Traditional Methods Research Articles

Regional investment risk evaluation based on compound risk correlation coefficient and migration learning approach

The high profit of regional investment is often accompanied by high risks, and the prior assessment of investment risks is conducive to avoid investment risks. However, the traditional evaluation methods usually only focus on individual risk factors, and it is difficult to evaluate and manage risks on the whole. Therefore, the study introduces deep learning algorithm, first build regional investment risk evaluation index system, then according to the characteristics of risk evaluation, design based on deep learning regional investment risk evaluation model, the final use parameter based migration learning algorithm and composite correlation coefficient to improve the evaluation model, solve the problem of insufficient training samples. The test results showed that the randomly selected 50 test samples with two different risk assessment models were 0.80 and 0.86, the deep learning algorithm tested 0.84, and the transfer learning improved model tested was 0.92, with the highest accuracy. This shows that the deep learning regional investment risk evaluation model improved by transfer learning effectively solves the problem of insufficient training data and improves the accuracy of prediction evaluation. In the field of venture capital, the model can help investors to evaluate and predict investment risks more accurately and improve the effect of investment decisions.

Functional Selection of Tau Oligomerization‐Inhibiting Aptamers

AbstractPathological hyperphosphorylation and aggregation of microtubule‐associated Tau protein contribute to Alzheimer's Disease (AD) and other related tauopathies. Currently, no cure exists for Alzheimer's Disease. Aptamers offer significant potential as next‐generation therapeutics in biotechnology and the treatment of neurological disorders. Traditional aptamer selection methods for Tau protein focus on binding affinity rather than interference with pathological Tau. In this study, we developed a new selection strategy to enrich DNA aptamers that bind to surviving monomeric Tau protein under conditions that would typically promote Tau aggregation. Employing this approach, we identified a set of aptamer candidates. Notably, BW1c demonstrates a high binding affinity (Kd=6.6 nM) to Tau protein and effectively inhibits arachidonic acid (AA)‐induced Tau protein oligomerization and aggregation. Additionally, it inhibits GSK3β‐mediated Tau hyperphosphorylation in cell‐free systems and okadaic acid‐mediated Tau hyperphosphorylation in cellular milieu. Lastly, retro‐orbital injection of BW1c tau aptamer shows the ability to cross the blood brain barrier and gain access to neuronal cell body. Through further refinement and development, these Tau aptamers may pave the way for a first‐in‐class neurotherapeutic to mitigate tauopathy‐associated neurodegenerative disorders.

Modeling and simulation analysis of optimal layout scheme of aviation logistics park based on genetic algorithm

AbstractAs an important component of modern logistics industry, aviation logistics parks address the problems of unreasonable layout and low efficiency in traditional planning and construction methods. The study first constructed a grid model for optimizing the layout of functional areas in the aviation logistics park, and selected the layout scheme with the highest comprehensive correlation as the optimal planning. At the same time, three important model parameters were determined: prediction of cargo throughput, size of functional areas in the park, and comprehensive correlation values between functional areas. Subsequently, in response to the optimization problem of functional area layout in aviation logistics parks, a cosine adaptive genetic algorithm based on adaptive reversal operation was introduced to solve the model. According to the findings, the research mention algorithm has an average utilization rate of up to 69.64% in the international freight zone, which is a 12.38% improvement over the conventional genetic algorithm. Additionally, it has an average domestic cargo area utilization rate of 67.93%, 11.24% greater than the genetic algorithm. This demonstrated that the functional area layout scheme of the aviation logistics park produced by examining the suggested algorithm is extremely viable and offers new ideas and techniques for the planning and designing of aviation logistics parks.

A novel integration of supplementary photovoltaic module into compound parabolic concentrator for accelerated defrosting of solar collecting system

In order to improve the temperature of solar greenhouse in winter and reduce the cost and energy consumption of traditional heating methods for solar greenhouse. This study presents a novel compound parabolic concentrator (CPC) coupled with a supplementary photovoltaic (SPV) module for soil heating in solar greenhouse. Firstly, a novel SPV-CPC was designed and its optical model was established by TracePro. The optical and thermal performance of the novel SPV-CPC and the traditional CPC under the same conditions were compared. The results indicate that the SPV module can intercept and utilize the escaping rays when the incident angle is large, for example, at the incident angle of 15°, the light receiving ratio of the novel SPV-CPC and traditional CPC are 78.50 % and 61.50 % respectively. In particular, the SPV module can effectively capture rays in early morning of winter to accelerate the defrosting on the glass cover. Under sunny days, the solar heat collection performance of the SPV-CPC is similar to that of the traditional CPC, and their photothermal conversion efficiencies are 54.61 % and 54.05 %, respectively. However, owing to the SPV module, the overall primary-energy saving efficiency of the SPV-CPC is 64.09 %.

Functional Selection of Tau Oligomerization-Inhibiting Aptamers.

Pathological hyperphosphorylation and aggregation of microtubule-associated Tau protein contribute to Alzheimer's Disease (AD) and other related tauopathies. Currently, no cure exists for Alzheimer's Disease. Aptamers offer significant potential as next-generation therapeutics in biotechnology and the treatment of neurological disorders. Traditional aptamer selection methods for Tau protein focus on binding affinity rather than interference with pathological Tau. In this study, we developed a new selection strategy to enrich DNA aptamers that bind to surviving monomeric Tau protein under conditions that would typically promote Tau aggregation. Employing this approach, we identified a set of aptamer candidates. Notably, BW1c demonstrates a high binding affinity (Kd=6.6 nM) to Tau protein and effectively inhibits arachidonic acid (AA)-induced Tau protein oligomerization and aggregation. Additionally, it inhibits GSK3β-mediated Tau hyperphosphorylation in cell-free systems and okadaic acid-mediated Tau hyperphosphorylation in cellular milieu. Lastly, retro-orbital injection of BW1c tau aptamer shows the ability to cross the blood brain barrier and gain access to neuronal cell body. Through further refinement and development, these Tau aptamers may pave the way for a first-in-class neurotherapeutic to mitigate tauopathy-associated neurodegenerative disorders.

Circular Dichroism Sensing: Strategies and Applications.

The analysis of the absolute configuration, enantiomeric composition, and concentration of chiral compounds are frequently encountered tasks across the chemical and health sciences. Chiroptical sensing methods can streamline this work and allow high-throughput screening with remarkable reduction of operational time and cost. During the last few years, significant methodological advances with innovative chirality sensing systems, the use of computer-generated calibration curves, machine learning assistance, and chemometric data processing, to name a few, have emerged and are now matched with commercially available multi-well plate CD readers. These developments have reframed the chirality sensing space and provide new opportunities that are of interest to a large group of chemists. This review will discuss chirality sensing strategies and applications with representative small-molecule CD sensors. Emphasis will be given to important milestones and recent advances that accelerate chiral compound analysis by outperforming traditional methods, conquer new directions, and pioneering efforts that lie at the forefront of chiroptical high-throughput screening developments. The goal is to provide the reader with a thorough understanding of the current state and a perspective of future directions of this rapidly emerging field.

Assumption-free analysis for amplification-based quantitative nucleic acid detection.

The accurate detection and quantification of biological species that are rarely present but potentially devastating is of paramount importance for the life sciences, biosecurity, food safety, and environmental monitoring. Consequently, there has been significant interest in the sensitive and accurate detection of nucleic acids, leveraging both chemical and biological methods. Among these, quantitative polymerase chain reaction (qPCR) is regarded as the gold standard due to its sensitivity and precision in identifying specific nucleic acid targets. Despite the widespread adoption of qPCR for nucleic acid detection, the analysis of qPCR data typically depends on the use of calibrated standard curves and a threshold method to interpret signal measurements. In this study, we use a stochastic simulation to show the limitations of the threshold method due to its assumptions on amplification kinetics. We propose a new approach for the absolute quantification of nucleic acids that overcomes these limitations by reconstructing the efficiency profile across amplification cycles and using cumulative amplification folds to build a standard curve, thus avoiding the constant efficiency assumption. Our method, validated through experiments with nucleic acid amplification in the presence of potent inhibitors, demonstrates improved accuracy in quantifying nucleic acids, avoiding the systematic errors of the threshold method. This innovation enhances the reliability of nucleic acid quantification, especially where traditional methods struggle with kinetic variability.

Circular Dichroism Sensing: Strategies and Applications

AbstractThe analysis of the absolute configuration, enantiomeric composition, and concentration of chiral compounds are frequently encountered tasks across the chemical and health sciences. Chiroptical sensing methods can streamline this work and allow high‐throughput screening with remarkable reduction of operational time and cost. During the last few years, significant methodological advances with innovative chirality sensing systems, the use of computer‐generated calibration curves, machine learning assistance, and chemometric data processing, to name a few, have emerged and are now matched with commercially available multi‐well plate CD readers. These developments have reframed the chirality sensing space and provide new opportunities that are of interest to a large group of chemists. This review will discuss chirality sensing strategies and applications with representative small‐molecule CD sensors. Emphasis will be given to important milestones and recent advances that accelerate chiral compound analysis by outperforming traditional methods, conquer new directions, and pioneering efforts that lie at the forefront of chiroptical high‐throughput screening developments. The goal is to provide the reader with a thorough understanding of the current state and a perspective of future directions of this rapidly emerging field.

The effect of gamma radiation on 5-hydroxymethylfurfural conversion in water and dimethyl sulfoxide

Abstract 5-Hydroxymethylfurfural (HMF) is a biomass-based chemical platform that can undergo many feasible reactions. One of the most important reactions is the oxidation to 2,5-furandicarboxylic acid (FDCA), which is the monomer for bioplastic production. In this work, the radiation method was used to investigate the conversion of HMF in both aqueous (DI) and dimethyl sulfoxide (DMSO) solutions. The effects of media solvents, atmospheric gases, HMF concentrations, additive bases, and absorbed doses of gamma radiation were studied. The results showed that the media solvent played a crucial role in HMF conversion under gamma irradiation. At 30 kGy, the HMF conversions in DI and DMSO were 92.1 and 24.1%, respectively, and the oxidation products were only found in the irradiated samples under DMSO. The HMF conversion and oxidation product formation increased with the gamma radiation dose. Moreover, it was found that FDCA stability toward gamma irradiation is highly sensitive in aqueous solution but relatively stable in DMSO. The results implied the alternative promising choice of radiation method compared with traditional methods. To join the bridge, the use of a mixture solvent DI/DMSO seems considerable in the future.

DEM-based topographic change detection considering the spatial distribution of errors

ABSTRACT Digital Elevation Model (DEM) errors tend to be spatially correlated, inevitably affecting DEM-based topographic change detection. Traditional topographic change detection methods often ignore the spatial distribution of the DEM error. This paper aims to develop a workflow that considers the spatial autocorrelation of the error in topographic change detection. Firstly, the DEM of Difference (DoD) is obtained from two-period DEMs, and the Monte Carlo method is employed to evaluate the Spatially Distributed Errors (SDE) in DEMs. Secondly, DoD errors are calculated by propagation based on spatially distributed DEM errors. At the same time, its spatial distribution is quantified using the semi-variance function. Finally, topographic changes (erosion, deposition, and net changes) are calculated based on the spatial distribution analysis and significance detection. The results in two small catchments indicate that DEM errors are spatially correlated, increasing the volume calculation errors. However, using Standard Deviation of Errors (SDE) instead of Root Mean Square Error (RMSE) can effectively reduce the sensitivity of the detection results in the significance threshold. When the significance threshold increases from 68% to 95%, the observations loss using the spatially distributed error is 4.67% −6.92% lower than that using the RMSE. The level of detection has little impact on the net topographic change and significantly influences gross erosion and deposition. In particular, the use of level of detection can effectively reduce the misclassification of erosion or deposition in stable topography areas. The proposed method can be effectively utilized in various applications like surface deformation monitoring, erosion monitoring, and sediment transport assessment.

The prevalent perplexities and countermeasures of the survey and settlement operation of Bangladesh

Identification of land through an effective land survey ensures a secure land tenure system as well as conveys a smooth settlement of land disputes. At this backdrop, the study is framed with the objective of assessing the efficacy of Zonal Settlement System (ZSS) in Bangladesh. Using non-doctrinal data collection techniques, the study found that the prevailing settlement operation conducted under ZSS in Bangladesh is surrounded by a traditional, complex, and lengthy survey method. The study also revealed that insufficient manpower, inadequacy of technical support, administrative misfeasance, and ineffective dispute resolution arrangements worsen the situation. However, the study discovered that initiation of digital land survey and inauguration of an extensive capacitybuilding program will be instrumental in overcoming the crisis. Based on the deliberate discussion, the study also recommends for establishing a time-saving and fruitful land surveying approach, along with effective and robust supervisory administration and dispute resolution authority.

Quantitative measurement of the ureter on three-dimensional magnetic resonance urography images using deep learning.

Accurate measurement of ureteral diameters plays a pivotal role in diagnosing and monitoring urinary tract obstruction (UTO). While three-dimensional magnetic resonance urography (3D MRU) represents a significant advancement in imaging, the traditional manual methods for assessing ureteral diameters are characterized by labor-intensive procedures and inherent variability. In the realm of medical image analysis, deep learning has led to a paradigm shift, yet the development of a comprehensive automated tool for the precise segmentation and measurement of ureters in MR images is an unaddressed challenge. The ureter was quantitatively measured on 3D MRU images using a deep learning model. A retrospective cohort of 445 3D MRU scans (443 patients, 52±18 years; 217 female patients) was collected and split into training, validation, and internal testing cohorts. A 3D V-Net model was trained for urinary tract segmentation, and a post-processing algorithm was developed for ureteral measurements. The accuracy of the segmentation was evaluated using the Dice similarity coefficient (DSC) and volume intraclass correlation coefficient (ICC), with ground truth segmentations provided by experienced radiologists. The external cohort comprised 50 scans (50 patients, 55±21 years; 30 female patients), and the model-predicted ureteral diameter measurements were compared with manual measurements to assess system performance. The various diameter parameters of ureter among the different measurement methods (ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction) were assessed with Friedman tests and post hoc Dunn test. The effectiveness of the UTO diagnosis was assessed by receiver operating characteristic (ROC) curves and their respective areas under the curve (AUC) between different methods. In both the internal test and external cohorts, the mean DSC values for bilateral ureters exceeded 0.70. The ICCs for the bilateral ureter volume obtained by comparing the model and manual segmentation were all greater than 0.96 (p < 0.05), except for the right ureter in the internal test cohort, for which the ICC was 0.773 (p < 0.05). The mean DSCs for interobserver and intraobserver reliability were all above 0.97. The maximum diameter of the ureter exhibited no statistically significant differences either in the dilated (p=0.08) or in the non-dilated (p=0.32) ureters across the three measurement methods. The AUCs of ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction in diagnosing UTO were 0.988 (95% CI: 0.934, 1.000), 0.961 (95% CI: 0.893, 0.991), and 0.979 (95% CI: 0.919, 0.998), respectively. There was no statistical difference between AUCs of the different methods (p>0.05). The proposed deep learning model and post-processing algorithm provide an effective means for the quantitative evaluation of urinary diseases using 3D MRU images.

Study on the constraint handling method for high-dimensional optimization of cascade reservoirs

Multicomplex constraints often need to be considered in the optimal operation of cascade reservoirs with high-dimensional decision variables, making it difficult for traditional optimization methods and modern intelligent algorithms to solve such problems. Therefore, this study proposes a constraint handling method combining a penalty function nested DPSA-POA and an intelligent algorithm, to determine the optimal flood control operation of cascade reservoirs in the middle reaches of the Ganjiang River, a problem with decision variables of up to 2196 dimensions. The results indicate that the constraint handling method proposed in this paper can solve high-dimensional optimization problems in three modes: continuous nesting (Mode 1), optimization with intelligence after obtaining a feasible solution (Mode 2), and optimization with the DPSA-POA after obtaining a feasible solution (Mode 3). Of the three modes, Mode 2 has the highest accuracy, but its calculation time is approximately 10 h. Although the accuracy of Mode 3 is slightly worse, can only achieve 98%–99% of Mode 2, its calculation time is only approximately 1–3 h. The comprehensive performance of Mode 1 is poor, the convergence accuracy can only reach 97% of Mode 2, which corresponds to a calculation time of approximately 4–6 h. The existing superiority of feasibility (SF), stochastic ranking (SR), penalty function (PF), ε-constraint (EC) and adaptive ε-constraint (Adaptive EC) methods cannot converge to a feasible solution stably, and the accuracy of the results of these methods under the condition of obtaining a feasible solution is significantly lower than that of Mode 2 and Mode 3, reaching only 95%.

A Survey on Multifactor Authentication Approaches Door Lock System

In a smart environment smart door lock is very important because previously they carried the traditional methods like carrying keys etc. after that they developed a biometric method for security for the users in different sectors because they are unique characters and that are also involved in hacking and using less quality component .and It is most important in present environment because the technology is very fast and they are not carrying key because if they lose the key it is hard to break the lock and it give high security for safety purpose because if we add smart door lock your home means it gives notification to your phone directly open or lock the door if any person came in front of the door if you are in another place it is easy to allow the person .there are so many methods used for smart door lock system but even though there are still having the challenges for security and photography. This summarized all the existing methods for solving the problem using iris recognition. By observing all the existing solutions Iris gives high security compared to other biometric.

Student Access Application

In an era where a lot of disruptive technologies that change the entire perspective of the market are arriving very often. One such is the emergence of Mobile App Development, which completely changed the way users utilized software. Even though soft- ware development generally meant a stand-alone or Web application in the past, now it has landscape, facilitating seamless access to academic resources is paramount for student success. Preliminary findings indicate a significant improvement in students' ability to access academic resources efficiently, leading to heightened engagement with course materials and improved communication between students and faculty. The SAA demonstrates promise in addressing challenges associated with traditional methods of information dissemination in educational institutions. The implications of this research extend beyond the immediate benefits for students, as institutions can draw insights for optimizing their technology-driven strategies to enhance overall educational experiences. The findings presented in this paper contribute valuable knowledge to the ongoing discourse on leveraging mobile applications to create more accessible and engaging learning environments for students in the digital age.

Effect of high-density fat combined with adipose stem cell glue on the success rate of facial filling and its clinical value.

Facial fat grafting is a popular cosmetic procedure, and experts are increasingly endorsing the use of high-density fat with adipose stem cell glue for better results. This study aims to explore the effect of high-density fat combined with adipose stem cell glue on the success rate of facial filling and its clinical value. We conducted a randomized trial with 100 patients who underwent facial fat transplantation between August 2020 and August 2022. They were divided into two groups: a control group receiving traditional Coleman fat transplantation and an observation group receiving high-density fat with adipose stem cells. In the observation and control groups, the excellent and good rate was 98.00 and 80.00%. After 3 months of treatment, the thickness of frontal subcutaneous fat and temporal subcutaneous fat in the observation group was higher (P < 0.05). Observation group retention of fat transplantation was noticeably higher 3 months after treatment (P < 0.05). Three months after treatment, the VISIA (facial imaging system) scores of facial color spots, facial pores and facial wrinkles in the observation group were lower (P < 0.05). After treatment, both groups indicated noticeable improvements in physiological functions, health status, social function, mental health, and somatic diseases compared to before treatment. Notably, the observation group had higher scores (P < 0.05). The observation group had a lower complication rate (4.00% vs. 22.00%) and higher satisfaction rate (98.00% vs. 86.00%) than the control group. Using high-density fat combined with adipose stem cell glue for facial fat grafting yields superior results, reduces complications, and boosts patient satisfaction compared to traditional methods. We have complied with all relevant ethical regulations with regard to the use of stem cells.

Energy Security Blind Spots of Gas, Oil, and Coal Exporters

The global narrative around domestic energy security is dominated by the paradigm of import-dependent countries, and as a result the interactions of energy export activities with domestic energy systems are not generally considered. In this paper, we apply a systems approach to establish two potential blind spots in evaluating the whole-of-system energy security of energy resource exporters (actual primary energy self-sufficiency and export exposure of the domestic energy system), and examine some case studies, primarily in the Australian context, to validate the existence of these blind spots. The commencement of LNG exports from the state of Queensland is examined in detail. Furthermore, we propose two novel quantitative indicators to mitigate the blind spots established. First, a revised method is proposed to calculate energy self-sufficiency, showing for the exporters studied a less secure position than shown by the traditional method. Second, an indicator is proposed to quantify the extent of exposure of the domestic energy system to international markets through export linkages, which we have applied to Australia’s domestic energy system, showing the extent of the increase in international exposure since LNG exports from Queensland commenced in 2015–2016. Conclusions of this paper include the realization that domestic energy security for energy exporters, such as Australia and the other countries examined, is more complex and, in the cases examined, less secure than importer-oriented energy security frameworks have previously recognized. A further conclusion is established that the decoupling of energy resource exports from the domestic energy system through transition to a zero-carbon energy system based on domestic renewable energy sources can be an effective means of improving Australia’s energy security.

Improved Multi-objective Particle Swarm Optimization in Software Engineering Supervision

In the 21st century, the software industry has achieved great development. The development complexity and volume of software projects are also continuously increasing. The design of software engineering supervision network plans is becoming increasingly important. In response to the poor optimization performance and poor convergence and distribution of optimal solutions in existing network planning algorithms, the Pareto optimal solution set construction method, global extremum selection method, and fitness value determination method of multi-objective particle swarm optimization algorithm are improved to enhance the convergence and distribution of the algorithm. Traditional methods only optimize one or two objectives of network planning, resulting in inconsistency with actual engineering. A multi-objective model based on resources, duration, cost, and quality is established for comprehensive optimization. Based on the results, the Pareto optimal solution curves obtained by the proposed algorithm on three classic test functions are consistent with the actual theoretical Pareto frontier curves. The proposed method is applied to engineering project examples. 10 solutions that meet the schedule requirements are obtained. Most engineering projects have a quality of over 80%, which verifies the practicality of the algorithm. The algorithm has achieved good results in optimizing engineering quality. Therefore, this model has the ability to consider various indicators such as resources and costs to obtain software engineering quality improvement plans. It has certain application potential.

A method of data analysis based on division-mining-fusion strategy

With the advancement of data technology and storage services, the scale and complexity of data are rapidly growing. Consequently, promptly analyzing data and deriving precise insights have become urgent. Nevertheless, traditional methods struggle to balance the speed and accuracy of data mining. This paper proposes a data analysis technique called the Division-Mining-Fusion (DMF) strategy to tackle this challenge. Specifically, we divide a large-scale and complex dataset into multiple small-scale and simple sub-datasets. Then, we extract the knowledge embedded within each sub-dataset. Finally, we combine the extracted knowledge from each sub-dataset to accomplish learning tasks. To demonstrate the superior performance of the DMF strategy, we apply it to two fields: rough set theory and feature selection. The DMF strategy can accelerate the speed of data mining, enhance the accuracy of data analysis, and reduce the dimensionality of data. These advantages suggest that the DMF strategy outperforms traditional methods in processing data more efficiently. In addition, the number of sub-datasets is a crucial parameter of the DMF strategy. As the number of sub-datasets increases, the ability of the DMF strategy to analyze data continuously improves.

Perspectives: Light Control of Magnetism and Device Development.

Accurately controlling magnetic and spin states presents a significant challenge in spintronics, especially as demands for higher data storage density and increased processing speeds grow. Approaches such as light control are gradually supplanting traditional magnetic field methods. Traditionally, the modulation of magnetism was predominantly achieved through polarized light with the help of ultrafast light technologies. With the growing demand for energy efficiency and multifunctionality in spintronic devices, integrating photovoltaic materials into magnetoelectric systems has introduced more physical effects. This development suggests that sunlight will play an increasingly pivotal role in manipulating spin orientation in the future. This review introduces and concludes the influence of various light types on magnetism, exploring mechanisms such as magneto-optical (MO) effects, light-induced magnetic phase transitions, and spin photovoltaic effects. This review briefly summarizes recent advancements in the light control of magnetism, especially sunlight, and their potential applications, providing an optimistic perspective on future research directions in this area.