Optimal Settings Research Articles

Performance Analysis of Fiber Laser Micro-Marking on Aluminium 7075 Alloy

Analyzing the performance of fiber laser micro-marking on aluminium alloy under focus–defocus conditions entails determining the way varying the focus position during the marking process impacts the overall performance. A special process variable defocus height refers to the distance between the focal point of the laser beam and the surface of the material being marked. In micro-marking, the focal point can be moved above or below the surface to generate distinct effects. The paper determines the optimal parameters for laser micro-marking on aluminium 7075 alloy, including laser power, pulse frequency, scan speed, defocus height (the distance between the top surface and the focal point), and duty cycle, for mark quality and characteristics such as depth and mark width. Experimental results showcase that laser parameters affect mark width and depth nonlinearly. In addition, small variations in laser power, scanning speed, or focus height, have led to moderate changes in dimensions of the mark geometry. A target mark of 40[Formula: see text][Formula: see text]m in width and 25[Formula: see text][Formula: see text]m in depth is achieved by combining optimum settings like a power of 23.862[Formula: see text]w, a duty factor of 45%, a pulse frequency of 76.0506[Formula: see text]Khz, a scanning speed of 0.70[Formula: see text]mm/s, and a defocus height of 0.7[Formula: see text]mm.

Optimum image alignment setting selection for structure-from-motion photogrammetry using Remotely Piloted Aircraft Systems (RPAS) to support coral habitat classification

Coral reefs provide a range of ecological services and support highly diverse coastal ecosystem. However, the extent of living corals has been reduced by half globally as a result of anthropogenic natural stressors. Continuous monitoring by deploying accessible remote sensing (RS) methods to map coral habitats is necessary for effective conservation and management strategies. Recently, there has been an increased use of Remotely Piloted Aircraft Systems (RPAS) for coral reef RS. Image misalignment is considered a key problem in the Structure from Motion (SfM) workflow. Combinations of image alignment settings (IAS) and key point limit (KPL) optimizations aim to improve quality of the sparse cloud while lowering SfM reconstruction uncertainty, increasing projection accuracy, and ultimately improving coral habitat classification accuracy. In this study, the orthoimages were produced from a total of 25 IAS (lowest, low, medium, high and highest) and KPM (5k, 10k, 20k, 40k, and 60k) combinations using Agisoft™ Metashape software. Measurements of geometric distortion, efficiency, and completeness were used to evaluate these orthoimages with three visible bands. Results that satisfied the requirements for both geometric quality, spectral accuracy, processing efficiency identified the optimum alignment methods needed for routinely monitoring and mapping of coral reefs of Pulau Bidong. The SfM-image alignment techniques chosen for this study could produce the coral mapping extent, a higher number of tie points and matches, better image alignment success and coral habitat classification accuracy, less processing time and memory usage and no geometric distortions. The development of the methodology for the optimum parametrization of RPAS multispectral imagery would be beneficial to researchers studying coral reefs, marine sciences, and RS data analysts. It would provide them reliable methods for evaluating the quality of orthoimages and faster processing methods to achieve coastal RS objectives.

Defining Optimal Settings for Lung Cryobiopsy in End-Stage Pulmonary Disease. A Human, Ex Vivo, Diseased Lung Clinical Trial.

To evaluate optimal settings of probe size, freezing time, and distance to the pleura that influence the size and quality of biopsy specimens during transbronchial lung cryobiopsies in ESPD. We prospectively recruited 17 patients undergoing lung transplantation. We created a nonperfused ex vivo bronchoscopy setting to perform multiple cryobiopsies with different probe sizes (1.7, 1.9, and 2.4mm), freezing times (3, 5, 7, 10, 20, 30 seconds), and probe distance from pleura (5, 10, and 20mm). Alveolated pulmonary parenchyma area≥50% in histology was considered a good quality biopsy, with a minimum procedural artifact. We used logistic regression to identify independent parameters as risk factors for histologic adequacy. A total of 545 cryobiopsies were obtained from 34 explanted lungs after pneumonectomy for lung transplantation. The mean maximum diameter of the specimen achieved with the 1.7 probe was larger (13.5mm) than those obtained with 1.9 and 2.4mm probes (11.3 and 10.7mm, P= 0.07). More pleural macroscopic damage and pleural tissue in histology occurred with the 2.4mm probe ( P <0.001). There was no difference in the quality of specimens between the different freezing times and the distance from the pleura. Freezing time and distance from the pleura did not affect the histologic quality for diagnosing ESPD in severely damaged lungs. Smaller cryoprobe size did not negatively affect sample adequacy.

Parallel Computation of Piecewise Linear Morse-Smale Segmentations.

This article presents a well-scaling parallel algorithm for the computation of Morse-Smale (MS) segmentations, including the region separators and region boundaries. The segmentation of the domain into ascending and descending manifolds, solely defined on the vertices, improves the computational time using path compression and fully segments the border region. Region boundaries and region separators are generated using a multi-label marching tetrahedra algorithm. This enables a fast and simple solution to find optimal parameter settings in preliminary exploration steps by generating an MS complex preview. It also poses a rapid option to generate a fast visual representation of the region geometries for immediate utilization. Two experiments demonstrate the performance of our approach with speedups of over an order of magnitude in comparison to two publicly available implementations. The example section shows the similarity to the MS complex, the useability of the approach, and the benefits of this method with respect to the presented datasets. We provide our implementation with the paper.

Optimising Camera and Flight Settings for Ultra-fine Resolution Mapping of Artificial Night-Time Lights Using an Unoccupied Aerial System

Light pollution from Artificial night-time lights (ANTL) is a global health, economic, and environmental issue. Remote sensing and unoccupied aerial systems (UAS) provide efficient and cost-effective ways to study ANTL spatial patterns and dynamics over large areas. With ultrahigh-resolution images that can identify individual light sources, UAS offers more detailed image than satellite imagery. However, standardisation and optimisation of camera and flight settings during the acquisition ANTL UAS images is lacking. The aim of this paper is to determine the camera and flight settings to capture high-quality ANTL using a DJI Matrice 300 RTK aircraft with a Zenmuse P1 camera. It emphasises the importance of selecting appropriate camera settings for high-quality ANTL images, which can benefit future research. Results show significant image quality gains when camera and flight settings are chosen appropriately in relation to the lighting conditions. We present three experiments demonstrating a range of camera settings, and we provide practical recommendations for high-quality night-time image collection. The optimal camera settings were determined to be an exposure time of 0.0166 s, ISO of 25600, and aperture of 2.97. This experiment produced outstanding results, with 85% of images having a blur extent below 0.40.

Optimization of pacing parameters to entrain slow wave activity in the pig jejunum.

Pacing has been proposed as a therapy to restore function in motility disorders associated with electrical dysrhythmias. The spatial response of bioelectrical activity in the small intestine to pacing is poorly understood due to a lack of high-resolution investigations. This study systematically varied pacing parameters to determine the optimal settings for the spatial entrainment of slowwave activity in the jejunum. An electrode array was developed to allow simultaneous pacing and high-resolution mapping of the small intestine. Pacing parameters including pulse-width (50, 100ms), pulse-amplitude (2, 4, 8mA) and pacing electrode orientation (antegrade, retrograde, circumferential) were systematically varied and applied to the jejunum (n = 15 pigs). Pulse-amplitudes of 4mA (p = 0.012) and 8mA (p = 0.002) were more effective than 2mA in achieving spatial entrainment while pulse-widths of 50ms and 100ms had comparable effects (p = 0.125). A pulse-width of 100ms and a pulse-amplitude of 4mA were determined to be most effective for slowwave entrainment when paced in the antegrade or circumferential direction with a success rate of greater than 75%. These settings can be applied in chronic studies to evaluate the long-term efficacy of pacing, a critical aspect in determining its therapeutic potential.

Interaction of electric current with burnishing parameters in surface integrity assessment of additively manufactured Inconel 718

One of main problem associated with surface sever plastic deformation (SSPD) techniques like low plasticity burnishing (LPB) of metallic materials is limitation of surface integrity improvement at further loads (i.e. static force or pass number). It is due to interlocking of dislocation motion that limits further plastic deformation. As a plausible solution, in the present work, electric current assisted burnishing (ECAB) is introduced to use the concept of electroplasticity in surface integrity (roughness, hardness and residual stress) enhancement of Inconel 718 by additive manufacturing. Nevertheless, there are some researches which use this method to enhance performance of SSPD, the interaction effect of electrical parameter like current density with other parameters haven’t been studied well. Thus, to make the process optimized in terms of surface integrity aspects, parametric study of ECAB process with focus on interaction of current density with other LPB parameters merits further investigations. Thus, in the present work, an experimental study was carried out to identify the interaction effect of current density (as main parameter of ECAB) with other burnishing parameters (spindle speed, feed rate, current density, pass number and static force) on surface integrity aspects. Analysis of variances was further carried out to identify the contribution of each parameter on performance measures. Then, the interaction plots based on polynomial regression were developed, analyzed and discussed based on the physic of plastic deformation and some microscopic analysis like surface topography, scanning electron microscopy and X-ray diffraction analysis. It was found from the results that electric current density has the greatest effect on surface integrity aspect with contribution of 60 % on surface roughness, 68.5 % on hardness and 30 % on surface compressive residual stress. On the other hand it was found that this parameter has high interaction with process factors while applying different current density changes the optimum burnishing setting for each performance measures. Application of LPB at optimum parameter setting causes improvement of surface roughness of as-built material about 54 %; further improvement of 70 % and 85 % in roughness values were also achieved when the electric current with densities of 5A/mm2 and 10A/mm2, respectively were applied to burnishing process at optimum setting. It was also found that application of optimum LPB as well as optimum ECAB with densities of 5A/mm2 and 10A/mm2, results in improving the surface hardness about 17.5 %, 42 % and 68 %, respectively. In addition, under foresaid post-processing sequence, the surface compressive residual stress enhanced from 121 MPa (tensile type) for as-built material to −208 MPa, −345 MPa and −488 MPa (compressive type). Finally, the most optimum solution results in minimum surface roughness as well as maximum hardness and residual stress were identified by desirability approach function as 300RPM spindle speed, 0.08 mm/rev feed rate, 10A/mm2 current density, 3 pass number and 300 N force. To show the application of this optimization in a real life problem, fatigue resistance of optimum samples for high cycle and low cycle testing were obtained and compared with as-built material. It was found that following this post-treatment, the life cycle of samples improves from 160 % to 270 %, respectively that affirms practical application and manufacturing efficiency of proposed approach.

Artificial Intelligence prediction and optimization of the mechanical strength of modified Natural Fibre/MWCNT polymer nanocomposite

Artificial Intelligence (AI), techniques like artificial neural networks (ANN), and machine learning have been used to solve a variety of engineering problems. In this study, Multiwall Carbon Nanotube (MWCNT) and Natural Fibres (NF) from plantain (Musa Paradisiaca) fiber (PF), were utilized to prepare a reinforced hybrid polymer nanocomposite for advanced composite applications. A chemical solution containing potassium permanganate (KMnO4) in acetone (C3H6O) was applied to modify the fibers to alter their surface and improve adhesion and interaction between the PF/polymer matrix. To predict and optimize the tensile strength (TS) of the prepared PF/MWCNT hybrid nanocomposite (PFMNC), the ANN model with hyper-parameter optimization in a single-layer-perceptron architecture of 3-5-1 was used with 5 neurons in the hidden layer, and Box-Behnken Design (BBD) was utilized. Scanning electron microscope (SEM) micrographs demonstrate that KMnO4 modification has impacted the TS of the hybridized nanocomposite. Mechanical Test results show that these variables impacted the TS of the PFMNC as shown by analysis of variance (ANOVA) with R2 = 0.9986. The expected findings were nearly identical to the experimental results. The model predicted an optimal tensile strength of 46.1563 Mpa. To substantiate the reliability of the empirical experimental investigation, TS analysis was performed at predicted optimal settings. TS results showed an average strength of 45.4401 Mpa. About 98.45 % of the projected tensile strength is accounted for by the model. The present study has demonstrated the effectiveness of the ANN-BBD modeling technique in achieving the appropriate mechanical property values quickly, reducing production costs, and preserving resources.

AI Generated Image Detection Using Neural Networks

This abstract introduces a breakthrough in image detection using Convolutional Neural Networks (CNNs). Renowned for their ability to recognize local features, share weights, and employ pooling mechanisms, CNNs form the foundation of this innovative approach. The study presents a novel method that harnesses CNNs' inherent strengths to elevate image detection. Central to this method is the incorporation of a specialized module called "ShortCut3- ResNet," inspired by the Residual Network (ResNet) concept. This module enhances the network's capacity to capture intricate image details, thereby facilitating more precise feature extraction. An integral facet of the proposed technique is the establishment of a dual optimization model. By harmonizing the convolution and full connection processes within the CNN, this model amplifies the network's ability to understand intricate image patterns. By systematically exploring a spectrum of CNN parameter configurations, the optimal setting is identified. This approach markedly enhances the CNN's proficiency in extracting pertinent features from images, leading to substantial improvements in its image recognition accuracy. In summary, this abstract underscore a pioneering advancement in image detection, leveraging the prowess of CNNs. By integrating a specialized module and refining the learning process, this technique augments the CNN's capability to discern complex image patterns. This innovation holds transformative potential, spanning applications that range from refining image recognition systems to enhancing the precision of AI-generated image detection. Through this advancement, the intersection of CNNs and image detection propels the field towards new horizons of accuracy and efficacy. Keyword: Image detection, Convolutional Neural Networks (CNNs), Local features, Share weights, Pooling mechanisms, ShortCut3-ResNet, Residual Network (ResNet), Feature extraction.

Image security using steganography and cryptography with sweeping computational ghost imaging

A sweeping computational ghost imaging (SCGI)-based encryption system is intended for increased data security and speedier data transport. SCGI is combined with steganography and cryptography processes in this system. SCGI requires fewer shots, resulting in faster image capture, transmission, encryption, and decryption. This strategy also results in smaller, more compact data packages and higher bitrates. Least significant bit (LSB) uses steganography to conceal the hidden picture. In the case of Rivest–Shamir–Adleman (RSA) encryption, public and private keys are generated via a one-way function based on bucket values. Encryption is performed on two levels, with an asymmetric approach divided into three sub-items that significantly increase encryption. Surprisingly, the method uses fewer pictures for image reconstruction, resulting in faster image reconstruction. SCGI promises applications in an extensive number of data encryption sectors since this technology leads to smaller data packages and higher bitrates. The presented approach is examined using the number of pixel change rate (NPCR), normalized root mean square (NRMS), peak signal-to-noise ratio (PSNR), and correlation coefficient (CC), which indicates constant encryption improvement. We experimentally and situationally demonstrate our findings under eavesdropping, which prove the resistance and robustness of our methods. In optimal settings, this innovation enhances encryption by up to 90% when compared to traditional encryption methods.

Multi-attribute decision making for deep learning-based typhoon disaster assessment

Emergency rescue decisions in case of a typhoon disaster can be considered multi-attribute decision-making problems. Considering the need for the timeliness and authenticity of decision-making information sources after such a disaster, this study proposed using learning methods to process real-time online data and interval-valued neutrosophic numbers (NNs) to express the classification results. Using Typhoon Hagupit as an example, a trained text classification model was used to classify real-time data (online comments), following which the classification results were used as weights to convert these data into interval-valued NNs. Finally, the technique for order of preference by similarity to ideal solution (TOPSIS) method was adopted to rank the extent of damage caused by the typhoon in each region; the sorting results were consistent with the official statistical data, proving the effectiveness of the proposed method. A detailed sensitivity analysis was conducted to determine the optimal parameter settings of the classification model. Furthermore, the proposed method was compared with existing methods in terms of data conversion and deep learning efficiency; the results confirmed the superior capabilities of the proposed method. Notably, the proposed method can provide support to disaster management professionals in their post-disaster emergency relief work.

Integration of a facile sustainable resonance Rayleigh scattering switchable-based system for feasible determination of centrophenoxine, a nootropic and antioxidant agent; application to crude materials and dosage forms

The proposed research adheres to a certain methodology to ensure that the technique used for analyzing the centrophenoxine drug is sustainable and green. It is important to highlight that several tools that have been recently developed were utilized as potential indicators of environmental sustainability and applicability. The present research presents a novel and entirely innovative method utilizing ultrasensitive spectrofluorimetry for the detection of centrophenoxine (CPX) drug. The employed methodology in this study involved the utilization of one-step, one-pot, and direct spectrofluorimetric technique, which was found to be both efficient and environmentally sustainable in the validation and assessment of the drug. Simply, when CPX and erythrosine B reagent were combined in an acidic environment, the highly resonance Rayleigh scattering product was immediately produced. The sensitivity limits were observed to be within the range of 15–47 ng mL−1, whereas the linearity was assessed to be in the range of 50–2000 ng mL−1. The optimal settings for all modifiable parameters of the system were ascertained through an analysis of centrophenoxine-erythrosine B complexes. Moreover, the system demonstrated compliance with International Council for Harmonization (ICH) specifications without encountering any issues. The suggested process was then rated on different recent environmental safety measuring metrics to see how good it was for the environment. Fortunately, the WAC standards that combine ecological and functional elements utilizing the Green/Red/Blue (RGB 12) design also acclaimed the current analytical technique as a white one. Additionally, a new applicability evaluation tool (BAGI) was employed to estimate the practicability of the planned method in the analytical chemistry field.

Optimal Droop Control Design Using Artificial Intelligent Techniques for Electric Power Systems of More-Electric Aircraft

The design of the droop coefficient is one of the challenges for the droop control of converters, as it plays a key role in enhancing the performance of the droop control method. This paper proposes an artificial neural network (ANN) based technique for the design of optimal droop control of parallel-connected converters in a fast and accurate manner, without imposing an additional computational burden on the system. The developed ANN-based design strategy of droop coefficients is used for load sharing and DC bus voltage regulation for the more electric aircraft application. In the design process, the optimal droop coefficient setting is obtained by evaluating a user-defined fitness function with the aid of a trained ANN-based surrogate model. It is observed that the system performance metrics predicted by the surrogate model matched very well with that obtained from the simulation model. The experimental results show that the selected optimal droop coefficient setting can enhance the performance of the traditional droop control method in both steady and transient conditions.

Celebrating the Data from 100,000 Real-World Users of the MiniMed™ 780G System in Europe, Middle East, and Africa Collected Over 3 Years: From Data to Clinical Evidence.

Introduction: The present report celebrates the benchmarking of 100,000 MiniMed™ 780G system users in Europe, Middle East, and Africa (EMEA) and summarizes the major insights into the usability and outcomes of this system. Methods: Carelink Personal data (August 2020-August 2023) of users living in EMEA were analyzed. Continuous glucose monitoring-based endpoints were aggregated for (1) the full cohort and (2) a 12-month longitudinal cohort. Subanalyses were done for users on optimal settings (those spending ≥95% of time with glucose target of 100 mg/dL, and ≥95% of time with active insulin time of 2 h), for self-reported age groups (≤15 and ≥56 years) and for various countries/regions. Results: Data from 101,629 users (34 countries) were analyzed. Mean time in range (TIR) was 72.3%, glucose management indicator (GMI) was 7%, time below 70 mg/dL (TBR70) was 2.0% and time below 54 mg/dL (TBR54) was 0.4%. In terms of international targets, 59.6% of users achieved a GMI <7%, 62.5% a TIR >70%, 88.4% a TBR70 < 4%, and 90.0% a TBR54 < 1%. Data improved impressively in optimal setting users (TIR = 78.8%, and users reaching TIR >70% = 86.3%) while safety remained (TBR70 = 2.2% and TBR54 = 0.4%). Data showed consistency across self-reported age groups and geographies. In the longitudinal cohort, TIR reached 75.5% in the first month and remained 73.3% or higher over the 12-month period. Conclusion: Over 100,000 users of the MiniMed™ 780G system have demonstrated consistency in achieving target control of glycemia.

Tribological properties optimization of nylon6 composites filled with SiC and Mica

Nylon is expanding tremendously on a global scale. This paper deals with the optimization of wear behaviour of nylon-6 hybrid polymer matrix composites. The composites were prepared with Nylon 6 as the matrix and SiC and Mica as the reinforcements. Twin screw extruder and injection moulding was used to fabricate the composites. Experiments were conducted on Pin on Disc apparatus. Reinforcement %, Sliding distance, Sliding speed and Load are the parameters used for the study. Experiments were designed using Taguchi and optimization was performed using Grey Relational Analysis.L9 orthogonal array was used. It was observed that 7.5 wt% of Mica, Load 5 N, 500 m sliding distance and 6 m/s Sliding speed were the optimum process parameter settings. Using SEM micrographs, the wear mechanisms of the worn discs were examined. Additionally, it was noted that some abrasive wear and very little adhesive wear had occurred on the Nylon 6 composites.

Adaptive Bayesian contextual hyperband: A novel hyperparameter optimization approach

&lt;p&gt;Hyperparameter tuning plays a significant role when building a machine learning or a deep learning model. The tuning process aims to find the optimal hyperparameter setting for a model or algorithm from a pre-defined search space of the hyperparameters configurations. Several tuning algorithms have been proposed in recent years and there is scope for improvement in achieving a better exploration-exploitation tradeoff of the search space. In this paper, we present a novel hyperparameter tuning algorithm named adaptive Bayesian contextual hyperband (Adaptive BCHB) that incorporates a new sampling approach to identify best regions of the search space and exploit those configurations that produce minimum validation loss by dynamically updating the threshold in every iteration. The proposed algorithm is assessed using benchmark models and datasets on traditional machine learning tasks. The proposed Adaptive BCHB algorithm shows a significant improvement in terms of accuracy and computational time for different types of hyperparameters when compared with state-of-the-art tuning algorithms.&lt;/p&gt;

Multiobjective optimization of process parameters of AZ91D/AgNPs/TiO2 composite fabricated by friction stir processing using response surface methodology and desirability

PurposeThis paper aims to determine the optimum parametric settings for yielding superior mechanical properties, namely, ultimate tensile strength (UTS), yield strength (YS) and percentage elongation (EL) of AZ91D/AgNPs/TiO2 hybrid composite fabricated by friction stir processing.Design/methodology/approachAn empirical model has been developed to govern crucial influencing parameters, namely, rotation speed (RS), tool transverse speed (TS), number of passes (NPS) and reinforcement fraction (RF) or weight percentage. Box Behnken design (BBD) with four input parameters and three levels of each parameter was used to design the experimental work, and analysis of variance (ANOVA) was used to check the acceptability of the developed model. Desirability function analysis (DFA) for a multiresponse optimization approach is integrated with response surface methodology (RSM). The individual desirability index (IDI) was calculated for each response, and a composite desirability index (CDI) was obtained. The optimal parametric settings were determined based on maximum CDI values. A confirmation test is also performed to compare the actual and predicted values of responses.FindingsThe relationship between input parameters and output responses (UTS, YS, and EL) was investigated using the Box-Behnken design (BBD). Silver nanoparticles (AgNPs) and nano-sized titanium dioxide (TiO2) enhanced the ultimate tensile strength and yield strength. It was observed that the inclusion of AgNPs led to an increase in ductility, while the increase in the weight fraction of TiO2 resulted in a decrease in ductility.Practical implicationsAZ91D/AgNPs/TiO2 hybrid composite finds enormous applications in biomedical implants, aerospace, sports and aerospace industries, especially where lightweight materials with high strength are critical.Originality/valueIn terms of optimum value through desirability, the experimental trials yield the following results: maximum value of UTS (318.369 MPa), maximum value of YS (200.120 MPa) and EL (7.610) at 1,021 rpm of RS, 70 mm/min of TS, 4 NPS and level 3 of RF.

Performance analysis of high-spectral-resolution lidar with/without laser seeding technique for measuring aerosol optical properties

High-spectral-resolution lidar (HSRL) is a powerful tool for aerosol measurements. With/without laser seeding technique in the transmitted laser, the HSRL can be distinguished as the single-longitudinal-mode (SLM) HSRL or the multi-longitudinal-mode (MLM) HSRL, and the Mach-Zehnder interferometer (MZI) with periodic transmittance function can be used as the spectral discriminator in both the SLM HSRL and MLM HSRL. To in-depth knowledge of the respective advantages of the SLM HSRL and MLM HSRL for measuring aerosol optical properties, the working principle, optimal parameter setting, and detection performance of the SLM HSRL and MLM HSRL are analyzed and discussed in detail, respectively. The working principle of the SLM HSRL and MLM HSRL indicate that the effective transmittance of MZI is the important parameter of data retrieval, the main source of retrieval uncertainties, and the key factor of MZI optical path difference (OPD) settings. To ensure that the MZI can achieve the preferable separation for aerosol Mie scattering signals and molecular Rayleigh scattering signals, the optimal OPDs of MZI are set at 165 mm and 1000 mm in the SLM HSRL and MLM HSRL from the aspects of the effective transmittance of MZI and the spectral discrimination ratio (SDR). Besides, to analyze the influence of frequency difference and divergence angle for the detection performance of HSRL, the effective transmittance of MZI and SDR are simulated and the results show that the MLM HSRL has higher requirements for the environmental parameters and the echo beam collimation than the SLM HSRL. Moreover, the HSRLs with SLM and MLM transmitted lasers are constructed in Xi'an for measuring aerosol optical properties. The preliminary measurement results show that the range square corrected signal (RSCS) of Rayleigh channel is smaller than that of Mie channel in both the SLM HSRL and MLM HSRL, while the difference between RSCS of Rayleigh channel and RSCS of Mie channel in the SLM HSRL is larger than that in the MLM HSRL, and the detection range of the SLM HSRL is lower than that of the MLM HSRL.

Coaxial electrospinning of polycaprolactone – A design of experiments approach

Coaxial electrospinning is a commonly used technique in pharmaceutical sciences that allows producing polymeric nano- or microfibers with core-shell structure. In addition to process parameters such as voltage and flow rate of the polymer solutions, the properties of such fibers are influenced by solution properties including the total polymer concentration and the polymer ratio between fiber core and shell. As these parameters are typically changed empirically during development of electrospinning methods, there is a lack of structured studies that determine the optimal settings for the production of homogeneous, mechanically stable fibers. Therefore, we used a design of experiments approach to systematically address the coaxial electrospinning process of polycaprolactone, a polymer for biomaterials applied to the skin, for instance in the form of patches for wound healing or the treatment of skin cancer. We set out to investigate the influence of different process parameters on the fiber morphology (diameter and size distribution) as well as their mechanical properties as these properties are decisive determinants for handling and pharmaceutical efficacy of such patches. The investigated process parameters were the polymer concentration in the electrospinning solution and the ratio of polymer between fiber core and shell. We additionally investigated the impact of the presence of the model drug hydrocortisone in the fiber core on the electrospinning outcome and the drug release. The experiments revealed that the fiber diameter increases with increasing total polymer concentration, correlating with a narrower fiber size distribution. In addition, our results show that a higher ratio of polymer content in core and shell leads to more mechanically stable fibers (higher tensile strength, higher Young’s modulus). Taken together, the results obtained in this study contribute to a better understanding of the electrospinning process of polycaprolactone.

Improving adolescents’ dietary behavior through teacher-delivered cancer prevention education: a school-based cluster randomized intervention trial in urban Rajasthan

BackgroundDietary practices are one of the most common modifiable risk factors for cancers. Foods rich in dietary fibers are considered protective, meanwhile fast & junk foods are risk for common cancers. Adolescence period is marked by habit formation and is thus suited for delivering behavioral intervention. Schools offer an optimal setting for planning and executing these interventions to a large number of adolescents.ObjectiveTo assess the effectiveness of a teacher-delivered cancer-prevention education in changing dietary behaviors of school going adolescents.MethodsA cluster randomized trial was conducted in government secondary and senior secondary schools with schools as clusters. A minimum required sample of 1032 students was estimated from 16 schools with 1:1 allocation in intervention and non-intervention groups. Dietary behaviors were recorded as dichotomous variable. The determinants were recorded as per theory of planned behavior framework using Likert-scale. Two teachers from each intervention school were trained to deliver cancer prevention education with focus on role of dietary behavior. Pre-post training assessment of teachers’ knowledge towards common cancers was done using a self-administered questionnaire. Gender adjusted difference-in-difference analysis was done to assess intervention effect on both healthy and unhealthy behaviors.ResultsIn selected schools all students from classes 8 to 10 were approached and a total of 1224 students were enrolled, of whom 1096 completed the study. The study recorded significant improvement in scores of students from intervention group compared to non-intervention group for their attitude, subjective norms, perceived behavioral control and intention towards consuming healthy and avoiding unhealthy foods. The intervention was effective in significantly improving the proportion of students limiting fried/fast/packed food & sugar sweetened beverages (OR:1.51, 95%CI:1.08,2.12,p:0.017), and consuming fruits & vegetables daily (OR:1.55, 95%CI:1.08,2.22, p:0.017) while adjusting effect of gender.ConclusionClassroom-based cancer prevention education delivered through teachers during regular working hours is effective in improving dietary behaviors and its determinants among adolescent students. Thus, we recommend integrating a section focusing on the role of diet in cancer prevention and other lifestyle diseases in the existing school curriculum.Trial RegistrationThe trial was registered under Clinical Trial Registry-India with registration number CTRI/2018/12/016586, dated-10/12/2018.