in the experimental group compared to the control group. Also, we found that the number of error and delay time of interaction decrease between sessions in the experimental group.

The subject of the research is to Development of laser ablation method for Fabrication of surface acoustic wave sensors on quartz wafer, the target of the GQW – is to design Acoustic wave sensor by using laser ablation method. By using the surface acoustic wave theory to sense by the signal and using this physical phenomenon, We will design the sensor which transduce an input electrical signal into a mechanical wave which unlike an electrical signal, can be easily influenced by physical phenomena. The device then transducers this wave back into an electrical signal on the secondary terminal of the sensor. Changes in amplitude, phase, frequency, or time-delay between the input and output electrical signals can be used to measure the presence of the desired Our work in this part, especially the practical part like temperature, vibration ,etc. we design a combs on the waver of quartz to make like an electrode primary electrode & secondary electrode by putting coats of cuppers & vanadium on the waver and then using the fiber optic laser regime to design this combs to can able transfer the signal by ablation the most important here to use the regime of fiber optic laser then we using this sensor in any electronic circuit How we will select the suitable kind of laser to design, this is the most important part, and what it will be the diameter of that combs of secondary and primary , how much the value of the wave length to select the micro distant combs to avoid any inductance and interference for transferred signal , also take the benefit of using MEMS theory in our project.

In this work, two embroidered textile moisture sensors are characterized with three different conductive yarns. The sensors are based on a capacitive interdigitated structure embroidered on a cotton substrate with an embroidered conductor yarn. The performance comparison of 3 different type of conductive yarns has been addressed. In order to evaluate the sensor sensitivity, the impedance of the sensor has been measured by means of a LCR meter from 20 Hz to 20 kHz on a climatic chamber with a sweep of the relative humidity from 30% to 65% at 20 ºC. The experimental results show a clear and controllable dependence of the sensor impedance with the relative humidity and the used conductor yarns. This dependence points out the optimum conductive yarn to be used to develop wearable applications for moisture measurement.

Detection of formaldehyde is very important in terms of life protection, as it can cause serious injury to eyes, skin, mouth and gastrointestinal function if indirectly inhaled and hence researchers are putting effort in developing novel and sensitive device. In this work, we have fabricated an electro-chemical sensor in the form of a field effect transistor (FET) to detect formaldehyde over wide range (10 nM to 1 mM). For this, ZnO nanosheets (NS) were first synthesized by hydrothermal method with in-situ deposition on cleaned SiO₂ coated Si (100) substrate. The synthesized materials were characterized for morphology and purity and surface area (31.718 m²/g). The developed device was tested for formaldehyde detection at room temperature that resulted in a linear response with concentration (96%), sensitivity value of 0.27 mA/M/cm² and a low detection limit of 210 nM, and a high 0.93194 return.

The deformation of underground gateroads tends to be asymmetric and complex. Traditional instrumentation fails to accurately and conveniently monitor the full cross-sectional deformation of underground gateroads. Here, a full cross-sectional laser scanner was developed together with a visualization software package. The developed system used polar coordinate measuring method and the full cross-sectional measurement was realized by 360° rotation of laser sensor driven by an electrical motor. Later on, the potential impact of gateroad wall flatness, roughness and geometrical profile as well as coal dust environment on the performance of the developed laser scanner were evaluated. The studies show that a high-level flatness is favorable in application of the developed full cross-sectional deformation monitoring system. For a smooth surface of gateroad, the sensor cannot receive reflected light when the incidence angle of laser beam is large, causing data loss. Conversely, the roughness surface shows its priority as the diffuse reflection light can be received by the sensor. With regards to the coal dust in measurement environment, the fine particles of floating coal dust in the air can lead to the loss of measurement data to some certain due to scattering of laser beam.

This paper presents a piezoresistive pressure sensor with a shield layer for improved stability. Compared with the conventional piezoresistive pressure sensors, p-type piezoresistors are covered by an n-type shield layer, which is formed by ion implantation. The proposed pressure sensors have been successfully fabricated by bulk micromachining techniques. The impact of electrical field on piezoresistors is studied by simulation. The temperature drift of the pressure sensor has been investigated by both simulation and experimental measurement. Characteristics of developed pressure sensors are tested from -40 C to 125 C. A sensitivity of 0.022 mV/V/KPa and a maximum non-linearity of 0.085% FS are measured for the fabricated sensor in a pressure range of 1 MPa. The temperature coefficients of resistance of shielded piezoresistors are found to be smaller than those of un-shielded ones. It is demonstrated that the shield layer is able to reduce the drift caused by electrical field and ambient temperature variation.

Background/Objectives: Post traumatic growth (PTG) has the potential to draw positive consequences from trauma. Hence, the interest of finding ways to promote PTG. Research has identified an attentional bias towards positive resilience-related words (e.g., “persistence”, “purpose”) in university students who report high PTG after experiencing adversities. Although people can respond to these experiences by showing low PTG, this bias seems to help with their struggle by making purposeful contents more accessible. Therefore, boosting attentional bias towards positive resilience-related words could help people with low PTG. Methods: In this study, the participants were thirty-six university students who had experienced bullying before entering university. Using a Stroop emotional task, they identified the color of resilience and neutral words, either positive or negative, before and after being submitted to transcranial direct current stimulation. Stimulation was targeted at the right temporal area involved in intentionality processing. Results: In the anodal condition, the results support a stimulation effect on the resilience attentional bias that could benefit participants with low PTG. A significant moderation of approach motivation for this effect was also found. Specifically, only when participants had medium or high approach motivation, did stimulation boost the attentional bias in students with low PTG. Conclusions: These results support that tDCS stimulation in this brain area is effective in enhancing resilience attentional bias in low PTG students. However, for this effect to occur it is necessary to have approach motivation, which is motivation related to goals.

This paper presents a comprehensive review of the transformative impact of 3D printing technology on smart cities. As cities face rapid urbanization, resource shortages, and environmental degradation, innovative solutions such as additive manufacturing (AM) offer potential pathways for sustainable urban development. By synthesizing 66 publications from 2015 to 2024, the study examines how 3D printing improves urban infrastructure, enhances sustainability, and fosters community engagement in city planning. Key benefits of 3D printing include reducing construction time and material waste, lowering costs, and enabling the creation of scalable, affordable housing solutions. The paper also addresses emerging areas such as the integration of 3D printing with digital twins (DT), machine learning (ML), and AI to optimize urban infrastructure and predictive maintenance. It highlights the use of smart materials and soft robotics for structural health monitoring (SHM), and repairs. Despite the promising advancements, challenges remain in terms of cost, scalability, and the need for interdisciplinary collaboration among engineers, urban planners, and policymakers. The findings suggest a roadmap for future research and practical applications of 3D printing in smart cities, contributing to the ongoing discourse on sustainable and technologically advanced urban development.

This article explores the use of vegetation indices (VIs) in remote sensing for estimating standing wood volume and volume increments in forest ecosystems. The study investigates the correlations between various VIs and wood volume across different forest stand age classes, focusing on the effectiveness of commonly used indices such as Squared Ratio Simple Red Edge (SQSR) and Red-Edge Ratio Vegetation Index (RERVI), as well as other typical indicators including Enhanced Vegetation Index (EVI), Red-Edge EVI (RE-EVI), and Green Normalized Difference Vegetation Index (GNDVI). Additionally, the analysis considers a pair of indicators based on blue and near-infrared channels, resembling the Blue-normalized Difference Vegetation Index (BNDVI). The research reveals that quadratic and power-law models of SQSR and RERVI demonstrate higher correlations (>0.40) with wood volume, especially in younger forest stands (>0.80). Furthermore, the study highlights the potential of NDII and NDWI indices for estimating volume increments in certain classes of forest stands (0.70). However, correlations vary across different forest stand classes, suggesting the need for further investigation into the reliability of VIs for monitoring wood volume and volume increments in forest ecosystems. Overall, this research contributes to the understanding of the applicability of VIs in remote sensing for forest management and ecosystem monitoring.

COP21 represented a starting point for several nations to develop and implement energy transition strategies to face and mitigate climate change, making the electrical power sector crucial in achieving the established goals and commitments. This research presents a 7-step methodology to improve decision-making in energy transition strategies for the power sector by integrating an economic dispatch optimization model based on linear programming to determine vulnerable aspects of power generation and transmission in strategic planning scenarios that could jeopardize the country’s energy transition. The application of the methodology is implemented under the open-source platform Python Optimization Modelling (PYOMO) and illustrated through a case study of the Mexican Electrical Power System (SEN) during the year 2025. The case study shows that the reserve margin fluctuated due to the variable renewable energy installed despite having a vast installed capacity to supply the country’s total demand. In addition, the results showed that most of the transmission lines had a congestion frequency higher than 90% of their capacity during most of the year. Two regions were identified as the best options for reducing greenhouse gas emissions by installing new power plants. Finally, most technologies reflected an under-generation, suggesting a high dependence on some fuels to supply the Mexican demand. The model’s programming is freely available in GitHub.

Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative ability to capture mobile targets, is becoming a hot research topic gradually. Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games. We define an Observation-constrained MVP (OMVP) problem in this paper and propose a Transformer-based Time and Team Reinforcement Learning scheme (T3OMVP) to address the problem. First, a new multi-vehicle pursuit model is constructed based on decentralized partially observed Markov decision processes (Dec-POMDP) to instantiate this problem. Second, by introducing and modifying the transformer-based observation sequence, QMIX is redefined to adapt to the complicated road structure, restricted moving spaces and constrained observations, so as to control vehicles to pursue the target combining the vehicle’s observations. Third, a multi-intersection urban environment is built to verify the proposed scheme. Extensive experimental results demonstrate that the proposed T3OMVP scheme achieves significant improvements relative to state-of-the-art QMIX approaches by 9.66%~106.25%. Code is available at https://github.com/pipihaiziguai/T3OMVP.

Abstract: Thermal desalination is yet a reliable technology in the treatment of brackish water and seawater; however, its demanding high energy requirements have lagged it compared to other non-thermal technologies such as reverse osmosis. This review provides an outline of the development and trends of the three most commercially used thermal or phase change technologies worldwide: Multi Effect Distillation (MED), Multi Stage Flash (MSF), and Vapor Compression Distillation (VCD). First, state of water stress suffered by regions with little fresh water availability and existing desalination technologies that could become an alternative solution are shown. The most recent studies published for each commercial thermal technology are presented, focusing on optimizing the desalination process, improving efficiencies, and reducing energy demands. Then, an overview of the use of renewable energy and its potential for integration into both commercial and non-commercial desalination systems is shown. Finally, research trends and their orientation towards hybridization of technologies and use of renewable energies as a relevant alternative to the current problems of brackish water desalination are discussed. This reflective and updated review will help researchers to have a detailed state of the art of the subject and to have a starting point for their research, since current advances and trends on thermal desalination are shown.

In recent years, the integration of sensors in agriculture has emerged as a pivotal catalyst for transforming conventional farming practices into precision agriculture. Sensor application is impacting the everyday objects that enhance human life. In this special issue, the main objective was to address recent advantages of sensor application in agriculture covering a wide range of topics in this field. This provides overview of diverse application of sensors in agriculture, highlighting their role in enhancing productivity, sustainability, and resource management. sensors are instrumental in monitoring various agriculture parameter such as soil condition, weather, crop health and livestock wellbeing. By collecting real-time data and providing valuable insight, sensors enable precision farming, optimize resource allocation, and minimize environmental impacts. we have to explore the key sensor types used in agriculture, their applications, benefits, and future prospects, emphasizing their crucial roles in the modernization of agriculture and global quest for food security and crop production.

To study and monitor the adverse health consequences from using electronic cigarettes, a user’s puff topography, which are quantification parameters of the user’s vaping habits, plays a central role. In this work, we introduce a topography sensor to measure the mass of total particulate matter generated in every puff and to estimate the nicotine yield. The sensor is compact and low-cost, and is integrated into the electronic cigarette device to promptly and conveniently monitor the user’s daily puff topography. The topography sensor is comprised of a photometric sensor and a pressure sensor. The photometric sensor measures the mass concentration of the aerosol, based on scattering of near-infrared light from airborne particles, while the pressure sensor measures the flow rate. The topography sensor was experimented under various conditions with a wide range of atomizer power, puff duration, and inhalation pressure. The sensor’s accuracy was validated by comparing the sensor’s readings with reference measurements, and the results matched closely with the trends reported by existing studies on electronic cigarettes. An example application of tracking a user’s puff topography was also demonstrated. Our topography sensor holds great promise in mitigating health risks of vaping, and in promoting quality control of electronic cigarette products.

A planar microwave sensor devoted to the detection of humidity in underwear and clothes in general is proposed. The ultimate goal of the sensor is to detect the presence of liquids in fabrics, of interest to aid patients that suffer from certain pathologies, such as hyperhidrosis and enuresis. The main target in the design of the sensor, considering the envisaged application, is simplicity. Thus, the sensor operates at a single frequency, and the working principle is the variation in the mag-nitude of the transmission coefficient of a matched line loaded with an open-ended quar-ter-wavelength sensing stub resonator. The stub, which must be in contact with the so-called fabric under test (FUT), generates a notch in the transmission coefficient with a resonance frequency that depends on the humidity level of the fabric. By designing the stub with a moderately high quality factor, the variation in the resonance frequency causes a significant change in the magnitude level at the operating frequency, the resonance frequency when the sensing stub is loaded with the dry fabric, and the presence of liquid can be detected by means of an amplitude detector. A prototype device is proposed and experimentally validated.

Five years ago, the concept of addressed fiber Bragg structures (AFBS) was proposed, which simultaneously perform the functions of a two-frequency radiation shaper, the difference frequency of which is the AFBS address, and a sensitive element, since the value of the difference frequency is invariant to measured physical fields, and the set of difference frequencies, moreover, is orthogonal in the array of such sensors, enabling their address multiplexing. In this article, we provide an overview of the theory and technology of AFBS, including the structures with three or more spectral components with various combinations of difference frequencies, symmetrical and asymmetric, performing the functions of the address and converting information signals to the low-frequency range at the same time, along with other functions. The subjects of interrogation of these structures, their fabrication and calibration are discussed as well. We also consider a wide range of applications in which AFBS can be used, covering such areas as oil and gas production, power engineering, transport, medicine, etc. In addition, the prospects of AFBS further development are proposed.

This paper presents an in-depth contextualized tutorial on Agricultural IoT (Agri-IoT), covering the fundamental concepts, assessment of routing architectures and protocols, and performance optimization techniques via systematic survey and synthesis of related literature. The negative impacts of climate change and the increasing global population on food security and unemployment threats have motivated the adoption of the wireless sensor network (WSN)-based Agri-IoT as an indispensable underlying technology in precision agriculture and greenhouses to improve food production capacities and quality. However, most related Agri-IoT testbed solutions have failed to achieve their performance expectations due to the lack of an in-depth and contextualized reference tutorial that provides a holistic overview of communication technologies, routing architectures, and performance optimization modalities based on users’ expectations. Thus, although IoT applications are founded on a common idea, each use case (e.g., Agri-IoT) varies based on specific performance and users expectations as well as its technological, architectural, and deployment requirements. Likewise, the agricultural setting is a unique and hostile area where conventional IoT technologies do not apply, hence the need for this tutorial. Consequently, this tutorial addresses these via the following contributions: (1) a systematic overview of the fundamental concepts, technologies, and architectural standards of WSN-based Agri-IoT, (2) an evaluation of the technical design requirements of a robust, location-independent, and affordable Agri-IoT, (3) a comprehensive survey of the benchmarking fault tolerance techniques, communication standards, routing and medium access control (MAC) protocols, and WSN-based Agri-IoT testbed solutions, and (4) an in-depth case study on how to design a self-healing, energy-efficient, affordable, adaptive, stable, autonomous, and cluster-based WSN-specific Agri-IoT from a proposed taxonomy of multi-objective optimization (MOO) metrics that can guarantee an optimized network performance. Furthermore, this tutorial established new taxonomies of faults, architectural layers, and MOO metrics for cluster-based Agri-IoT (CA-IoT) networks and a 3-tier objective framework with remedial measures for designing an efficient associated supervisory protocol for cluster-based Agri-IoT networks.

Abstract: The United Nations Global Sustainability Agenda 2030 introduces a transformative framework through its Sustainable Development Goals (SDGs), which are designed to achieve equitable, prosperous, and sustainable development globally. Unlike the incremental Millennium Development Goals (MDGs), the SDGs aim for systemic changes across economic, social, and environmental dimensions, ensuring that no one is left behind. This research paper provides a comprehensive examination of Pakistan's progress towards achieving the SDGs under the UN Global Sustainability Agenda 2030. Specifically, it includes an analysis of the prioritization of SDGs in Pakistan, highlights federal and institutional initiatives aimed at promoting sustainable development, and explores the role of the China-Pakistan Economic Corridor (CPEC) in advancing these goals. Additionally, the paper critically analyzes the SDGs in Pakistan’s annual status report, identifies the key challenges faced by the nation in implementing these goals, and offers recommendations for enhancing the effectiveness of SDG implementation. Overall, the focus is primarily on the detailed analysis of the annual status report, the challenges encountered, and strategic recommendations for achieving excellence in SDGs in Pakistan. Purpose: The objectives of this research are to assess how effectively Pakistan has prioritized the Sustainable Development Goals (SDGs) within its national framework, identifying the key areas of focus. This includes exploring the role of key federal institutions and initiatives, such as the China-Pakistan Economic Corridor (CPEC), in advancing the country’s SDGs. Additionally, the study aims to analyze the development and framework of the 17 SDGs in Pakistan by evaluating the country's status report on SDGs implementation. Furthermore, the research will examine the challenges Pakistan faces in achieving significant outcomes on the SDGs and provide actionable recommendations for enhancing the effectiveness and success of these initiatives.

Bayesian networks have emerged as a powerful tool for modelling complex probabilistic relationships in uncertain environments. The process of inducing Bayesian networks involves learning both the structure of the network and the parameters of the associated probability distributions. This paper provides a detailed examination of the two main approaches to Bayesian network induction: structure learning and parameter estimation, highlighting the use of both constraint-based and score-based techniques. Furthermore, the role of distribution-based methods in the estimation and inference of probabilistic models is explored. These methods, including Maximum Likelihood Estimation, Bayesian Estimation, and Monte Carlo techniques, offer robust ways to handle parameter uncertainty and facilitate efficient inference in large, complex models. This comprehensive view aims to bridge the gap between the theoretical foundations and practical applications of these methods, providing insights for researchers and practitioners in the field of machine learning and probabilistic reasoning.

This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies. To improve image quality without altering or introducing new biological information, we apply Zero-Shot Deconvolution Networks (ZS-DeconvNet), a deep learning-based method that enhances resolution in an unsupervised manner. ZS-DeconvNet significantly refines image clarity and sharpness across multiple microscopy modalities without requiring large, labeled datasets, or introducing artifacts. By combining the strengths of multi-modal light sheet microscopy and ZS-DeconvNet, we achieve improved visualization of subcellular structures, offering clearer and more detailed representations of existing data. This approach holds significant potential for advancing high-resolution imaging in biomedical research and other related fields.

Aim: To evaluate the impact of applying alternative diagnostic criteria for gestational diabetes mellitus (GDM) during the COVID-19 pandemic on GDM prevalence, obstetrical and perinatal outcomes, and costs, as compared to the standard diagnostic method. Methods: A cohort of pregnant individuals undergoing GMD screening with the alternative GDM method, which uses plasma glucose (fasting or non-fasting) and HbA1c, was compared with a cohort of pregnant individuals undergoing the standard GDM screening method. Both cohorts were obtained from 6 hospitals across Catalonia, Spain, from April 2020 to April 2022. The primary outcome was large for gestational age rate at birth. Secondary outcomes were composite adverse outcomes including pregnancy complications, delivery complications, and neonatal complications. Cost differences between screening methods were also evaluated. A similar analysis was performed in the subgroup diagnosed with GDM. Results: Data were collected from 1,543 pregnant individuals in the standard screening group and 2,197 in the alternative screening group. The standard screening group had a higher GDM diagnostic rate than the alternative screening group (10.8% vs 6.9%, respectively; p<0.0001). The primary outcome (large for gestational age rate) was similar between groups: 200/1543 (13.0%) vs 303/2197 (13.8%). The adjusted OR for this outcome was 1.74 (95% CI: 0.74-4.10). An adjusted analysis showed no differences between groups in composite adverse outcomes for pregnancy complications (OR: 1.11; 95% CI: 0.91-1.36), delivery complications (OR: 0.95; 95% CI: 0.75-1.19), and neonatal complications (OR: 1.28; 95% CI: 0.94-1.75). Among individuals diagnosed with GDM, the large for gestational age rate was similar between groups: 13/166 (7.8 %) vs 15/151 (9.9 %). The OR adjusted for this outcome was 1.24 (95% CI: 0.51-3.09). An adjusted analysis showed no differences in composite adverse outcomes for pregnancy complications (OR: 1.57; 95% CI: 0.84-2.98), delivery complications (OR: 1.21; 95% CI: 0.63-2.35), and neonatal complications (OR: 1.35; 95% CI: 0.61-3.04). The mean cost (which included expenses for consumables, equipment and personnel) of the alternative screening method was 46.0 euros (22.3 SD), as compared to 85.6 euros (67.5 SD) for the standard screening method. Conclusions: In this Spanish population during the COVID-19 pandemic, GDM prevalence was lower in the alternative screening group than in the standard screening group. After adjusting for GDM risk factors, outcomes related to obstetrics, delivery, and neonatal complications were comparable between both groups. Finally, the alternative screening method was cheaper than the standard screening method.

The multi-UAV target search problem is crucial in the field of autonomous Unmanned Aerial Vehicle (UAV) decision-making. The algorithm design of Multi-agent Reinforcement Learning (MARL) methods has become integral to research on multi-UAV target search, due to its adaptability to the rapid online decision-making required by UAVs in complex, uncertain environments. In non-cooperative target search scenarios, targets may have the ability to escape, complicating UAVs' search efforts and hindering the convergence of the MARL algorithm training. This paper investigates the multi-UAV target search problem in scenarios involving static obstacles and dynamic escape targets, modeling the problem within the framework of Decentralized Partially Observable Markov Decision Process. Based on this model, a Spatio-Temporal Efficient Exploration network and a Global Convolutional Local Ascent mechanism are proposed. Subsequently,we introduce a Multi-UAV Escape Target Search Algorithm Based on MAPPO(ETS-MAPPO) for addressing the escape target search difficulty problem. Simulation results demonstrate that the ETS-MAPPO algorithm outperforms five classical MARL algorithms in terms of the number of target searches,the area coverage rate, and other metrics.

Traditional 3D reconstruction models have consistently encountered a challenge: attaining high recall of object edges while preserving precision. In this paper, we introduce a post-processing method Multi-Head Attention Refiner (MA-R) aimed at tackling this challenge by integrating a multi-head attention mechanism within the U-Net style refiner module. The 3D reconstruction model applying this method demonstrates excellence in parsing intricate image details, resulting in significant enhancement of boundary predictions and increased recall rates. In our experiments, our method significantly enhances the reconstruction performance of Pix2Vox++ when multiple images are utilized as input. Specifically, it achieves an enhanced Intersection Over Union score of approximately 1.1% compared to the original Pix2Vox++ model when 16-view images are employed.

Pepper leaf segmentation plays a crucial role in monitoring pepper leaf diseases in various backgrounds and ensuring the healthy growth of peppers. However, existing transformer-based segmentation methods suffer from computational inefficiency, excessive parameterization, and limited utilization of edge information. To tackle these challenges, we propose an adaptive multi-scale MLP framework, named AMS-MLP, which combines the multi-path aggregation module (MPAM) and the multi-scale context relation mask module (MCRD) to refine the object boundaries in pepper leaf segmentation. AMS-MLP consists of an encoder-based network, an adaptive multi-scale MLP (AM-MLP) module, and a decoder network. In the encoder network, the MPAM module effectively fuses five-scale features to generate a single-channel mask, improving the accuracy of pepper leaf boundary extraction. The AM-MLP module divides the input features into two branches: the global multi-scale MLP branch captures long-range dependencies between image information, while the local multi-scale MLP branch focuses on extracting local feature maps. Adaptive attention mechanism is designed to dynamically adjust the weights of global and local features. The decoder network incorporates the MCRD module into the convolutional layer, enhancing the extraction of boundary features. To verify the performance of the proposed method, we conducted extensive experiments on three pepper leaf datasets with different backgrounds. The results demonstrate mIoU scores of 97.39%, 96.91%, and 97.91%, as well as F1 scores of 98.29%, 97.86%, and 98.51%, respectively. Comparative analysis with U-Net and state-of-the-art models reveals that the proposed method dramatically improves the accuracy and efficiency of pepper leaf image segmentation.

Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

The hybrid bond graph has been studied in depth for scalar bond graphs, but how does this translate to the multi-bond graph? Here, the controlled junction – used to model structural switching such as contact – is extended to the multi-bond case. This is a simple process, assuming that all bonds switch simultaneously (which makes physical sense). A controlled 0-junction is applied to multi-bond graph of a car, which can lose contact with the ground in cornering. Dynamic causality features, but this can be accommodated using an equational submodel in 20-Sim (in a manner similar to that used with scalar bond graphs). The junction is proposed for subsequent work to develop a validated multi-body dynamics car model in cornering.

The framework of product life cycle (PLC) cost analysis is one of the most important evaluation tools for a contemporary high-tech company in an increasingly competitive market environment. The PLC-purchasing strategy provides the framework for a procurement plan and examines the sourcing strategy of a firm. The marketing literature emphasizes that ongoing technological change and shortened life cycles are important elements in commercial organizations. From a strategic viewpoint, the vendor has an important position between supplier, buyer and manufacturer. The buyer seeks to procure the products from a set of vendors to take advantage of economies of scale and to exploit opportunities for strategic relationships. However, previous studies have seldom considered vendor selection (VS) based on PLC cost (VSPLCC) analysis. The purpose of this paper is to solve the VSPLCC problems considering the situation of a single-buyer-multiple-supplier. For this issue, a new VSPLCC procurement model and solution procedure are derived by this paper to minimize net cost, rejection rate, late delivery and PLC cost subject to vendor capacities and budget constraints. Moreover, a real case in Taiwan is provided to show how to solve the VSPLCC procurement problem.

In this paper, a new approach to the sliding-mode control of single-phase inverters under linear and non-linear loads is introduced. The main idea behind this approach is to utilize a non-linear, flexible and multi-slope function in controller structure. This non-linear function makes the controller possible to control the inverter by a non-linear multi-slope sliding surface. In general, this sliding surface has two parts with different slopes in each part and the flexibility of the sliding surface makes the multi-slope sliding-mode controller (MSSMC) possible to reduce the total harmonic distortion, to improve the tracking accuracy, and to prevent overshoots leading to undesirable transient-states in output voltage which are occurred when the load current sharply rises. In order to improve the tracking accuracy and to reduce the steady-state error, an integral term of the multi-slope function is also added to the sliding surface. The improved performance of the proposed controller is confirmed by simulations and finally, the results of the proposed approach are compared with a conventional SMC and a SRFPI controller.

Antecedents. EFs (EFs) are the basis for establishing a goal and working towards that goal by coordinating thoughts and actions. EFs are fundamental to several aspects of daily life, specifically for academic performance. Aim. To analyze and compare the development of EFs in the transition period between the first and second cycles of Early Childhood Education. Methodology. Non-experimental methodology, ex post facto design, descriptive, cross-sectional evolution study. Participants. Preschoolers of different educational levels (first and second cycle of Infant Education). In this study the participants have been evaluated by different informants: 54.42% by parents and 45.58% by teachers. In relation to gender, 52.65% were male and 47.35% were female. In relation to age, 37.54% had a range of 2-3 years and 62.46% had a range of 4-5 years. Measurement. Instrument development of EFs were evaluated using BRIEF-P by key informants. Results. Preschoolers in the first cycle show significantly higher scores than preschoolers in the second cycle in BRIEP-P. Conclusions. The development of EF is key in these first key moments, having a special impact on later development and academic performance. It is necessary to work on EFs from the first cycle of Early Childhood Education, considering the evolutionary development of EFs.

Growth hormone therapy (GHT) can improve growth velocity and final height but can also accelerated the process of bone growth, which is related to structural bone modelling, both in formation and resorption. The study evaluated the capacity of bone turnover markers to predict early growth response to one year GHT in short stature children born as small for gestational age (SGA). 25 prepubertal SGA children were included. We have estimated P1NP (N-terminal procollagen type 1), CTX (C-terminal telopeptide of collagen type I), P3NP (N-terminal procollagen type III), NT-pro-CNP (amino-terminal C-type natriuretic peptide) and Ca-P metabolism using standard ECLIA, IRA and ELISA methods. A statistically significant increase in bone resorption markers (Ctx ) was found both at 6 and 12 months. P1NP bone markers increased at 6 months and after 12 months of therapy. The P3NP marker of collagen synthesis also increased after 12 months of therapy. We obtained significant increases in phosphorus levels at 6 and 12 months and similarly ALP. We have found a significant correlation between height (cm) and Ctx after 6 -12 months as well as P1NP / height (SD) after 12 months. Calcium levels significantly correlated with height (SD) after 12 months. We find a strong reaction of bone resorption and bone formation markers during growth hormone therapy that may determine their selection as prediction of GH treatment outcome in SGA children. However, the issue requires further research.

Polysulfides are endogenously produced in mammals and generally associated with protective functions. Our aim was to investigate the effect of dimethyl trisulfide (DMTS) in a mouse model of acute stress. DMTS activates transient receptor potential ankyrin 1 (TRPA1) channels and leads to neuropeptide release, potentially that of substance P (SP). We hypothesize that DMTS might inhibit the degrading enzymes of endocannabinoids, so this system was also investigated as another possible pathway for mediating the effects of DMTS. Trpa1 gene wild type (WT) and knock out (KO) mice were used to confirm the role of the TRPA1 ion channel in mediating the effects of DMTS. C57BL/6J, NK1 gene KO, and Tac1 gene KO mice were used to evaluate the effect of DMTS on the release and expression of SP. Some C57BL/6J animals were treated with AM251, an inhibitor of the cannabinoid CB1 receptor, to elucidate the role of the endocannabinoid system in these processes. Open field test (OFT) and forced swim test (FST) were performed in each mouse strain. Tail suspension test (TST) was performed in TRPA1 WT and KO animals. C-FOS immunohistochemistry was carried out on TRPA1 WT animals. DMTS treatment increased the number of highly active periods and decreased immobility time in the FST in WT animals, but had no effect on Trpa1 KO mice. DMTS administration induced neuronal activation in stress-related brain areas such as the locus coeruleus, dorsal raphe nucleus, lateral septum, paraventricular nucleus of the thalamus, and paraventricular nucleus of the hypothalamus. DMTS may have a potential role in the regulation of stress-related processes, and the TRPA1 ion channel may also be involved in mediating the effects of DMTS. DMTS can be an ideal candidate for further study as a potential remedy in stress-related disorders.

p-Toluenesulfonic acid is commercially available, non-toxic, cheap organic compound with robust stability. p-Toluenesulfonic acid is solid, non-corrosive and a strong organic acid that can be handled easily. Due to this p-toluenesulfonic acid could be the better replacement of other conventional acids like nitric acid, sulfuric acid, perchloric acid and AlCl3 in organic transformations. Moreover, p-toluenesulfonic acid could be used synergistically with other mode of catalysis like transition metal catalysis and photocatalysis. In recent years, the use of this solid organic acid in organic and polymer synthesis has grown tremendously. In this review, we present an overview of p-toluenesulfonic acid promoted various organic reactions with particular emphasis to the more recently reported methods. In some cases, we also detailed the understanding of the reactions by providing the reaction mechanism. Despite these advances, there are still many opportunities for the development of new reactions using PTSA. Industry and academia have become more conscious to adopt sustainable chemical processes using safer reagents and solvents and the use of PTSA in this context is highly significant. This review will inspire researchers for further reaction development employing p-toluenesulfonic acid.

Sechium compositum (Cucurbitaceae) is a wild species that is distributed in the Soconusco region, Chiapas, Mexico, and the border with Guatemala. This species has an intangible biochemical value resulting from the pharmacological relevance of its secondary metabolites. However, as a consequence of the lack of knowledge about its importance, it is being displaced from its habitat at an accelerated rate, incurring the risk of genetic loss. Therefore, an in vitro culture protocol with two experimental phases was evaluated to propagate, conserve, and regenerate this species. The first phases considered the multiplication of seedlings, adding seven concentrations (0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2 mg mL-1) of 6-benzylaminopurine (BA) and thidiazuron (TDZ) and evaluating the number of buds, shoots, and seedling height. The best multiplication response was recorded with 0.1, 0.2, 0.4, and 1.0 mg L-1 of BA and 0.1 mg L-1 of TDZ, as well as the MS base culture medium. With 0.1 mg L-1 of BA, 52-mm height, 1.36 shoots, and 9.22 buds were obtained, surpassing the MS control (MS culture medium alone); likewise, twice more roots (80%) and a 14% reduction in the bud structure were packed in 50 repetitions. The second phase consisted of inducing callus formation from stem and leaf explants through the addition of 0.5, 1.0, and 2.0 mg L-1 of TDZ and 2,4-Dichlorophenoxyacetic acid (2,4-D) to the medium. The results demonstrated that the induction of callogenesis in S. compositum has a better start from the stem in a medium with 2.0 mg L-1 of 2,4-D, whose value was 4.89 —equivalent to 97.8% callus formation around the explant. The addition of 500 mg L-1 of polyvinylpyrrolidone (PVP) is also suggested to reduce oxidation. This protocol represents a significant advance in the conservation, multiplication, and callogenesis of S. compositum and contributes to its rescue and revaluation in the face of the danger of extinction.

In this study, we investigate the existence of positive solutions within a system of Riemann-Liouville fractional differential equations that incorporate the (r1,r2,r3)-Laplacian operator while being subject to three-point boundary conditions. These equations incorporate various fractional derivatives and are influenced by parameters represented as (ψ1,ψ2,ψ3). Our approach involves employing techniques such as cone expansion and compression of the functional type, in conjunction with the Leggett-Williams fixed point theorem, to establish the existence of positive solutions. To emphasize the practical significance of our findings in the realm of fractional differential equations, we provide two illustrative examples.

In recent years, numerous approaches have been developed to comprehend the molecular altera-tions underlying thyroid cancer (TC) oncogenesis and explore novel therapeutic strategies for TC. It is now well established that the neurokinin-1 receptor (NK-1R) is overexpressed in cancer cells and that NK-1R is essential for viability of cancer cells. The binding of substance P (SP) to NK-1R in neoplastic cells plays a pivotal role in cancer progression by promoting neoplastic cell growth, protecting tumour cells from apoptosis, triggering invasion and metastasis through enhanced migration of cancer cells, and stimulating endothelial cell proliferation for tumour angiogenesis. Remarkably, all types of human TC (papillary, follicular, medullary, anaplastic), as well as met-astatic lesions, exhibit overexpression of SP and NK-1R compared to the normal thyroid gland. TC cells synthesize and release SP, which exerts its multiple functions through autocrine, para-crine, intracrine, and neuroendocrine processes, including the regulation of tumour burden. Con-sequently, the secretion of SP from TC results in increased SP levels in plasma, which are signifi-cantly higher in TC patients compared to controls. Additionally, NK-1R antagonists have demonstrated a dose-dependent antitumour action. They impair cancer cell proliferation on one side and induce apoptosis of tumour cells on the other side. Furthermore, it has been demonstrat-ed that NK-1R antagonists inhibit neoplastic cell migration, thereby impairing both invasiveness and metastatic abilities, as well as angiogenesis. Given the consistent overexpression of NK-1R in all types of TC, targeting this receptor represents a promising therapeutic approach for TC. Therefore, NK-1R antagonists, such as the drug aprepitant, may represent novel drugs for TC treatment.

The aim of this systematic review is to describe and to identify prospect of β-Tricalcium Phosphate (β-TCP) as Alveolar Bone Grafting (ABG) in Cleft Lip/Palate (CL/P) or alveolar bone cleft defect. A systematic review protocol based on Preferred Reporting Items for Systematic Reviews and Meta Analyses 2020 (PRISMA 2020) was drafted. The literature search was conducted with MEDLINE/PubMed, Web of Science/ ISI-Web of Knowledge, Scopus and Cochrane Library with English language and no publication year limits. The keywords yielded a total of 5824 publications. There are 3196 suitable articles to evaluate after removing duplicates and languages. Afterward, 1315 studies left after doing title and abstract reading. In addition, 85 full-articles were assessed for eligibility. The read the complete texts of those papers and eventually chose 20 that matched the inclusion requirements. Thirteen of the twenty studies included in this systematic review had a low risk of bias, one had a high risk of bias, and six had moderate bias which not report the randomization. β-TCP as ABG is biocompatible, visible and practical, offers a less invasive procedure, and does not interfere with orthodontic treatment. β-TCP as synthetic ABG can be the alternative to autologous bone grafts with several terms and conditions. Improvement of β-TCP efficacy for ABG in CL/P or alveolar bone cleft defects can be done by a tissue engineering approach combining β-TCP with growth factor, mesenchymal stem cells, or other graft materials and modification of β-TCP physical properties.

Respiratory syncytial virus (RSV) RNA synthesis takes place in cytoplasmic viral factories also called inclusion bodies (IBs), which are membrane-less organelles concentrating the viral RNA polymerase complex. IBs assembly is driven by liquid-liquid phase separation promoted by interactions between the viral nucleoprotein N and the phosphoprotein P. We recently demonstrated that cyclopamine (CPM) inhibits RSV multiplication by disorganizing and hardening IBs. Although a single mutation in the viral transcription factor M2-1 induced resistance to CPM, the mechanism of action of CPM still remains to be characterized. Here, using FRAP experiments on reconstituted pseudo-IBs both in cellula and in vitro, we first demonstrated that CPM activity depends on the presence of M2-1 together with N and P. We show that CPM impairs the competition between P and RNA binding to M2-1. As mutations on both P and M2-1 induced resistance against CPM activity, we suggest that CPM may affect the dynamics of the M2-1−P interaction, thereby affecting the relative mobility of proteins contained in RSV IBs. Overall, our results reveal that stabilizing viral protein-protein interactions is an attractive new antiviral approach. They pave the way for the rational chemical optimization of new specific anti-RSV molecules.

The paper considers coherent states for the representation of Weyl commutation relations over a field of $p$-adic numbers. A geometric object, a lattice in vector space over a field of p-adic numbers, corresponds to the family of coherent states. It is proved that the bases of coherent states corresponding to different lattices are mutually unbiased, and the operators defining the quantization of symplectic dynamics are Hadamard operators.

A paradigm shift away from null hypothesis significance testing seems in progress. Based on simulations, we illustrate some of the underlying motivations. First, P-values vary strongly from study to study, hence dichotomous inference using significance thresholds is usually unjustified. Second, statistically significant results have overestimated effect sizes, a bias declining with increasing statistical power. Third, statistically non-significant results have underestimated effect sizes, and this bias gets stronger with higher statistical power. Fourth, the tested statistical hypotheses usually lack biological justification and are often uninformative. Despite these problems, a screen of 48 papers from the 2020 volume of the Journal of Evolutionary Biology exemplifies that significance testing is still used almost universally in evolutionary biology. All screened studies tested default null hypotheses of zero effect with the default significance threshold of p = 0.05, none presented a pre-specified alternative hypothesis, pre-study power calculation, and the probability of ‘false negatives’(beta error). The results sections of the papers presented 49 significance tests on average (median 23, range 0–390). Of 41 studies that contained verbal descriptions of a ‘statistically non-significant’ result, 26 (63%) falsely claimed the absence of an effect. We conclude that studies in ecology and evolutionary biology are mostly exploratory and descriptive. We should thus shift from claiming to ‘test’ specific hypotheses statistically to describing and discussing many hypotheses (possible true effect sizes) that are most compatible with our data, given our statistical model. We already have the means for doing so, because we routinely present compatibility (‘confidence’) intervals covering these hypotheses.

A vivid debate is ongoing in the scientific community about statistical malpractice and the related publication bias. No general consensus exists on the consequences and this is reflected in heterogeneous rules defined by scientific journals on the use and reporting of statistical inference. This paper aims at discussing how the debate is perceived by the agricultural economics community and implications for our roles as researchers, contributors to the scientific publication process, and teachers. We start by summarizing the current state of the p-value debate and the replication crisis, and commonly applied statistical practices in our community. This is followed by motivation, design, results and discussion of a survey on statistical knowledge and practice among the researchers in the agricultural economics community in Austria, Germany and Switzerland. We conclude that beyond short-term measures like changing rules of reporting in publications, a cultural change regarding empirical scientific practices is needed that stretches across all our roles in the scientific process. Acceptance of scientific work should largely be based on the theoretical and methodological rigor and where the perceived relevance arises from the questions asked, the methodology employed, and the data used but not from the results generated. Revised and clear journal guidelines, the creation of resources for teaching and research, and public recognition of good practice are suggested measures to move forward.

We utilized laser irradiation as a potential technique in tuning the electrical performance of NiOx/SiO2 thin film transistors (TFTs). By optimizing the laser fluence and the number of laser pulses, the TFT performance is evaluated in terms of mobility, threshold voltage, on/off current ratio and subthreshold swing, all of which were derived from the transfer and output characteristics. The 500 laser pulses irradiated NiOx/SiO2 TFT exhibited an enhanced mobility of 3 cm2/V-s from a value of 1.25 cm2/V-s for as-deposited NiOx/SiO2 TFT. The laser-irradiated NiOx material likely has a significant concentration of defect gap states, which could also be involved in light absorption processes. Second, and more importantly, the excess energy that the photogenerated charge carriers possess (due to the significant difference between the photon energy and the bandgap of NiOx), combined with the very high light intensity, would result in complex thermal and photo thermal changes thus resulting in an enhanced electrical performance of p-type NiOx/SiO2 TFT structure.

Increasing evidence suggests that prenatal exposure to opioids can adversely influence brain development, yet, limited data exists on the effects of opioid-exposure on preschool language development. Our study aimed to characterize the nature and prevalence of language problems in children prenatally exposed to opioids, and the factors that support or hinder language acquisition. A sample of 100 children born to pregnant women in methadone maintenance treatment and 110 randomly identified non-exposed children were studied from birth to age 4.5 years. At 4.5 years, 89 opioid-exposed and 103 non-exposed children completed the preschool version of the Clinical Evaluation of Language Fundamentals (CELF-P) as part of a comprehensive neurodevelopmental assessment. Children prenatally exposed to opioid had poorer receptive and expressive language outcomes at age 4.5 years compared to non-exposed children. After adjustment for child sex, maternal education, other pregnancy substance use, maternal pregnancy nutrition and prenatal depression, opioid exposure remained a significant independent predictor of children’s total CELF-P language score. Examination of a range of potential intervening factors showed that a composite measure of the quality of parenting and home environment at 18 months and early childhood education participation at 4.5 years were important positive mediators.

A renewable cyclic monoterpene obtained from citrus peel, limonene is widely used as a fra-grance, nutraceutical ingredient, antibacterial, biopesticide, and green extraction solvent. Indus-trial demand largely exceeds supply. After reviewing recent advances in the recovery of limonene from citrus peel and residues with a particular attention to benign-by-design extractive processes, we focus on the latest results in its dehydrogenation to p-cymene via heterogeneous catalysis.

Distinguishable annual growth rings produce in Pinus roxburghii are an asset to find out the age of individual tree. This paper aimed to determine the age of P. roxburghii through coring samples and test the relationship with forest production. The biomass estimated, girths measured at two different sections and heights measured which allowed to determine the rate of tapering of the stand. The regression analysis was performed to test the relationship between various variables. The mean age of the P. roxburghii stand was found to be 23.97 (~24 years). The result showed the significant (p<0.05) positive correlation coefficient has been seen between age with girth at breast height, biomass, volume and carbon stock. However, no significant (p>0.05) correlation (r = 0.08) was found between age and height of the stand. In contrast, a correlation between diameter at breast height (DBH) was significant (p<0.05) and positive with volume, biomass, but no significant (p>0.05) correlation (r = 0.14) found between DBH and height of the stand. However, height has a significant (p<0.05) positive correlation with biomass. The mean biomass was 375 kg and mean annual increment (MAI) was 15 kg per tree. Rate of tapering of the studied stand predicted to be 3 cm diameter decreased with trunk height running at 100 cm from base to upward of P. roxburghii stand and vice versa. Result suggests that height-age relation is very weak whereas age, DBH, biomass and carbon has a significant correlation signifies that time-based forests' production and potential production estimation can be obtained in a relatively accurate way by utilizing the age of stand. The time-based forest production analysis is pioneer work in Nepal. The study affirms the tree ring count in P. roxburghii would be a credible and accurate method to determine the age of standing trees.

In this work, magnetic CuFe₂O₄/Ag nanoparticles activated by porous covalent organic frameworks (COF) was fabricated to evaluate the heterogenous reduction of 4-nitrophenol (4-NP). The core-shell CuFe₂O₄/Ag@COF was successfully prepared by polydopamine reduction of silver ions on CuFe₂O₄ nanoparticles, followed by COF layer condensation. With integrating the intrinsic characteristics of the magnetic CuFe₂O₄/Ag core and COF layer, the obtained nanocomposite exhibited features of high specific surface area (464.21 m² g^-1), ordered mesoporous structure, strong environment stability, as well as fast magnetic response. Accordingly, the CuFe₂O₄/Ag@COF catalyst showed good affinity towards 4-NP via π-π stacking interactions and possessed enhanced catalytic activity compared with CuFe₂O₄/Ag and CuFe₂O₄@COF. The pseudo-first-order rate constant of CuFe₂O₄/Ag@COF (0.77 min^-1) is 3 and 5 times higher than CuFe₂O₄/Ag and CuFe₂O₄@COF, respectively. The characteristics of bi-catalytic CuFe₂O₄/Ag and the porous COF shell of CuFe₂O₄/Ag@COF made a contribution to improve the activity of 4-NP reduction. The present work demonstrated a facile strategy to fabricate COF activated nano-catalysts with enhanced performance in the fields of nitrophenolic wastewater treatment.

In this paper, we have used magnetron sputtering to grow the high-quality GeSn layer on Ge (100) with Sn compositin up to 7%. The crystallinity of the GeSn layer is pretty good, and the strain relaxation degree of the GeSn layer is evaluated to be approximately 50%. The root mean square (RMS) value is 0.8 nm, and the averaged TDDs in the GeSn layer is 8.7×109 cm-2, which is obtained from the rocking curve of GeSn layer along (004) plane. PL measurement result shows the significant optical emission from the deposited high-quality GeSn layer, which also means that magnetron sputtering is an optional way to achieve the higher Sn composition GeSn luminescent material. To verify whether our deposited GeSn can used for optoelectronic devices, we fabricate the simple vertical p-i-n diode, and the room temperature current-voltage (I-V) characteristic was obtained. This result paves the way for future sputtered-GeSn optimization, which is critical for the optoelectronic application.

In this work, nylon 6/ P(N-phenylmaleimide-alt-styrene) blends were prepared by melt blending, and the mechanical, heat-resistant, crystallographic and dynamical mechanical properties of nylon 6/ P(N-phenylmaleimide-alt-styrene) blends with different contents were investigated and analyzed. The results showed that the mechanical properties decreased with increasing PNS, while the heat deflection temperature (HDT), relative crystallinity (Xn), and storage modulus (G’) increased with increasing PNS. The results of differential Scanning Calorimetry (DSC) proved the PNS played the positive role of nucleating PA6. And the results of dynamic mechanical analysis (DMA) proved the PNS could improve the rigidity of PA6/PNS blends. From the SEM, these PNS domains were between 0.2 and 4 μm in diameter. The experimental results indicated that the addition of PNS improved the rigidity of PA6/PNS blends, and then improved the heat-resistant property.

Incidental P losses from non-point sources may contribute to eutrophication and to decreased soil fertility. These incidents have been related to heavy rainfall on freshly fertilized agricultural soils and little is known about such incidents on more natural soils or in forests. The aim of this work is to determine if incidents of high P leaching also occur in spruce forests, and if such incidents are of significance in P cycling. We found a peak in the mineral soil solution showing that single events of high P leaching occur. The orthophosphate concentration in the Bf-horizon of the 80-year old spruce forest peaked in the autumn of the second year of a continuous monitoring. The concentration increased by more than 85 times compared to the highest concentration obtained earlier during the sampling. The amount leached during this 6 months peak is 10 times higher than the average annual leaching. This P leaching might be due to a combination of high P deposition/through-fall and a high anion exchange with dissolved-organic-carbon and Cl-. We suggest that single events of high sub-surface P leaching may contribute to the overall P leaching, and might increase with the global warming as more DOC is expected to be released to the soil solution.

The objective of the this study was to reduce the benzo[a]pyrene-induced hepatotoxicity method in herbal medicine products and to give proof that neferine, daidzein, genistein possess antihepa-totoxic effects. B[a]P which classified as a group 1 carcinogen and metabolized to B[a]P-7,8-dihydrodiol-9,10-epoxide (BPDE) causes mutagenic DNA addition products. The reduc-tion of BPDE-DNA adduct formation by B[a]P-7,8-dihydrodiol-9,10-epoxide (BPDE) was derived by benzo[a]pyrene (B[a]P). In HepG2 cells, B[a]P exhibited toxicity and substance treatment of the cells with B[a]P with neferine in lotus and daidzein, genistein in soybean reduced the BPDE-DNA ad-ducts level. The level of B[a]P-metabolites in the substance treatment of the cells was presented that BPDE levels were reduced by neferine in lotus and daidzein, genisten in soybean. These results suggest that neferine in lotus and daidzein, genistein in soybean prevent B[a]P-induced hepato-toxicity for BPDE-DNA adduct formation.

The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (WMC) and a Directional Charger (DC) are deployed to transmit wireless energy to the sensor node to improve the network's throughput. To the best of our knowledge, this is the first work to optimize the data sensing rate and charging delay by joint scheduling of MC and DC. We prove we can transmit maximum energy to each sensor node to obtain our optimization objective. In our proposed work, a DC selects a total horizon of equipped with DCs, which selects the horizon of each specific area with based on its antenna orientation. DC's orientation is scheduled for each time slot. Furthermore, multiple MCs are used to transmit energy for sensor nodes which cannot be covered by DCs. We divided the Rechargeable Wireless Sensor Network into several zones via the Voronoi diagram. We deployed a static DC and one MC charging location in each zone to provide wireless charging service jointly. We obtain the optimal charging location of MCs in each zone by solving Mix Integral Programming for energy transmission. The optimization objective of our proposed research is to sense maximum data from each sensor node with the help of maximum energy. The lifetime of each sensor network can increase, and the end delay can be maximized with joint energy transmission. Extensive simulation results demonstrate that our RWSNs are designed significantly to improve network lifetime over the baseline method.

Shape memory alloys (SMA) are becoming a more important factor in actuation technology. Due to their unique features, they bring the potential to safe weight, installation space and reduce energy consumption. The system integration of the generally small diameter NiTi wires is an important cornerstone for the emerging technology. Crimping, a common method for the mechanical and electrical connection of SMA wires, has several drawbacks when it comes to miniaturization and high force outputs. For high force applications for example, multiple SMA wires in parallel are needed to keep actuation frequencies high, while scaling up the actuation force. To meet these challenges, the proposed study deals with the development of a resistance welding process for manufacturing NiTi wire bundles. The wires are welded to sheet metal substrate, resulting in promising functional properties and high joint strengths. The welding process benefits from low cost, easy to control parameters and good automation potential. A method for evaluating the resistance welding process parameters is presented. With these parameters in place, a manufacturing process for bundled wire actuators is discussed and implemented. The welded joints are examined by peel tests, microscopy, and fatigue experiments. The performance of the manufactured bundle actuators is demonstrated by comparison to a single wire with the same accumulated cross-sectional area.

Previous studies have reported that clinical walk tests could not predict development of frailty. With advancements in wearable technology, it may be possible to predict the development of frailty using loading asymmetry parameters during clinical walk test. This prospective cohort study aimed to test the hypothesis that increased limb loading asymmetry predicts frailty risk in community-living older adults. Sixty-two independently ambulant community-living adults aged ≥ 65 years were recruited, and forty-seven subjects completed the one-year follow-up after baseline. Loading asymmetry index of net and regional (forefoot, midfoot, and rearfoot) plantar forces were collected using force sensing insoles (100 Hz) during a 10-m walk test with their maximum speed. Development of frailty was defined if the participant progressed from baseline at least one grading group of frailty at follow-up period using the Kihon Checklist. Fourteen subjects developed frailty at follow-up period. Increased risk of frailty was associated with each 1% increase in loading asymmetry of net impulse (Odds ratio 1.153, 95% CI 1.001 to 1.329). Loading asymmetry of net impulse significantly correlated with asymmetry of peak force in midfoot force. These results indicate the feasibility of measuring plantar forces of gait during clinical walking tests, and underscore the potential of using load asymmetry as a tool to augment frailty risk assessment in community-dwelling older adults.

Techniques have been developed to determine whether a horse is in resting or running mode by measuring at least one of the following: the horse's body temperature, heart rate, oxygen saturation, and exercise level. However, the existing technology has not reached a level where a trainer can accurately assess a horse's running conditions, such as speed, acceleration, stride, limb rotation, ground impact, and frontal balance, during the training of racehorses. In addition, data is needed to quickly detect abnormalities during training and provide a basis for changing the intensity of training or deciding to give a rest to the horse. Devices that detect abnormalities in equine gait with high sensitivity will contribute to animal welfare and increase the athletic longevity of horses. This paper reports the development results of a new IoT device that acquires, processes, and analyzes information such as heart rate, position, speed, acceleration, pitch, stride, and ground contact during a training of racehorses. The purpose of this development is to provide an information on a novel processing system, device, method, and program that can analyze training condition of racehorses from multiple angles.

Abstract: With the growing integration of the Internet of Things (IoT) in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of Wireless Sensor Networks (WSNs) in a commercial multi-story building, specifically addressing the difficulty of dealing with variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights that have the potential to signifi-cantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system’s performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. By leveraging the capabilities of Contiki operating system, this study employs a specialized simulator to generate simulated data, allowing for a comparison between real-world and simulated scenarios. We provide an in-depth analysis to scrutinize the disparities and commonalities between the datasets obtained from real-world deployments and simulated en-vironments. We highlight the necessity of precise simulation to validate real-life sensor data, shedding light on the challenges and opportunities associated with implementing WSNs in in-telligent buildings. Results obtained show the significance of accurate simulation models for re-liable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. The findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental pa-rameters. This includes insights into temperature, humidity, and light intensity, offering oppor-tunities for enhanced comfort and efficiency in intelligent environments.

Biometric fingerprint identification hinges on the reliability of its sensors, however, calibrating and standardizing these sensors poses significant challenges, particularly in regard to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like shaped phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels and in the contactless fingerprint sensor setting, 3D printing and silicone filling provides the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.

In this study, a novel flexible ethanol gas sensor was created by the deposition of CoFe2O4 (CFO) thin film was on a thin mica substrate using pulsed laser deposition. Transition electron microscopy (TEM) investigations clearly demonstrated the successful growth of CFO on the mica, where a well-defined interface was observed. Ethanol gas-sensing studies showed optimal performance at 200 °C, with the highest response, 19.2, to 100 ppm ethanol. Operating the sensor in self-heating mode under 7 V applied voltage, which was corresponds to a temperature of approximately 200 °C, produced a maximal response to ethanol of 19.2. This aligned with the highest responses observed during testing at 200 °C, confirming the sensor’s accuracy and sensitivity to ethanol under self-heating conditions. In addition, the sensor exhibited good selectivity to ethanol and excellent flexibility; maintaining its high performance after bending and tilting up to 5000 times. As this is the first report on flexible self-heated CFO gas sensors, we believe that this research holds great promise for the future development of high-quality sensors based on this approach.

Effective detection of isopropyl methylfluorophosphonate (GB, sarin), a type of organophosphine poisoning agent, is an urgent issue to be addressed in maintaining public safety. In this research, a gas-sensitive film material defined as poly (4-hydroxy-4,4-bis trifluoromethyl) - butyl-1-enyl) - si-loxane (SXFA) with the structure of hexafluoroisopropyl (HFIP) functional group was synthesized by using methyl vinylpropyl dichlorosilane and hexafluoroacetone trihydrate as initial materials. Synthesis process products were characterized by FTIR. SXFA was prepared on a 200 MHz shear surface wave delay line with spin coating method for GB detected. Detection limit of ＜ 0.1 mg/m3 was obtained through conditional experiments. Meanwhile, we also obtained the maximum re-sponse of 2.168 mV at 0.1 mg/m3 concentration, which indicting a much lower detection limit of SAW-SXFA sensor. Additionally, the maximum response standard deviation of 0.11 mV with a co-efficient of variation of 0.01, as well as the maximum recovery standard deviation of 0.22 mV with a coefficient of variation of 0.02 were also obtained through 5 repeated experiments. Results showed that the SAW-SXFA sensor has strong selectivity and reproducibility, good selectivity, positive detection ability, high sensitivity, and fast alarm performance for sarin detection.

Ischemia, the inadequate blood flow, can arise from pre-existing or traumatic events, remain an undetectable issue, and impact the body's ability to provide oxygen to necessary limbs, organs, muscles, or tissue. New technology involving wearable ultrasonic devices allows medical professionals to gain real-time insight into the cardiovascular system of their patients. A non-invasive, postage-stamp-sized ultrasonic sensor can be attached to a patient, allowing for continuous monitoring of the patient’s blood pressure outside of the hospital. After reviewing 16 papers on ultrasonic sensors, this paper intends to review the current use of wearable ultrasonic sensors and the advantages and limitations of the technology used to obtain accurate blood pressure readings. This review will bring attention to the evolving and expanding world of wearable ultrasonic sensors for medical applications.

Cellulose-based carbon (CBC) has been widely concerned with its porous structure, high specific surface area, and is liable to adsorb gas molecules and macromolecular pollutants. However, the application of CBC in gas sensing has been little studied. In this paper, ZnO/CBC heterojunction was formed by means of simple co-precipitation and high temperature carbonization. As a new ammonia sensor, the prepared ZnO/CBC sensor can detect ammonia compared with that of the previous pure ZnO ammonia sensor can not at room temperature. It has great gas sensing response, stability and selectivity to ammonia concentration at 200 ppm. This study provides a new idea for the design and synthesis of biomass carbon-metal oxide composites.

In this paper, we introduced a comprehensive study, based on both numerical and experimental analyses, of side polished (SP) single mode fibers (SMF) to investigate their evanescent field interaction with air and liquid analytes. In particular, the finite element method (FEM) and the beam propagation method (BPM) are employed to predict the optical properties of the SP-SMF. An ad-hoc experimental setup has been designed and built to characterize the side polished single mode fiber when air, water, and isopropanol are set in the side polished region. The sensitivity of the analytes is obtained as 1.207 V/RIU. The performance in terms of effective refractive index and transmittance are reported to show how these SP-SMF can be efficiently used for calculating the liquid refractive index. The simulation and experimental results display the significant performance of the SP-SMF as a sensing element.

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. Recent advances in air quality monitoring have produced portable, easy-to use, low-cost, sensor-based monitors, which has given a new dimension to the air pollutant monitoring and has democratized the air quality monitoring process by making monitors and results directly available at community level. This study used PurpleAir(c) sensors for PM2.5 assessment in California, USA. Evaluation of PM2.5 from sensors included Quality Assurance &amp; Quality Control (QAQC) procedures, assessment with respect to reference monitored PM2.5 concentrations, and formulation of a decision support system integrating these observations using geostatistical techniques. The hourly and daily average observed PM2.5 concentrations from PurpleAir monitors followed the trends of observed PM2.5 at regulatory monitors. PurpleAir monitored PM2.5 also captured the peak PM2.5 concentrations due to incidents like forest fire. In comparison with reference monitored PM2.5 levels, it was found that PurpleAir PM2.5 concentrations were mostly higher. The most important reason for PurpleAir higher PM2.5 concentrations was the inclusion of moisture or water vapor as aerosol in contrast to measurements of PM2.5 excluding water content in FEM/FRM and non-FEM/FRM monitors. On long term assessment (2016-2020), R2 was between 0.54 and 0.86 at selected collocated PurpleAir and regulatory monitors for hourly PM2.5 concentrations. Past research studies have been conducted for mostly shorter time periods (&lt;3-4 months) that resulted in higher R2 values between 0.80 to 0.98. This study aims to provide reasonable estimations of PM2.5 concentrations with high spatiotemporal resolutions based on statistical models using PurpleAir measurements. The methods of Kriging and IDW, geostatistical interpolation techniques, showed similar spatio-temporal patterns. Overall, this study revealed that low-cost, sensor based PurpleAir sensors could be effective and reliable tools for episodic and long-term ambient air quality monitoring.

This chapter aims to construct a relatively complete, universal, and highly reliable road surface state monitoring and perception system through the research of the A1 contact road reconstruction project of an international airport (hereinafter referred to as the “airport”). The system design is based on on-the-spot investigation, theoretical analysis, and engineering practice, using vibrating string type and resistive strain sensors as sensing means, real-time high-frequency signal acquisition technology as the guide, 4G router for wireless communication technology as the media, and storage technology for monitoring system based on server storage terminals. In combination with engineering background, aircraft motion state, main landing gear configuration, sensor selection and evaluation, and other major factors, the actual monitoring information needs of the plane are fully taken into account, as well as the difference in sensor information types, and the system design is carried out by stratification and classification.

Detecting low concentrations of thyroxine hormone (T4) is of utmost importance to prevent diseases and medical issues caused by hypothyroidism. In this work, we propose the use of a simple technique for T4 sensing, the well-known surface-enhanced Raman spectroscopy (SERS). Spherical Au and Ag nanoparticles (NPs) and functionalized with ascorbic acid and tannic acid, respectively. They synthesized by a seed-mediated growth method, were used as SERS substrates to quantify T4 at different concentrations. The NPs were characterized by UV-VIS spectroscopy and scanning electron microscopy. Both, Au and Ag NPs, have an average diameter of 50 nm, the first ones have a surface plasmon resonance at 537 nm, while the latter have it at 421 nm. It was noticed the SERS substrates are composed of isolated and agglomerated NPs. From theoreti-cal-numerical calculations, we identified the hot spots |E| of the agglomerate NPs is at least 7 times more intense than that of the isolated NPs. We show the substrates can detect up to 0.01 mM and, for the range of concentration studied, the Ag substrate is more sensitive than Au. Thus, the proposed substrates and the SERS technique constitute a potential prospect for sensing hormones and organic molecules at low concentrations.

This study aimed to improve sensitivity of ferrous particle sensors used in various mechanical systems such as wind power gearboxes to detect abnormalities by measuring the amount of ferrous wear particles generated by metal-to-metal contact. Existing sensors collect ferrous particles using a permanent magnet. However, its ability to detect abnormalities is limited because it only measures amounts of ferrous particles collected on the top of the sensor. To overcome this limitation, this study evaluated sensibility of an existing sensor using a multi-physics analysis method and improved it by changing the core shape inside the sensor. The detection ability of the improved sensor was analytically shown to be better than the existing sensor. This study highlights the use of analytical methods and multi-physics analysis for developing a ferrous particle sensor and presents a method to compare sensibility analytically. Results of the improved sensor are expected to enhance the ability to detect abnormalities in the lubrication system where ferrous wear particles are generated. This study will contribute to the development of more effective ferrous particle sensors, which can potentially improve the performance and reliability of mechanical systems.

The fourth industrial revolution, also known as Industry 4.0, has led to an increased transition towards automation and reliance on data-driven innovations and strategies. Companies in the manufacturing sector are heavily integrating Industry 4.0 technologies, including cloud compu-ting, cyber-physical systems (CPS), big data analytics, horizontal and vertical system integration, the internet of things (IoT), and additive manufacturing (3d printing). The interconnected systems and processes have significantly increased operational efficiency, enhanced organizational capacity to monitor and control functions, reduced costs, and improved product quality. One significant way that companies have achieved these benefits is by integrating diverse sensor technologies within these innovations, allowing critical data collection on products, equipment performance, and maintenance. The sensors connect systems and machines, allowing them to communicate and track operations for enhanced operations. While numerous research has been conducted to show the role of sensors in industry 4.0, limited studies show how these innovations can leverage product design to maximize benefits and opportunities. Given the rapidly changing market conditions, Industry 4.0 requires new products and business models to ensure companies adjust to the current and future changes. These requirements call for the evolutions in product design processes to accommodate design features and principles applicable in the current dynamic business environment. This research paper employs a systematic bibliometric literature review (LRSB) methodology to explore and synthesize data on how Industry 4.0 and sensors can leverage product design. The results show that various product design features create opportunities to be leveraged to guarantee the success of Industry 4.0 and sensor technologies. However, the research also identifies numerous challenges that undermine the ongoing transition towards intelligent factories and products.

Glyphosate　(GLYP)　is a broad-spectrum, non-selective, organic phosphine post emergence herbicide registered for use on many food and non-food field. Herein, we developed a biosensor (Mbs@dsDNA) based on carboxylated modified magnetic beads incubated with NH2-polyA and then hybridized with polyT-glyphosate aptamer and complementary DNA. Afterward, a quantitative detection method based on qPCR was established.　When the glyphosate aptamer on Mbs@dsDNA specifically recognized glyphosate, a complementary DNA is released and then enters the qPCR signal amplification process. The linear range of the method was 0.1-5 μg/mL, and the detection limit was set at 0.1 μg/mL. The recoveries in tap water were ranged from 103.4 ~ 104.9%, and the relative standard deviations (RSDs) were < 1%. The aptamer proposed in this study has a good potential for recognizing glyphosate. The detection method combined with qPCR might have a good application prospect in detecting and supervising other pesticide residues.

Magnetoelectric sensors based on microelectromechanical cantilevers consisting of TiN / AlN / Ni are investigated using finite element simulations in regard of the anisotropy of the E effect and its impact on the sensor sensitivity. The E effect is derived from the anisotropic magnetostriction and magnetization of single crystalline Nickel. The magnetic hardening of Nickel in saturation is demonstrated for the (110) as well as the (111) orientation. It is shown further, that magnetostrictive bending of the cantilever has a negligible impact on the eigenfrequency and thus sensitivity. The intrinsic E effect of Nickel decreases in magnitude depending on the crystal orientation when integrated into the magnetoelectric sensor design. The transitions of the individual magnetic domain states are found to be the dominant influencing factor on the sensitivity for all crystal orientations. The peak sensitivity was determined to 41.3 T-1 for (110) in-plane orientated Nickel at a magnetic bias flux of 1.78 mT. It is found, that the transition from domain wall shift to domain rotation along the hard axes yields much higher sensitivity than the transition from domain rotation to magnetization reversal. The results achieved in this work show that Nickel as hard magnetic material is able to reach almost identical sensitivities as soft magnetic materials, such as FeCoSiB.

The increasingly affordable price point of terrestrial laser scanners has led to a democratization of instrument availability, but the most common low-cost instruments have yet to be compared in terms of the consistency to measure forest structural attributes. Here, we compared two low-cost terrestrial laser scanners (TLS): the Leica BLK360 and the Faro Focus 120 3D. We evaluate the instruments in terms of point cloud quality, forest inventory estimates, tree-model reconstruction, and foliage profile reconstruction. Our direct comparison of the point clouds showed reduced noise in filtered Leica data. Tree diameter and height were consistent across instruments (4.4% and 1.4% error, respectively). Volumetric tree models were less consistent across instruments, with ~29% bias, depending on model reconstruction quality. In the process of comparing foliage profiles, we conducted a sensitivity analysis of factors affecting foliage profile estimates, showing a minimal effect from instrument maximum range (for forests less than ~50 m in height) and surprisingly little impact from degraded scan resolution. Filtered unstructured TLS point clouds must be artificially re-gridded to provide accurate foliage profiles. The factors evaluated in this comparison point towards necessary considerations for future low-cost laser scanner development and application in detecting forest structural parameters.

We consider the problem of calibrating distance measurement of Light Detection and Ranging (lidar) sensor without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground, and so that its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. We moreover consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that “fixed features shall have fixed relative distances and angles”. The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploying special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyse their statistical performance both in simulation and with field tests, reporting thus the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observing that in field tests the approach can lead to a ten-fold improvement in the accuracy of the raw measurements.

To quantify damage to reinforced concrete (RC) column members after an earthquake, an engineer needs to know the maximum applied force that was generated by the earthquake. Therefore, in this work, piezoceramic transducers are used to detect the applied force on an RC column member under dynamic loading. To investigate the use of post-embedded piezoceramic sensors in detecting the force that is applied to RC columns, eight full-size RC column specimens with various failure modes are tested under specific earthquake loadings. Post-embedded piezoceramic sensors are installed at a range of depths (70-80 mm) beneath the surface of a column specimen to examine the relationship between the signals that are obtained from them and the force applied by the dynamic actuator. The signals that are generated by the post-embedded piezoceramic sensors, which correlate with the applied force, are presented. These results indicate that the post-embedded piezoceramic sensors have great potential as tools for measuring the maximum applied force on an RC column in an earthquake. Restated, signals that are obtained from post-embedded piezoceramic sensors on an RC column in an earthquake can be used to determine the applied force and corresponding damage or residual seismic capacity.

Paper-based sensors, microfluidic platforms and electronic devices have attracted attention in the past couple of decades because they are flexible, can be recycled easily, environmentally friendly, and inexpensive. Here we report a paper aptamer-based potentiometric sensor to detect the whole Zika virus for the first time with a minimum sensitivity of 2.6 nV/Zika and the minimum detectable signal (MDS) of 0.8x1e6 Zika. Our paper sensor works very similar to a P-N junction where a junction is formed between two different wet regions with different electrochemical potentials near each other on the paper. These two regions with slightly different ionic contents, ionic species and concentrations, produce a potential difference given by the Nernst equation. Our paper sensor consisted of a 2-3 mm x 10 mm segments of a paper with a conducting silver paint contact patches on its two ends. The paper is soaked in a buffer solution containing aptamers designed to bind to the capsid proteins on Zika. Atomic force microscopy studies were carried out to show both the aptamer and Zika become immobilized in the paper. We then added the Zika (in its own buffer or simulant Urine) to the region close to one of the silver-paint contacts. The Zika virus (40 nm diameter with 43 kDa or 7.1x10-20 gm weight), became immobilized in the paper’s pores and bonded with the resident aptamers creating a concentration gradient. The potential measured between the two silver paint contacts reproducibly became more negative as upon adding the Zika. We also showed that an LCD powered by the sensor, can be used to detect the sensor output.

Paper-based sensors, microfluidic platforms and electronic devices have attracted attention in the past couple of decades because they are flexible, can be recycled easily, environmentally friendly, and inexpensive. Here we report a paper aptamer-based potentiometric sensor to detect the whole Zika virus for the first time with a minimum sensitivity of 2.6 nV/Zika and the minimum detectable signal (MDS) of 1.2x10⁶ Zika. Our paper sensor works very similar to a P-N junction where a junction is formed between two different wet regions with different electrochemical potentials near each other on the paper. These two regions with slightly different ionic contents, ionic species and concentrations, produce a potential difference given by the Nernst equation. Our paper sensor consisted of a 2-3 mm x 10 mm segments of a paper with a conducting silver paint contact patches on its two ends. The paper is soaked in a buffer solution containing aptamers designed to bind to the capsid proteins on Zika. Atomic force microscopy studies were carried out to show both the aptamer and Zika become immobilized in the paper. We then added the Zika (in its own buffer) to the region close to one of the silver-paint contacts. The Zika virus (40 nm diameter with 43 kDa or 7.1x10^-20 gm weight), became immobilized in the paper’s pores and bonded with the resident aptamers creating a concentration gradient. The potential measured between the two silver paint contacts reproducibly became more negative as upon adding the Zika. We also showed that an LCD powered by the sensor, can be used to detect the sensor output.

The paper describes preliminary studies on the influence of humidity on the electrical resistance of a textile sensor made of carbon fibers. The concept of the sensor refers to externally bonded fiber reinforcement commonly used to strengthen building structures. However, the zig-zag arrangement of carbon fiber tow allows measuring strains, as it is done in popular resistive strain gauges. The sensor tests proved its effectiveness in the measurement of strains, but also showed a high sensitivity to changes in the temperature and humidity which unfavorably affects the readings and their interpretation. The influence of these factors must be compensated. Due to the size of the sensor, there is not possible electrical compensation by the combining of several sensors into the half or full Wheatstone bridge circuit. Only mathematical compensation based on known humidity resistance functions is possible. The described research is the first step to develop such relations. The tests were carried out at temperatures of 10 °C, 20 °C and 30 °C, with changing the humidity in the range of 30-90%.

In this work, an embroidered textile moisture sensor is presented. The sensor is based on a capacitive interdigitated structure embroidered on a cotton substrate with an embroidery conductor yarn composed by 99% pure silver plated nylon yarn 140/17 dtex. In order to evaluate the sensor sensitivity, the impedance of the sensor has been measured by means of a LCR meter from 20 Hz to 20 kHz on a climatic chamber with a sweep of the relative humidity from 25% to 65% at 20 ºC. The experimental results show a clear and controllable dependence of the sensor impedance with the relative humidity. Moreover, the reproducibility of the sensor performance subject to the manufacturing process variability and washing process is also evaluated. The results show that the manufacturing variability introduce a moisture measurement error up to 4%. The washing process impact on the sensor behavior after applying the first washing cycle implies a sensitivity reduction higher than 14%. Despite these effect, the textile sensor keeps its functionality and can be reused in standard conditions. Therefore, these properties point out the usefulness of the proposed sensor to develop wearable applications on health and fitness scope including the user needs to have a life cycle longer than one-time use

Multiple sensor nodes known as detection stations make the sensor networks; each node is light and portable. Every sensor node contains power source, microcomputer, transducer and transceiver. Power source provides power to each node. Micro-computer is used for storing and processing the output coming from the sensors. The transducer is used to generate the signals and the transceiver is used to receive and transmit data to the central computer. Eavesdropping gets facilitated with wireless communication, and it has many useful applications in military, homeland, hostile and uncontrolled environments. So it is prone to the high level of security. The process in which information is gathered to form a summarized type for analysis is known as data aggregation, as it is used to reduce the energy consumption in wireless sensor networks. The security issues have become crucial in data aggregation, especially when gets deployed in hostile and remote environment. In wireless sensor networks many secure aggregations have been proposed. It still faces some resource constrained that’s why new techniques are needed. In our survey we will discuss those approaches and their pros and cons.

Fragmentation processes of multiply charged ions of C10H8 isomers produced in fast proton collision (velocity between 1.41 and 2.4 a.u.) is discussed in view of their fundamental molecular dynamics. Especially so in the mechanisms to produce a variety of carbon clusters from such ions. This aspect is assessed with the help of a multihit analysis of daughter ions in coincidence with elimination of H+ and CHn+ (n = 0 to 3). The elimination of H+/C+ was found to be very different from CH3+ loss. The loss of CH3+ was found to be accompanied with the formation of heavy ions like C9H5+, C9H5++ and C7H3+ by a cascade of momentum correlated dissociations. The most probable structures of large fragment ions in the CH3+ loss cascade are predicted with the help of computed electronic energy and the multihit ToF correlations of second and third hit. In addition, we report observation of super-dehydrogenation of naphthalene and azulene targets, with evidence of complete dehydrogenation in a single collision.

This paper proposes a multi-terminal adaptive collaborative operation method to solve the problem of unstable internal force tracking in clamping and handling unknown objects by multi-terminal robots. In the proposed method, the internal command force changes the complex internal force control problem into an internal force tracking problem from multi-slave to master. Moreover, we develop an algorithm for multi-slave setups to estimate object stiffness and motion uncertainty in the direction of the internal command force according to Lyapunov theory. Finally, the impedance control generates a reference trajectory for the multi-slave to maintain the desired internal force and track the master’s motion. Several experiments are conducted on a self-made robot equipped. The experimental results show that the oscillation amplitude of each slave end is less than 1 mm, the directional oscillation amplitude is less than 1 degree during the tracking of the desired commanded internal force. For objects with low stiffness, the error of the commanded internal force is less than 1 N (6%) per slave. The error in tracking the commanded internal force for objects with high stiffness is less than 2 N (8%). The results prove the feasibility and effectiveness of the proposed method.

In gravitational theory and astrophysical dynamics, singular initial value problems (IVPs) are frequently encountered. Finding the solutions to this class of IVPs can be challenging due to their complex nature. This study strives to circumvent the complexity by proposing a numerical method for solving such problems. The approach proposed in the current research seeks solutions to the IVP by partitioning the domain [0,L] of the problem into two intervals and solving the problem on each domain. The study seeks a closed-form solution to the IVP in the interval containing the singular point. A linearization technique and piecewise partitioning of the domain not containing the singularity are applied to the nonlinear IVP. The resulting linearized differential equation is solved using the Chebyshev spectral collocation method. Some examples are presented to illustrate the efficiency of the proposed method. Numerical analysis of the solution and residual errors are shown to ascertain convergence and accuracy. The results suggest that the technique gives accurate convergent solutions using a few collocation points.

Dracunculiasis (also known as Guinea worm disease) is caused by Dracunculus medinensis parasite and it spreads by drinking water containing Larvae of Guinea worm. The lack of safe water facilities, preventions and treatments resulted in highly dangerous consequences in its endemic regions. The economy of the affected regions totally falls down due to less production which is the result of agricultural field worker’s bad health. In this study, a multi epitope vaccine was designed against Dracunculus medinensis by using immune-informatics. The vaccine was designed by using T-Cell and B-Cell epitopes derived from Dracunculus medinensis proteins (Lactamase-B domain-containing protein, G-Domain containing protein and Ferrochelatase) in addition to Adjuvants and Linkers. The tertiary structure, physiochemical properties and immunogenic elements of vaccine were achieved. The validation of tertiary structure was accessed, and quality was achieved. In addition, the world coverage of parasite’s CTL and HTL epitopes is 95.61%. The stability of the chimeric vaccine was achieved through disulfide engineering. The molecular docking with Toll Like Receptor 4 (TLR-4) of vaccine showed its binding efficiency followed by Molecular Dynamic Simulation. The immune simulation suggested the mediated cell immunity and repeated antigen clearance. At the end, the optimized codon was used in in silico cloning to ensure vaccine’s higher exposure in bacterium E. coli strain K12. With further assessments, it is believed that the proposed multi epitope vaccine has strong immunogen to control Dracunculus medinensis which may result in better social and economic conditions of endemic regions.

Introduction Tuberculosis is common in Pakistan. Due to various factors including socioeconomic factors, compliance is poor to anti-tuberculosis drugs, leading to resistance. We aim to determine the prevalence of Multidrug resistance (MDR) tuberculosis in Pakistani population.Methods A prospective observational study was conducted from April 1, 2019, to December 31, 2019, in the Pulmonology department of a tertiary care hospital in Pakistan. Culture and sensitivity were assessed using a sputum sample or, in cases of an absent sputum sample, from Broncho alveolar lavage.ResultsApproximately 71.3% percent patients who had tuberculosis were found to be resistant to Isoniazid and around 48.6% did not respond to Rifampin. Multi-drug resistant was found in 29.4% participants.ConclusionMulti-drug resistance tuberculosis is very prevalent in Pakistan, which may increase burden on health care system and may lead to various complications of tuberculosis.

Phenotypic studies require large datasets for accurate inference and prediction. Collecting plant data in a farm can be very labor intensive and costly. This paper presents the design, architecture (hardware and software) and deployment of a distributed modular agricultural multi-robot system for row crop field data collection. The proposed system has been deployed in a soybean research farm at Iowa State University.

Human motion analysis using inertial measurement units (IMUs) has recently been shown to provide accuracy similar to the gold standard, marker-based optical motion capture, but at much lower costs and while being less restrictive and time-consuming. However, IMU-based motion analysis requires precise knowledge of the orientation in which the sensor is attached to the body segments. This knowledge is commonly obtained via an anatomical calibration procedure based on precisely defined poses or motions, which is time-consuming and error-prone. In the present work, we propose a self-calibrating approach for magnetometer-free joint angle tracking that is suitable for joints with two degrees of freedom (DoF), such as the elbow, ankle, and metacarpophalangeal finger joints. The proposed methods exploit kinematic constraints to simultaneously identify the joint axes and the heading offset. The experimental evaluation shows that the proposed methods are able to estimate plausible and consistent joint axes from just ten seconds of arbitrary elbow joint motion. Comparison with optical motion capture shows that the proposed methods yield joint angles with similar accuracy as a conventional IMU-based method while being much less restrictive. Therefore, the proposed methods improve the practical usability of IMU-based motion tracking in many clinical and biomedical applications.

This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA sample. The communication energy efficiency is increased by RⱮ, R≥1, where R is the sample reduction ratio of compressive sensing and Ɱ is the number of bits originally present in a TOA-sample word. It is found that the localization system involving sigma-delta modulation has a superior performance to that using delta-modulation or pure compressive sampling alone, in terms of both energy efficiency and localization error in the presence of TOA measurement noise, owing to the noise shaping property of sigma-delta modulation.

Existing methods of hyperspectral anomaly detection still face several challenges: 1) Due to the limitations of self-supervision, avoiding the identity mapping of anomalies remains difficult; 2) The ineffective interaction between spatial and spectral features leads to the insufficient utilization of spatial information; 3) Current methods are not adaptable to the detection of multi-scale anomaly targets. To address the aforementioned challenges, we proposed a blind spot network based on multi-scale blind spot convolution for HAD. The multi-scale mask convolution module is employed to adapt to diverse scales of anomaly targets, while the dynamic fusion module is introduced to integrate the advantages of mask convolutions at different scales. The proposed approach includes a spatial-spectral joint module and a background feature attention mechanism to enhance the interaction between spatial-spectral features, with a specific emphasis on highlighting the significance of background features within the network. Furthermore, we propose a preprocessing technique that integrates pixel shuffle down-sampling (PD) operation with spatial spectral joint screening to address the issue of anomalous identity map-ping and facilitate finite-scale mask convolution to adapt to the detection of more scale targets. The proposed approach was assessed on four real hyperspectral datasets comprising anomaly targets of different scales. The experimental results demonstrate the effectiveness and superior performance of the proposed methodology compared with nine state-of-the art methods.

In the framework of maritime surveillance, vessel detection techniques based on spaceborne Synthetic Aperture Radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. The paper deal with this topic presenting a novel approach exploiting a cascade application of a pre-screening, and discrimination phase. The latter leverage the SAR spectrum by means of sub-looks analysis. For the first time, the method has been validated with experiments on multi-frequency (C-, X-, and L-band) SAR images. For a future syngergic exploitation of more SAR missions, the developed dataset, composed of Sentinel-1, SAOCOM and COSMO-SkyMed images, is comprehensive of multiple images gathered over the same area with short time lag (below 15 minutes). Finally, the diversified processing chains and the results for each mission product and scenarios are discussed. Being the first dataset of SLC (Single Look Complex) SAR multi-frequency data, the present work intends to encourage additional research in this promising field of research.

A Wireless Body Area Sensor Network (WBASN) is combination of numerous sense nodes, positioned onto/close or inside a person body. Wireless Body Area Sensor Networks (WBASN) is a developing automation trend that exploits wireless sensor nodes to put instantaneous wearable well-being of ill person to improve individual’s existence. The sensor nodes might be used outwardly to observe abundant health parameters (like heart activity, blood pressure and cholesterol) of an ill person at a vital site within hospital. Hence the goal of WBASN is much crucial, enhancing the lifetime of nodes is compulsory to sustain many issues such as utility and efficiency. It is essential to evaluate time that when the first node will die it we want to refresh or change the battery reason is that loss of crucial information is not tolerable. The lifetime is termed as the time interval when a first node dies out due to battery exhaustion. In our proposed protocol life time of a network is the main concern as well other protocol related issues such as throughput, path loss, and residual energy. Bio-sensors are used for deployment on human body. Poisson distribution and equilibrium model techniques have been used for attaining the required results. Multi-hop network topology and random network node deployment used to achieve minimum energy consumption and longer network lifetime.

In this paper, the P versus NP problem is studied based on a six-dimensional space-time perspective. The gap between the P-class and NP-class arises from the relationship between time and the solution, as well as the answer that is independent of time. The complexity of solving a problem depends on the time dependence of the answer. Solving problems whose answers do not depend on the passage of time is very difficult and impossible. Like the description of the gravitational factor in Newtonian physics, NP class problems require different mathematical tools. Based on the generalization of physical facts in mathematics, based on six-dimensional space-time, a method to solve problem P versus NP problem has been stated. The results of this study offer a fresh insight into information processing that occurs independent of the passage of time.

In this work, a GaN-on-Si quasi-vertical Schottky diode was demonstrated on a locally grown n-GaN drift layer. The diode achieves a high current density of 2.5 kA/cm², a specific on-resistance RON,sp of 1.9 mΩ.cm² despite the crowding effect in quasi-vertical device and on/off current ratio (Ion/Ioff) of 1010. Temperature-dependent current-voltage characteristics have been measured in the range of 300-433 K to investigate the mechanisms of leakage conduction in the device. At near zero bias, thermionic emission (TE) was found to dominate. At voltage range from -1 to -10 V, electrons gain enough energy to excite into trap states, leading to the dominance of Frenkel-Poole emission (FPE). For a higher voltage range (-10V to -40V), the increased electric field promotes electron hopping along the threading dislocations in the “bulk” GaN layers, and thus, Variable Range Hopping becomes the main mechanism for the whole temperature range. This work provides an in-depth insight into the leakage conduction mechanisms on vertical GaN-on-Si Schottky barrier Diodes (SBDs) grown by localized epitaxy.

The correct selection of the value of p is a complex and iterative procedure that requires experience in the interpretation of the obtained interpolated maps. Inverse Distance Weighting is a method applied to the porosities of the K and L hydrocarbon reservoirs, discovered in the Neogene (Lower Pontian) subsurface sandstones in the northern Croatia (Pannonian Basin System). They represent small and large data samples. Also a standard statistical analysis of the data was made, followed by the qualitative-quantitative analysis of the maps, based on the selection of different values of the power distance exponent (p-value) for the K and L reservoirs maps. According to the qualitative analysis, for a small data set, p-value could be set on 1 or 2 giving the most optimal result, while for a large data set, a p value of 3 and 4 could be applied. By quantitative analysis, for the case of small data p=2 is recommended, resulting in a root mean square error value of 0.03458, a mean absolute error of 0.02013 and a median absolute deviation of 0.00546. Oppositely, a p-value of 3 and 4 is selected as appropriate for a large data set, with root mean square errors of 0.02435 and 0.02437, mean square errors of 0.01582 and 0.01509 and median absolute deviations 0.00896 and 0.00444. Eventually for a small data set, it is recommended to use a p-value of 2, and for a large data set, a p-value of 3 or 4.

Mesoporous silica nanoparticles (MSNs) can be used as carrier materials for the controlled release of pesticides while reducing their negative environmental impact. In this study, we screened an active ingredient, p-cymene (PC), with an excellent inhibitory effect on rice bacterial blight. Subsequently, the PC was successfully loaded onto MSNs via physisorption(PC@MSNs). PC@MSNs, characterized by a regular spherical shape, smooth surface, and an MSN average size of 262.9 nm, achieved an 8.6 % drug loading capacity. The release kinetics of the PC from the PC@MSNs demonstrated a sustained release (288 h) pattern influenced by drug diffusion. The efficacy of the PC@MSNs against Xanthomonas oryzae pv. Oryzae paralleled those of PC. Acute toxicity assays re-vealed that the PC@MSNs were less toxic to aquatic life (LC50=257.867 mg/L) and that the formu-lation showed no adverse effects on rice seedling growth. In summary, these results suggest that PC@MSNs can broaden PC's application scope in managing rice diseases.

Given a set of distinct non-negative integers X^n and a target certificate S in parametrized form: ∃X^k⊆X^n,∑_(x_i∈X^k)▒x_i =S (k=|X^k |,n=|X^n |). We present a polynomial solution of the subset sum problem with time complexity T≤O(kn)≤O(n^2) and space complexity S≤O(((n-1)n)/2)≤O(n^2 ), so that P = NP.

Clopidogrel is one of the most prescribed antiplatelet drugs for cardiovascular diseases prevention. Clopidogrel is a prodrug metabolised by CYP2C19 in active metabolites with anti-aggregant effect. Pharmacogenetic variants such as CYP2C19*2 variant can alter the anti-aggregant effect of clopidogrel leading to a higher risk of cardiovascular disease relapse. The aim of this study was to evaluate the prescription characteristics of CYP2C19 pharmacogenetics in patients on clopidogrel treatment, based on a specific query in a clinical data warehouse. We evaluated the demographics of patients, prescribers, medical indications, CYP2C19 pharmacogenetic tests and therapeutic changes. Neurologists were the main prescribers of CYP2C19 pharmacogenetic tests associated with clopidogrel treatment in a context of suspicion of stroke relapse. Pharmacogenetic tests were mainly prescribed a posteriori to clopidogrel prescription. Clopidogrel pharmacogenetic resistance was associated with the prescription of lysine acetylsalicylate in substitution of clopidogrel. Clear clinical practice guidelines are needed to position CYP2C19 pharmacogenetic testing for patients on clopidogrel treatment.

Electrocardigraphy remains as a first-line evaluation method for cardiac function, recording the electrical activity of the heart from the body surface. Since atrial activation is seen on the ECG as P-wave, several factors are known to impact the appearance of the P-wave, such as direction of electric impulse, conduction abnormalities and anatomical characteristics of the atria. The aim of this retrospective study was to find statistically significant associations between anatomy of pulmonary veins (PVs) osberved in cardiac computed tomography (CT) and P-wave appearance during sinus rhythm on resting ECG. For each patient, a resting 12-lead ECG was recorded, the field of analysis was P wave – its’ duration, morphology and axis. The evaluation of the CT scan recordings was performed by creating 3D models of the left atrium and analyzing anatomy of the PVs and left atrial appendage (LAA). Noteworthy correlations were found: anatomy of the left PVs showed an association with LAA volume, LAA morphology and P-wave notching in lead II. The right PVs demonstrated relation with P wave axis and amplitude. Although these correlations cannot be classified as strong, they may play a role in significant scientific discoveries in future research projects.

The scope of this study was twofold: first, to introduce a new method in examining measurement invariance, especially with large-scale studies where a large number of group comparisons are involved: the alignment approach, where only configural invariance is necessary to achieve measurement invariance. Second, to evaluate the psychometric robustness of this approach using real-life data. Particularly, we applied this approach to examine whether the factor structure of a cognitive ability test (PGAT) exhibits measurement invariance across the 26 universities of the Kingdom of Saudi Arabia. The sample consisted of 9,849 graduate students from 26 universities around the Kingdom. The results indicated a robust configural model suggesting that the P-GAT subscales were invariant across the 26 universities. Finally, the aligned factor mean values were estimated, and factor mean comparisons of every group’s mean with all other group means were conducted for both cognitive domains (Verbal and Quantitative). In both domains, King Saud University had the highest mean score among all universities. Moreover, its mean factor score was significantly higher than any other university besides Dammam University. On the other hand, several universities allocated at the northern and Southern borders of the country exhibited the lowest factor mean scores.

Panax ginseng C. A. Meyer (P. ginseng), has been widely used in traditional Chinese medicine (TCM). It contains a number of chemical components and possesses a variety of pharmacological activities. Alzheimer's disease (AD) is neurodegenerative disease that creates a huge burden on the lives and health of individuals. In recent years, studies have indicated that the chemical components of P. ginseng, especially ginsenosides, play a pronounced positive role in the prevention and treatment of neurological diseases. This review aimed to summarize currently studies on chemical components and the mechanisms action in AD intervention treatment of P. ginseng, especially ginsenosides. In this review, the components of P. ginseng and their respective active effects were first introduced. Then the key molecular mechanisms and signaling pathways of P. ginseng were introduced, and its different active ingredients in the prevention and treatment of Alzheimer's disease-related pathogenesis were also summarized from the pathogenesis of AD Aβ generation and aggregation, hyperphosphorylation of tau protein，oxidant stress，neuroinflammation，mitochondrial damage，disorder of neurotransmitter and gut microbiota. Signaling pathway networks related to the action of ginseng active ingredients was constructed, which could serve as a therapeutic target for Alzheimer's disease. In addition to a detailed report on P. ginseng in improving Alzheimer's disease-related pathogenesis, the application of the current technology, spatial metabonomics in AD therapeutics and diagnostics were later discussed. Spatial metabonomics could be applied to investigate the multi-target intervention of P. ginseng on Alzheimer's disease. Research perspectives for the study of P. ginseng in the treatment of Alzheimer's disease were provided for further studies.

The research is a deep study of the CBOE Volatility Index (VIX), which represent the market sentiments of the S & P 500 Index (SPX) over a period of 33 years. It evaluates how the two indexes perform in the stock market crash and how reliable VIX is in predicting market sentiments. The research distributes the market rally in two forms, and one is a trust rally, when investors are happy to see the market in a stable form, and investors' sentiments are positive towards the stock market. Second is a fear rally, where investors' sentiments are negative, and their outlook is bearish for various reasons. It observes the pattern of rallies, like the longest trust or fear rally, in which year and its duration. The research analyzes data to reach a meaningful conclusion and various inferences that can be drawn from the study of historical data.

When 2−1/Q<p≤2, we establish the C^{0,1} and C^{1,α}-regularities of weak solutions to quasi-linear p-Laplacian type non-homogeneous equations in the Heisenberg group.

Paenibacilli are efficient producers of potent agents against bacterial and fungal pathogens, which are of great interest both for therapeutic applications in medicine as well as in agrobiotechnol-ogy. Lipopeptides produced by such organisms play a major role in their inactivation potential. In this work we investigated two lipopeptide complexes, the fusaricidins and the polymyxins, produced by Paenibacillus polymyxa strains DSM 32871 and M1 by MALDI-TOF mass spectrometry. The fusaricidins show potent antifungal activities and are distinguished by an unusual variabil-ity. For strain DSM 32871 we identified numerous yet unknown variants mass spectrometrically. DSM 32871 produces polymyxins of type E (colistins), while M1 forms polymyxins P. For both strains novel, but not yet completely characterized polymyxin species were detected, which pos-sibly are glycosylated. These compounds may be of interest therapeutically, because polymyxins attain increasing attention as last-resort antibiotics against multiresistant pathogenic Gram-negative bacteria. In addition, the volatilomes of DSM 32781 and M1 were investigated with a GC-MS approach using different cultivation media. Production of volatile organic com-pounds (VOCs) was strain and medium dependent. In particular, strain M1 manifested as an effi-cient VOC-producer that exhibited formation of 25 volatiles in total. A characteristic feature of Paenibacilli is the formation of volatile pyrazine derivatives.