We report on the application of time-resolved inline digital holography to study the nonlinear optical properties of quantum dots deposited onto the sample glass. Fresnel diffraction patterns of the probe pulse due to noncollinear degenerate phase modulation induced by the femtosecond pump pulse were extracted from the set of inline digital holograms and analyzed. Absolute values of the nonlinear refractive index of both sample glass substrate and the deposited layer of quantum dots were evaluated using the proposed technique. To characterize the inhomogeneous distribution of samples’ nonlinear optical properties we proposed to plot an optical nonlinearity map calculated as a local standard deviation of diffraction patterns intensity induced by noncollinear degenerate phase modulation.

In order to meet the national energy saving goals set in the Vietnam National Energy Efficiency Program in the period of 2019 – 2030 (VNEEP), the Vietnamese government has adopted a series solutions and policies to improve energy efficiency. The Vietnam’s 63 provinces will be as main actor for the national achievement in energy efficiency. Thus, understanding the province’s potentiality of energy efficiency is useful for the harmonious and sustainable development between the economy and energy systems. In this study, provincial and national data from General Statistic Office are analyzed in terms of the energy efficiency levels. With the trends of economic development and energy consumption in both national and regional levels, the Lorenz curve between Vietnamese energy consumption and GDP is investigated. The Lorenz coefficient shows the energy allocation is nether reasonable nor balanced. By using clustering method, the 63 provinces of Vietnam clustered into 7 groups that the provinces in the cluster has the similar indexes of energy efficiency i.e. ability, responsibility, potential and difficulty. The energy consumption and GDP are predicted in the period of 2019 – 2025. Based on the difference of GDP development and energy consumption levels, the target of energy efficiency for each province through clustering is set. The results show that 33 provinces included in the cluster 1, 2, 3, 4 and 6 are heavy contribution. Among them, the provinces in the cluster 2 and 3 need to focus on the industry sector in their energy saving policy. The cluster 7 included the under-developed provinces can learn development’s experiences of the provinces in the cluster 1, 2, 3 and 4 to find the best way of their future development.

Aluminum wire is a common material for wire bonding due to its resistance to oxidation and low price. It does not melt when becoming a free air ball (FAB) during the electronic flame-off (EFO) process with wettability, and is applied by wedge bonding. This study used 20μm Zn-Coated Al-0.5wt.%Si (ZAS) wires to improve the FAB shape after the EFO process, while maintaining stability of the mechanical properties, including the interface bonding strength and hardness. In order to test circuit stability after ball bonding, the current-tensile test was performed. During the experiment, it was found that 80nm ZAS with wire bonding has lower resistance and higher fusing current. For the bias tensile test, the thicker Zn film diffused into the Al-Si matrix easily, after which the strength was reduced. The ball-bond interfaces had no change in their condition before and after the bias. Accordingly, ZAS could be a promising candidate for ball bonding in the future.

Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the adaptive network-based fuzzy inference system (ANFIS) method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Neural cells can fully learn the process in their memory and after the training stage, the fuzzy structure predicts the multiphase flow data. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm. This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs. This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.

The study examines the behavior of the Mechanically Stabilized Earth (MSE) wall using available reinforcement materials in Danang, Vietnam. The MSE was reinforced by the self-fabricated galvanized steel grids using CB300V steel with 3 cm ribs. The backfill soil is a sandy clay soil from the local area with a low cohesion. A full-scale model with full instrumentation was installed to investigate the distribution of tensile forces along the reinforcement layers. The highest load that caused the wall to collapse due to internal instability (reinforcement rupture) was 302 kN/m2, which is 15 times the design load of 20 kN/m2. The failure surface within the reinforced soil had a parabolic sliding shape which is similar to the theoretical studies. At the failure load level, the maximum lateral displacement at the top of the wall facing was small (3.9 mm) which is significantly lower than the allowable displacements for the retaining wall. Furthermore, a numerical model using FLAC software was applied to simulate the performance of the MSE wall. The modeling results are in good agreement with the physical model. Thus, the self-fabricated galvanized steel grids could be confidentially used in combination with the local backfill soil for the MSE walls.

The shift towards lean production is gradually replacing traditional mass production, and lean accounting is also being mentioned to evaluate operational efficiency based on the lean philosophy, eliminating waste, and simplifying direct cost aggregation along the value stream to improve productivity, distribution, quality, and service. This study aims to evaluate the impact of various factors on the adoption of lean accounting in Vietnamese garment firms, based on data collected from 242 survey questionnaires completed by managers and accountants of Vietnamese garment firms. Through Cronbach's Alpha test, EFA test, and multiple regression analysis to verify and forecast information, eight determinants affecting the adoption of Lean Accounting in Vietnamese garment firms are arranged in descending order of influence, including Leadership, Size, Cost of implementation, Resources, Accounting department, Education and training, Culture, and Competitive pressure. Based on the findings, recommendations are proposed to management businesses and agencies to address shortcomings in the process of applying lean accounting, contributing to making it one of the most effective tools in promoting product development and continuous improvement, enhancing quality and production efficiency.

In order to make full use of the complementarity of existing navigation systems to improve the navigation and positioning accuracy, this paper investigates the visual/INS integrated navigation system based on the factor graph optimization (FGO) algorithm. In the traditional visual/INS integrated navigation system, there is often the problem of image blurring due to the rapid change of light and shadow captured by the vision sensor and the attitude change of the carrier, which leads to the decrease of the positioning accuracy of the combined navigation system. Therefore, this paper firstly introduces Retinex algorithm and BID algorithm to carry out the design of image de-blurring method; then implements the visual/INS integrated navigation algorithm based on the factor graph optimization method; finally, the experimental scheme is designed, comparative experiments are carried out, and the researched method is verified by using the public dataset. The experimental results show that the combined navigation algorithm realized in this paper has high accuracy, and its positioning accuracy is improved by 40% compared with visual positioning.

Taiwan developing offshore wind power to promote green energy and self-electricity production. In this study, a Light Detection and Ranging (Lidar) was set up at Chang-Hua development zone one on the sea and 10km away from the seashore. At Lidar location, WRF (3.33km & 2km grid lengths) model and WAsP were used to simulate the wind speed at various elevations. Three days mean wind speed of simulated results were compared with Lidar data. From the four wind data sets, developed five different comparisons to find an error% and R-Squared values. Comparison between WAsP and Floating Lidar was shown good consistency. Lukang meteorological station 10 years wind observations at 5m height were used for wind farm energy predictions. The yearly variation of energy predictions of traditional and TGC wind farm layouts are compared under purely neutral and stable condition. The one-year cycle average surface heat flux over the Taiwan Strait is negative (-72.5 (W/m2) and 157.13 STD), which represents stable condition. At stable condition TGC (92.39%) and 600(92.44%), wind farms were shown higher efficiency. The Fuhai met mast wind data was used to estimate roughness length and power law exponent. The average roughness lengths are very small and unstable atmosphere.

PG9, PG16, PGT121, and PGT145 antibodies were identified from culture media of activated memory B-cells of an infected donor and shown to neutralize many HIV-1 strains. Since HIV-1 spreads via both free virions and cell-cell fusion, we examined the effect of the antibodies on HIV-1 Env-mediated cell-cell fusion. Clone69TRevEnv cells that express Env in the absence of tetracycline were labeled with Calcein-AM Green, and incubated with CD4+ SupT1 cells labeled with CellTrace™ Calcein Red-Orange, with or without antibodies. Monoclonal antibodies PG9, PG16, 2G12, PGT121, and PGT145 (at up to 50 µg/mL) had little or no inhibitory effect on fusion between HIV-Env and SupT1 cells. By contrast, Hippeastrum hybrid agglutinin completely inhibited fusion. Our results indicate that transmission of the virus or viral genetic material would not be inhibited by these broadly neutralizing antibodies. Thus, antibodies generated by HIV-1 vaccines should be screened for their inhibitory effect on Env-mediated cell-cell fusion.

Pilots can be one of the factors in many air traffic accidents. When one or both pilots are impaired (e.g. fatigue, drunk), disabled, capable but wrong-headed, don’t have sufficient training, distracted, miscommunicate with the air traffic controller, or follow wrong instructions from the air traffic controller, the risk of accident will increase dramatically. In some of these cases, the risk can be mitigated by using big data and machine learning. The system will collect and analyze large amount of data about the state of the aircraft, e.g., the flight path, the immediate environment around the aircraft, the weather and terrain information, and the pilots’ input to control the aircraft. Additional sensors such as eye tracking devices and biological monitor can also be added to determine the condition of the pilots. If the pilots’ input do not match proper reaction to the situation or the pilots are impaired, the learning machine will first provide an advisory to the pilots. If both pilots are impaired or incapable, a warning will be sent to the flight attendants and air traffic controllers so that they can take appropriate actions. The learning machine will be trained by both accident database and an automatic training system.

This study explores wind energy resources in different locations through the Gulf of Oman and also their future variability due climate change impacts. In this regard, EC-EARTH near-surface wind outputs obtained from CORDEX-MENA simulations are used for historical and future projection of the energy. The ERA5 wind data are employed to assess the suitability of the climate model. Moreover, the ERA5 wave data over the study area are applied to compute sea surface roughness as an important variable for converting near-surface wind speeds to those of wind speed at turbine hub height. Considering the power distribution, bathymetry and distance from the coats, some spots as tentative energy hotspots to provide a detailed assessment of directional and temporal variability and also to investigate climate change impact studies. RCP8.5 is a common climatic scenario is used to project and extract future variation of the energy in the selected sites. The results of this study demonstrate that the selected locations have a suitable potential for wind power turbine plans and constructions.

On 17 October 1947, Niels Bohr was made a knight of the Order of the Elephant by the King of Denmark in view of his outstanding achievements and contributions to science. Bohr designed his own coat of arms that featured a pattern of Yin and Yang (Tai Chi symbol) to symbolize the wave-particle complementarity. However, Bohr’s Yin-Yang diagram (YYD) was neither drawn based on the principles of quantum mechanics, nor did it originate from the traditional Taoist YYD. Scientists still have doubts about the legitimacy of using YYD as the icon of the wave-particle complementarity, because the YYD belonging to quantum mechanics itself is unknown so far. This article reports the YYDs existing in quantum mechanics and justifies the role of YYD in the wave-particle duality by showing that any system, whether classical or quantum, has an ideal YYD as long as it satisfies Bohr’s principle of complementarity (BPC). The deviation of a deformed YYD from the ideal YYD indicates the extent to which a real system satisfies BPC. This article constructs the quantum YYD by the complex quantum trajectory of a particle tunneling via a step barrier, which displays the continuous transition between the wave behavior and the particle behavior. It appears that the YYD designed by Bohr in his coat of arms resembles the YYD generated by tunneling motion, not only in appearance but also in the governing equation.

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

The black hole (BH) phenomenon is an intraluminal restenotic lesion. It was identified by intravascular ultrasound (IVUS) and optical coherence tomography (OCT) after intracoronary brachytherapy and drug-eluting stent implantation. Despite the similarity in the mode of action of brachytherapy and drug-eluting stent implantation, the BH phenomenon appears to be uncommon after drug-eluting stent implantation. Specifically, the BH phenomenon is better identified by OCT than by IVUS. Herein, we present a case of in-stent restenosis with suspected BH phenomenon on IVUS confirmed by OCT.

Passive and forced drinking harm was prevalent but less recognized in Chinese adolescents. We educated adolescents on such harm to reduce their intention to drink. Students (n=1244) from 7 secondary schools in Hong Kong participated in a video-based health talk on passive and forced drinking harm. Paired t-test was used to assess their change in knowledge of passive and forced drinking, health and social harm of drinking after the health talk. McNemar's chi-squared test and adjusted multivariable logistic regression (AOR) were used to assess their change in intention to drink and intention to quit. Students were less likely to drink (OR 0.29, 95% CI 0.19-0.42) and more likely to quit drinking (OR 3.50, 1.10-14.6) after the health talk. Increased knowledge of passive drinking was associated with less intention to drink (AOR 0.93, 0.90-0.97), increased knowledge of health harm (adjusted b 0.06, 0.05-0.08), and social harm of drinking (adjusted b 0.12, 0.10-0.16). Similar associations were observed in forced drinking (intention to drink: AOR 0.87, 0.79-0.96; health harm: adjusted b 0.16, 0.12-0.19; social harm: adjusted b 0.36, 0.28-0.43). We showed preliminary evidence that the health talk on passive and forced drinking reduced the intention to drink in adolescents.

Background: Passive drinking is prevalent in adolescents worldwide, but its prevalence and harm are understudied. Methods: Secondary students (n=5840, grades 7-12) from 23 selected schools in Hong Kong participated in the survey from 2015-16. Students reported the harm of passive drinking, perceived health status, Patient Health Questionnaire-2, Perceived Stress Scale-4, perceived happiness, family health, happiness, and harmony in the questionnaire. The associations were analyzed using multivariable logistic regression (odds ratio, OR) and linear regression (unstandardized coefficient, b), adjusted for confounders. Results: 29.1% (95% CI 27.8 to 30.5%) of students experienced passive drinking in the past 30-day. Past 30-day parental passive drinking was associated with a higher level of depressive symptoms (AOR 1.63, 95% CI 1.26 to 2.10), stress (adjusted b 0.76, 0.42 to 1.10), and lower level of perceived happiness (adjusted b -0.52, -0.72 to -0.33). Past 30-day parental passive drinking was associated with a lower level of family health (adjusted b -1.39, 95% CI -1.66 to -1.11), family happiness (adjusted b -1.36, -1.64 to -1.08), and family harmony (adjusted b -1.40, -1.70 to -1.10). Conclusion: Passive drinking was associated with poorer mental health, family wellbeing, and lower level of happiness among Hong Kong Chinese adolescents.

In Taiwan, mountainous areas account for approximately two-thirds of the total area. The steep terrain and concentrated rainfall during typhoons cause landslides, which pose a considerable threat to mountain settlements. Therefore, models for analyzing rainfall-induced landslide hazards are urgently required to ensure adequate land use in mountainous areas. In this study, focusing on Pingtung County in southern Taiwan, we developed a landslide hazard index (IRL) to land use. Using FORMOSA-2 and SPOT-5 satellite images, data were collected before and after four typhoons (one in 2009 and three in 2013). The ArcGIS random tree classifier was used for interpreting satellite images to explore surface changes and disasters, which were used to analyze slope disturbances. The product of the maximum 3-h rolling rainfall intensity and effective accumulated rainfall was used as a rainfall trigger index (IRT). Considering environmental and slope disturbance factors, an index of slope environmental strength potential (ISESP) was developed through logistic regression (LR). Landslide hazard to land use was estimated using IRT and ISESP. The average coefficient of agreement (Kappa) was approximately 0.71 (medium to high accuracy); the overall accuracy of slope environmental strength potential analysis was approximately 80.4%. At a constant ISESP, IRT increased with the increasing hazard potential of rainfall-induced landslides. Furthermore, IRT and ISESP were positively correlated with landslide occurrence. When large ISESP values occur (e.g., fragile environment and high land development intensity), small IRT values may induce landslides.

Ever since oil was discovered in the Persian Gulf Region in the 1920s, the oil and gas industries have been dominant in the area. These countries are responsible for generating approximately 35 percent of the world's natural gas and 25 percent of its crude oil, making them the largest crude oil producers globally. The release of carbon dioxide (CO2) into the atmosphere is directly linked to the use of fossil fuels. CO2 accounts for 58.8 percent of all greenhouse gas emissions caused by human activities and has a detrimental impact on urban residents through the urban heat island effect. A significant portion of these emissions can be attributed to residential buildings. Some nations exacerbate the problem by relying excessively on fossil fuels. Oil-rich countries provide energy subsidies to the public in order to lower energy costs for their citizens. However, this policy can have negative consequences, including wasteful energy usage in countries like Iran.

Abstract: The COVID-19 pandemic has disrupted global energy markets and caused significant socio-economic impacts worldwide. The pandemic has also impacted the energy sector, with demand for energy reducing due to lockdowns and reduced economic activity. Hence, this paper aims to provide a comprehensive and analytical review of the impact of COVID-19 on the energy sector and discuss the potential role of artificial intelligence (AI) in mitigating some of these effects. This review examines the changes in energy demand patterns resulting from the pandemic and the implications for the energy industry, including the shifts in policymaking, communication, digital technology, energy conversion, environmental, energy market, and power system operation. The analysis of the energy pattern is discussed according to the timeframes which include pre-, mid-, and post-pandemic periods. Furthermore, we explore how AI can be used to improve energy efficiency, optimize energy use, and reduce energy wastages. The potential for AI to contribute to developing more efficient and sustainable energy systems has also been addressed. Lastly, we highlighted AI’s challenges, which play a significant role in the energy sector's response to the COVID-19 pandemic. The recommendations for AI applications in the energy sector for the transition to a more sustainable energy future are outlined. Information corroborated in this review is expected to provide important guidelines for crafting future research areas and directions in preparing the energy sector for any unforeseen circumstances or pandemic-like situations.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

The kinetics of biogas production from biomass depends on several factors such as: carbon to nitrogen ratio (C/N), reactor temperature (T), and retention time (RT). The purpose of this study was to obtain a new model for predicting biogas production. Spent Mushroom Compost (SMC) was used to produce biogas in a batch type reactor. The experiments were carried out with different C/N ratios (12.1, 20, 30 and 40) and in both mesophilic (35°C) and thermophilic (55°C) temperatures. The results showed that the Maximum biogas production at 35°C, C/N=20 was equal to 41.9 mL/gVS and 55°C, C/N=30 was equal to 51.6 mL/gVS. By using experimental data, a new kinetic model was proposed to predict biogas production. Comparing the values of the results indicate that the total values of RMSE for Logistics, Gampartz and new kinetic models was 0.1906, 0.1830 and 0.1617, respectively. Therefore, the process of anaerobic digestion of biomass can be assumed to be just a chemical reaction, and the new kinetic model is an appropriate alternative to microbial growth models.

Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.

The fluorine-less noble-gas containing anions OBONgO- and OCNNgO-have been studied by correlated electronic structure calculation and density functional theory. The obtained energetics indicates that for Ng = Kr and Xe, these anions should be kinetically stable at low temperature. The molecular structures and electron density distribution suggests that these anions are stabilized by ion-induced dipole interactions with charges concentrated on the electronegative OBO and OCN groups. The current study shows that in additional to the fluoride ion, polyatomic groups with strong electronic affinities can also form stable noble-gas containing anions of the type X-...NgO.

Introduction: Treating elbow fracture-dislocation is a challenge, especially in the situation of basal-coronoid trans-ulnar fracture. The study ams to explain whether or not the minimal invasive surgery (MIS) would be feasible in elbow dislocation with coronoid fracture. Materials & Methods: At Taipei Veterans General Hospital, patients who had elbow dislocations with coronoid process fractures underwent single one surgeon’s MIS techniques which included the fluoroscopy-guided ulnar anteromedial (FGUAM) approach in the stage of reducing coronoid process. When there is a proximal ulnar fracture, the posterior incision should be necessary, followed by the incision over the lateral or medial elbow in treating radial fractures or ligament injuries. Results: The Flow Diagram about approach recommendation was established in base of defining MIS as not to do cross-plane dissection. Anteriorly rigid fixation for the coronoid process was also emphasized. Conclusions: MIS can be achieved by multiple limited surgical incisions. However, if there is an unstable ulnar metaphysis or ligament avulsion fracture, the posterior approach has to be extended and the so-called MIS fails to be established.

Contaminants of Emerging Concern (CECs), a new category of contaminants in limelight, is a major issue of global concern. The pervasive nature of CECs and its harmful effects, such as cancer, reproductive disorders, neurotoxicity etc., make the situation alarming. The perilous nature of CECs lies in the fact that even very small concentration of CECs can cause great impacts on living beings. It also has a nature of bioaccumulation. Thus, it is the ultimate need to have efficient sensors for detection of CECs to ensure safe living environment. Electrochemical sensors are an efficient platform for CEC detection as they are highly selective, sensitive, stable, reproducible, and prompt, and can detect very low concentrations of analyte. Major classes of CECs are pharmaceuticals, illicit drugs, personal care products, endocrine disruptors, newly registered pesticides, and disinfection by-products. This review focusses on CECs, including their sources and pathways, health effects caused by them, and electrochemical sensors as reported in literatures under each category for the detection of major CECs.

The aim of this paper is to apply all the existing pyranometric method, to estimate the sunshine duration from the global solar irradiation, in order to find in their original form the most suitable method for a tropical site. Then, in a second step, one of these methods will be optimized to well fit tropical sites. Five methods in the literature (Step Algorithm, Carpentras Algorithm, Slob & Monna Algorithm, Slob & Monna 2 Algorithm, and Linear Algorithm) were applied with eleven years of global and diffuse solar radiation data. As a result, in their original form, the Step Algorithm is on the first rank. But in the second step, after improving its main coefficients, the Carpentras Algorithm was found to be the best algorithm for tropical site in the southern hemisphere.

GaN ultraviolet photodetector with metal-semiconductor-metal structure is achieved by growing on a periodic trapezoid column-shape patterned sapphire substrate using metalorganic chemical vapor deposition. Under 5-V reverse bias, the photodetector fabricated on such patterned sapphire substrate exhibits a lower dark current, a higher photocurrent, and a 476 % enhancement in the maximum responsivity as compare with those of the photodetector fabricated on conventional flat sapphire substrate. It is also found that the much larger UV-to-visible rejection ratio and the fact that responsivity drops in a smaller cut-off region are observed from photodetector fabricated by using a periodic trapezoid column-shape patterned sapphire substrate. These phenomena may all be attributed to the reduction of threading dislocation density and the improved quality of GaN film, as well as the internal reflection and/or scattering effect on the interface between GaN film and the periodic trapezoid column-shape pattern of the substrate.

Helicobacter pylori (H. pylori) is known to play a significant role in the development of peptic ulcer diseases and gastric cancer. Previous studies have reported the existence of polymorphism in the ureC gene of H. pylori. However, the relationship between genetic sequence variations in ureC and the pathogenesis of peptic ulcer diseases remains unclear. We aimed to investigate the associations between ureC sequence variations and the development of gastric ulcer and gastric cancer. PCR amplification and DNA sequencing to analyze ureC sequences, and conducted phylogenetic analysis using MEGA X software. Samples from Taiwanese patients with gastric ulcer and gastric cancer were included in this study. PCR was used to amplify the ureC gene from these samples, followed by DNA sequencing. The obtained sequences were then subjected to phylogenetic analysis using MEGA X software. In addition, ureC sequences from different geographical regions, including China and other countries, were included in the analysis for comparison. Analysis of the ureC se-quences from Taiwanese samples revealed that 6 out of 7 gastric ulcer samples clustered together in one group, while the sequences from 7 out of 8 gastric cancer samples were distributed across other groups. Phylogenetic analysis incorporating ureC sequences from different geographical regions showed that Taiwanese ureC sequences could be divided into two distinct groups, although the bootstrap values supporting this separation were low. Notably, the ureC sequences from China formed a distinct group with a high bootstrap value, separate from sequences from all other countries. There are two genotypes of ureC sequences present in Taiwanese samples, with one genotype showing a closer association with gastric cancer. Additionally, the ureC sequences from China appear to be unique and separate from those obtained from other countries, indicating the presence of genetic diversity in ureC sequences among H. pylori strains from different regions, potentially contributing to differences in disease outcomes. Further research is needed to elucidate the specific mechanisms by which ureC sequence variations may influence the pathogenesis of peptic ulcer diseases and gastric cancer.

Recently, there is a growing need for sensors that can operate autonomously, without the need for an external power source. This is especially important in applications where conventional power sources, such as batteries, are impractical or difficult to replace. Self-powered sensors have emerged as a promising solution to this challenge, offering a range of benefits such as low cost, high stability, and environmental friendliness. One of the most promising self-powered sensor technologies is L-S TENG, which stands for the liquid-solid triboelectric nanogenerator. This technology works by harnessing the mechanical energy generated by external stimuli such as pressure, touch, or vibration, and converting it into electrical energy that can be used to power sensors and other electronic devices. Therefore, self-powered based on L-S TENG, which provides numerous benefits such as rapidly responding, portability, cost-effectiveness, and miniaturization, is critical for increasing living standards and optimizing industrial processes. In this review paper, the working principle with three basic modes has been briefly introduced firstly. After that, affecting parameters to L-S TENG are reviewed based on the properties of the liquid and solid phases. With different working principles, L-S TENG has designed a lot of structure that works as a self-powered sensor for pressure/force change, liquid flow motion, concentration, and chemical detection or biochemical sensing. Moreover, the continuous output signal of TENG plays an important role in a real-time sensor that is vital to the growth of the Internet of Things.

Applied talent is not only a builder of human civilization but also a promoter of economic development. They not only need systematic and solid engineering theoretical knowledge but also rich engineering practical experience. Applied undergraduate colleges are the cradle for cultivating applied talent and shoulder the important mission of cultivating various types of applied talent for society. With the rapid development of higher education, the number of applied undergraduate institutions has sharply increased, but the quality of applied talent cultivated by them has shown a significant decline, which greatly restricts the pace of industrial development in the world. This article proposes a new concept of promoting the "three certificates" (graduation certificate, degree certificate, and skill certificate) education model in applied undergraduate colleges, which is of great significance for the country to cultivate composite talents with the ability of "product design, production management, and product processing" at the same time.

Scaffoldings play a critical role as temporary structures in supporting construction processes. Accidents at construction sites frequently stem from issues related to scaffoldings, including insufficient support caused by deviations from the construction design, insecure rod connections, or absence of cross bracing, which result in uneven loading and potential collapse, leading to casualties. In this study, we introduce a deep-learning-based, augmented reality (AR)-enabled system called scaffolding assembly deficiency detection system (SADDS) to assist field inspectors in identifying deficiencies in scaffolding assemblies. Inspectors have the flexibility to utilize SADDS through various devices, such as video cameras, mobile phones, or AR goggles (i.e., Microsoft HoloLens 2), for automated detection of deficiencies in scaffolding assemblies. The training test yielded satisfactory results, with a mean average precision of 0.89 and individual precision values of 0.96, 0.82, 0.90, and 0.89 for “qualified” frames and frames with the “missing cross-tie rod,” “missing lower-tie rod,” and “missing footboard” deficiencies, respectively. Field tests conducted at two construction sites demonstrated improved system performance compared to that in the training test. However, these field tests also revealed certain limitations of the SADDS system.

Aims: A community-based multi-component intervention (increasing awareness of the importance of physical activity in chronic illness management through reading comic books, training regarding warm-up stretching exercises, identifying facilitators and barriers to exercise through photosharing, supporting self-reflection and development of action plans) was developed to promote physical activity (PA) among patients with diabetes and hypertension. This study aimed to evaluate the efficacy of this intervention on health behaviour (walking) and health outcomes. Design: A non-randomized controlled trial with waitlisted control and pre- and post-measures. Setting: Community centres for the elderly. Participants: A total of 204 older adults with diabetes and/or hypertension were recruited. They were assigned to either the intervention group (IG) or waitlisted to the control group (CG). Intervention: Under the supervision of a nurse, six weekly group meetings were arranged in community centres for the elderly in which the participants freely exchanged their views regarding the barriers and facilitators of regular physical activity. Participants were encouraged to take photos in their neighbourhood or at home and brought these photos to share at the group meetings. The photos showed both the barriers and the facilitators to PA. In the last meeting, each participant worked out a plan to perform PA in the coming four weeks. Measures: PA referred to the number of steps taken per day and it was measured by a Garmin Accelerometer at baseline, Week 6 and Week 10. Other measures included the nine-item Self-Efficacy Scale for Exercise - Chinese version (SEE-C), the 23-item Chinese Barriers to Exercise Scale and Senior Fitness Tests. Generalised Estimating Equations (GEE) models compared the outcomes over time between IG and CG. Results: A statistically significant difference in the changes in the average number of steps taken daily between the two groups at Week 10 (mean difference = 965.4; 95% confidence interval: 92.2, 1838.6, p = 0.030) was observed, although the difference at Week 6 was non-significant (mean difference = 777.6; 95% confidence interval: −35.3, 1590.5, p = 0.061). IG participants also showed significant improvements in lower body strength (mean difference = 0.967; 95% confidence interval: 0.029, 1.904, p = 0.043) and lower limb flexibility (mean difference = 2.068; 95% confidence interval: 0.404, 3.731, p = 0.015) at Week 10 compared to CG participants. Conclusion: This multi-component intervention improved the participants’ physical activity level and physical fitness, particularly in lower limb flexibility and body strength.

In this paper, the classical least square (CLS) method with molecular absorption spectrophotometric measurement was used to determine simultaneously of paracetamol (PAR), ibuprofen (IBU), and caffeine (CAF) in tablets. The absorbance spectra of the standard solutions and samples were measured over a wavelength from 220 to 300 nm with a 0.5 nm step. The concentration of PAR, IBU and CAF in the sample solutions were calculated by using a program called CLS-Excel written in Microsoft Excel 2016 and Visual Basic for Applications (VBA). The method and CLS-Excel program were tested on mixed standard laboratory samples with different PAR, IBU, and CAF concentration ratios, and they showed small errors and satisfying repeatability. An analytical procedure for tablets containing PAR, IBU, and CAF was developed. The reliability of the procedure was proved via the recovery and repeatability of the analysis results with an actual tablet sample and comparing the mean contents of active substances in the tablets obtained from the analytical procedure with the from the HPLC method. The procedure is simple with a reduced cost compared with the HPLC standard method.

Heavy metals contamination in groundwater often occurs in various industrial processes. Stud-ies have confirmed that polysulfide could reduce hexavalent chromium to trivalent chromium, achieving the effect of in-situ stabilization. For other heavy metals contamination in groundwa-ter, whether polysulfide also had a stabilizing ability to achieve in-situ remediation. This re-search focused on heavy metals except for chromium that often contaminated in groundwater, including lead, nickel, zinc, copper, and cadmium to explore the feasibility of using calcium polysulfide (CaSx) as an in-situ stabilization technology for these heavy metals contamination groundwater. Results showed that CaSx had a great removal efficiency for heavy metals lead, nickel, zinc, copper, and cadmium. However, for nickel, zinc, copper and cadmium, when CaSx was added excessively, complexes would be formed, causing the result of re-dissolve and this would also reduce the removal efficiency. Since it is difficult to accurately control the dosage of agents for in-situ groundwater remediation, the concentration of re-dissolved nickel, zinc, cop-per, and cadmium may not be able to meet the groundwater control standards. CaSx had high lead removal efficiency, and it would not cause re-dissolution due to excessive CaSx dosing. CaSx can be used as an in-situ stabilization technique for lead contaminated groundwater.

The sustainable development of resource-based cities has long been a focal point for both governmental policies and academic research. A wealth of academic literature identifies the 'resource curse' as a critical barrier to sustainable development in these urban areas. Despite extensive studies, a consensus on effective strategies to circumvent or resolve this challenge remains elusive. Recent empirical data reveals a noticeable pattern of economic growth differentiation in China's resource-based cities. Why do some resource-based cities experience economic decline while others achieve sustainable economic growth? Therefore, this paper analyzes the reasons for the divergence of economic growth in resource cities from the perspective of heterogeneity. The results of the study show that the phenomenon of the resource curse is conditional, and the heterogeneity of demo-graphic changes, geographic location and institutional environment has a significant impact on the economic growth of resource cities. These factors are investigated to elucidate the observed diversification and heterogeneity in the economic growth of resource-based cities. Building upon this analysis, the paper proposes a framework for differentiated sustainable development pathways tailored to the unique contexts of these cities. This approach not only contributes to the current understanding of the 'resource curse' but also offers novel insights for future research aiming to adopt a more nuanced and context-specific perspective in examining sustainable urban development.

CASBEE-City tool determines the city’s Built Environment Efficiency (BEE) by calculating the improvement of Quality of Life (Q) over human activities’ Environmental Load (L) within the city’s hypothetical boundary. A total of 58 variables (57 Q indicators and one variable for L) are used in the worldwide version of CASBEE-City which were grounded using ISO 37120:2014 Sustainable Development of Communities and 17 Sustainable Development Goals (SDGs) by the United Nations (UN). This paper examines the application of CASBEE-City for Malaysian cities using the case of Johor Bahru City and identifies assessment indicators which are customised based on the data availability, reliability and suitability through focus group discussions (FGDs) which involved 36 respondents (researchers, urban planners and stakeholders). This paper reveals Johor Bahru with moderate score B+ in 2010 and 2025. Consensus were also achieved from the 36 FGD respondents for the practicability and future potential of CASBEE-City and BEE framework in Johor Bahru.

There always exist steel cuttings, holes and cracks at the interfaces in the explosive composite plate. Multi-pass friction stir processing (M-FSP) is proposed in this research to optimize the interface microstructure and the interface connection for 1060Al/Q235 explosive composite plate. Results show that the microstructures of 1060Al after M-FSP are fine and uniform owing to the strong stirring effect and recrystallization. Micro-defects formed by the explosive welding can be repaired by the M-FSP. However, M-FSP can also form tunnel defects in the aluminum, especially when the passes are one and two. The melting block and the melting lump in the composite plates are easy to become source of crack. The shear strengths and the bending properties for the 1060Al/Q235 explosive composite plate after M-FSP are the best when the passes are three, with the tool rotation speed of 1200rpm and the forward speed of 60mm/min. The optimized interfaces for the explosive composite plate after M-FSP are mainly by the metallurgical bondings, with a certain thickness and are discontinuous. Therefore, the crack extension stress is the largest and the mechanical properties are the best.

In this study, an optimization of the first blade in new test rig presented. Blade tuning is conducted using 3D geometrical parameters. Sweep and dihedral play an essential role in this study. Compressor characteristics and blades vibrational behavior are the main objective of the evaluation. Here, the attachments are designed to isolate blade dynamics from Disk. So, the Vibrational behaviors of the one's blade are tuned based on the self-excited and forced vibration phenomenon. Using a semi analytical MATLAB code instability conditions are satisfied. The code takes advantages of whitehead and force response theory to predict classically and stall flutter speeds. Beside, Forced vibrations instability is controlled using a theory presented by Campbell. Aerodynamics of new blade geometry determined using multistage simulations Computational fluid dynamics (CFD) software. Numerical results show increasing performance near the surge line and working interval along with increasing mass flow.

In the rapidly evolving landscape of genetic engineering, the advent of CRISPR-Cas technologies has catalyzed a paradigm shift, empowering scientists to manipulate the genetic code with unprece-dented accuracy and efficiency. Despite the remarkable capabilities inherent to CRISPR-Cas sys-tems, recent advancements have witnessed the integration of small molecule to augment their functionality, introducing new dimensions to the precision and versatility of gene editing applica-tions. This review delves into the synergy between CRISPR-Cas technologies and small molecule drugs, elucidating the pivotal role of chemicals in optimizing target specificity and editing efficiency. By examining a diverse array of applications, ranging from therapeutic interventions to agricultural advancements, we explore how the judicious use of chemicals enhances the precision of CRISPR-Cas-mediated genetic modifications. In this review we emphasize the significance of small molecule drugs in fine-tuning the CRISPR-Cas machinery, which allows researchers to exert meticulous con-trol over the editing process. We delve into the mechanisms through which these chemicals bolster target specificity, mitigate off-target effects, and contribute to the overall refinement of gene edit-ing outcomes. Additionally, we discuss the potential of chemical integration in expanding the scope of CRISPR-Cas technologies, enabling tailored solutions for diverse genetic manipulation challenges. As CRISPR-Cas technologies continue to evolve, the integration of small molecule drugs emerges as a crucial avenue for advancing the precision and applicability of gene editing techniques. This re-view not only synthesizes current knowledge but also highlights future prospects, paving the way for a deeper understanding of the synergistic interplay between CRISPR-Cas systems and chemical modulators in the pursuit of more controlled and efficient genetic modifications.

Epithelial ovarian cancer is an aggressive disease of the female reproductive system and a leading cause of cancer death in women. Standard of care includes surgery and platinum-based chemotherapy yet patients continue to experience a high rate of recurrence and metastasis. Hyperthermic intraperitoneal chemotherapy (HIPEC) treatment in highly selective patients extends overall survival by nearly 12 months. The clinical studies are highly supportive of the use of HIPEC in the treatment of ovarian cancer though the therapeutic approach is limited to academic medical centers. The mechanism underlying HIPEC benefit remains unknown. The efficacy of HIPEC therapy is impacted by several procedural and patient/tumor factors including the timing of surgery, platinum sensitivity, and molecular profiling such as homologous recombination deficiency. The present review aims to provide insight into the mechanistic benefit of HIPEC treatment with a focus on how hyperthermia activates the immune response, induces DNA damage and impairs DNA damage repair pathways, and has a synergistic effect with chemotherapy, with the ultimate outcome of increasing chemosensitivity. Identifying the points of fragility unmasked by HIPEC may provide the key pathways that could be the basis of new therapeutic strategies for ovarian cancer patients.

Allanite minerals are the principal host of REEs in the Sin Quyen, Iron Oxide Copper Gold (IOCG) type deposit. The studied allanites have concentrations of: REE (14-27 wt%), Ca (9-16 wt%), Al (8-19 wt%), Si (26-34 wt%) and Fe (12-21 wt%). Two populations of allanite are documented, the first is texturally older probably related to the Ca-K alteration (second stage of crystallization). This population has higher REE concentrations ranging from 20 to 27 wt%, and the second population texturally younger has lower total REE concentration ranging from 14 to 19.9 wt%, which occur as a rim surrounding the older and likely arose during the K alteration with Cu-Au mineralization (third crystallization). Differences between the two allanite populations are documented by both optical properties and analysis of their chemical composition. The last parameter indicate that the studied allanites belong to the Ce-La-ferriallanite family, with low HREE with an average of 0.21 wt.%. Temperature 355ºC which was calculated using value of δ34S isotopes is interpreted as a temperature of the second crystallization stage of allanite. The pressure of crystallization solution was calculated and is ranging from 0.98 to 5.88 MPa.

In the TANGO study, the long-term noninferior efficacy of switching to dolutegravir/lamivudine (DTG/3TC) compared to continuing tenofovir alafenamide–based regimens was demonstrated in treatment-experienced adults with HIV. However, there is limited real-world data comparing the effectiveness and safety of bictegravir/emtricitabine/tenofovir alafenamide fumarate (BIC/FTC/TAF) and DTG/3TC in Asia. This retrospective observational study, conducted at a designated HIV-care hospital from March 2019 to January 2023, aimed to address this gap. Demographic, clinical, and laboratory data, including HIV viral loads and lipid profiles, were collected and analyzed. The primary outcome was the proportion of patients with HIV RNA <50 copies/mL at week 48 in both groups, utilizing a snapshot, per-protocol–exposed population. A total of 1086 patients were included, with 511 in the DTG/3TC group and 575 in the BIC/FTC/TAF group. At week 48, 98% of patients in the DTG/3TC group and 94% in the BIC/FTC/TAF group achieved HIV RNA <50 copies/mL (p <0.01). Logistic regression analysis identified risk factors for detectable viral loads at week 48, including HIV RNA >100,000 copies/mL (AOR 5.13, 95% CI 1.42–18.53), low-level viremia (AOR 3.91, 95% CI 1.07–14.25), and record of virologic failure ≥2 times (AOR 4.21, 95% CI 1.64–10.85) before the switch. Furthermore, no significant difference was found between the DTG/3TC and BIC/FTC/TAF regimens (AOR: 1.51, 95% CI 0.70–3.24). In conclusion, the study demonstrates that the effectiveness and safety profiles of DTG/3TC and BIC/FTC/TAF were comparable in treatment-experienced adults with HIV, particularly among those with undetectable viral loads at baseline.

The integration of robotic devices into healthcare settings for rehabilitation purposes represents a rapidly advancing field with significant benefits for both therapists and patients. However, standalone robot systems face limitations such as inefficient processing of patient data and logistical challenges for patients, such as travel time to rehabilitation centers. To address these shortcomings, this paper proposes the integration of cyber-physical systems into rehabilitation robotics. Cyber-physical systems leverage network connectivity and real-time data processing to enhance the efficiency and convenience of robot-based rehabilitation. By seamlessly integrating sensors, actuators, and computational algorithms, cyber-physical rehabilitation systems empower therapists to more effectively treat patients by providing actionable insights derived from comprehensive data analysis. This enables therapists to tailor treatment plans precisely to each patient’s unique needs, ultimately leading to improved outcomes. Additionally, cyber-physical rehabilitation systems facilitate remote monitoring and intervention, allowing therapists to oversee patient progress and make timely adjustments to treatment protocols without the need for in-person visits. This not only saves patients valuable time and resources but also ensures continuity of care, particularly for individuals residing in remote or underserved areas. Furthermore, cyber-physical rehabilitation systems enable collaborative decision-making by facilitating communication and information sharing among healthcare professionals, further enhancing the quality and efficacy of rehabilitation services. In summary, integrating cyber-physical systems into rehabilitation robotics represents a significant advancement in leveraging technology to optimize patient care and outcomes, promising to revolutionize rehabilitation therapy.

This paper presents a comprehensive study on the application and optimization of automotive thermoelectric generators (ATEGs), focusing on the crucial role of heat exchangers in enhancing energy conversion efficiency. Recognizing the substantial waste of thermal energy in internal combustion engines, our research delves into the potential of TEGs to convert engine waste heat into electrical energy, thereby improving fuel efficiency and reducing environmental impact. We meticulously analyze various heat exchanger designs, assessing their influence on the TEG's output power under different exhaust gas flow rates and temperatures. Furthermore, we explore the impact of TEG material properties on the overall energy conversion effectiveness. Our findings reveal that specific heat exchanger designs significantly enhance the efficiency of waste gas heat exchange, leading to an improved performance of the TEG system. We also highlight the importance of thermal insulation in maximizing TEG output. This study not only contributes to the ongoing efforts to develop more sustainable and efficient vehicles but also provides valuable insights into the practical application of thermoelectric technology in automotive engineering. Through this research, we aim to pave the way for more environmentally friendly transportation solutions, aligning with global efforts to reduce fossil fuel dependence and mitigate environmental pollution.

Ganoderic Acid (GA), a major bioactive compound isolated from an oriental medicinal mushroom, i.e. Ganoderma tsugae, has traditionally been found to have significant medicinal properties. As a result of GA's poor solubility in water, formulation poses several challenges. Additionally, the traditional method of administering it via alcohol always results in unfavorable patient side effects. In order to overcome these problems, Ganoderma Tsugae extracts obtained by ethanol extraction were encapsulated in nanodispersions by ultrasonic cavitation and solvent evaporation to maximise bioavailability. Resultant nanodispersions contained Ganoderic acid in a hydrophobic core with a mean particle size of no more than 200 nm and a very narrow particle distribution. An analysis of the interactions between the control variables and the process optimisation was conducted using the Response Surface Methodology (RSM). Based on the results of the experiment, it was found that the particle size of nanodispersions was influenced by the Hydrophilic-Lipophilic Balance (HLB) number and the evaporation temperature. Moreover, the physical stability of micellar systems is generally determined by the organic phase within them. Regarding formulation stability, the zeta average of the nanodispersions remained virtually unchanged in solutions with different pH levels and short-term storage (14 days) at room temperature. Overall, it has been demonstrated that Ganoderic acid nanodispersions can be efficiently generated by combining ultrasound cavitation and solvent evaporation.

Due to the fact that abrasive media can deform flexibly in abrasive flow machining (AFM) making this method easily to polish the complex holes and the curved surface of the hard machining shapes. Although abrasive media dominate the polishing behavior in AFM process, the mechanism of the abrasive media are not easy to understand because of the high viscous gels, therefore, many finishing works need lots of time to design AFM process. Power laws of the abrasive gels is studied here to evaluate the non-Newtonian flow of abrasive gels in complex holes polishing firstly, at the mean time different abrasive gels are utilized to finish the complex holes to follow the results by non-Newtonian flow calculation. Moreover, traditional AFM has difficulty to achieve a uniform roughness of radial distribution in the complex holes polishing because of the non-uniform abrasive forces. So a helical passageway is proposed to create a multiple motion of abrasive medium and to obtain the even surface of the complex holes in AFM process.

In the implementation of thermal enhanced oil recovery (TEOR) techniques, the temperature impact on relative permeability in oil - water systems is of special concern. Hence, developing a fast and reliable tool to model the temperature effect on two-phase oil - water relative permeability is still a major challenge for precise studying and evaluation of TEOR processes. To reach the goal of this work, two promising soft-computing algorithms, namely Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP) were employed to develop reliable, accurate, simple and quick to use paradigms to predict the temperature dependency of relative permeability in oil - water systems (K_rw and K_ro). To do so, a large database encompassing wide-ranging temperatures and fluids/rock parameters, including oil and water viscosities, absolute permeability and water saturation, was considered to establish these correlations. Statistical results and graphical analyses disclosed the high degree of accuracy for the proposed correlations in emulating the experimental results. In addition, GEP based correlations were found to be the most consistent with root mean square error (RMSE) values of 0.0284 and 0.0636 for K_rw and K_ro, respectively. Lastly, the comparison of the performances of our correlations against those of the preexisting ones indicated the large superiority of the introduced correlations compared to previously published methods. The findings of this study can help for better understanding and studying the temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes.

Questions：SAP in mountainous cities of China serve as crucial carriers for species and wild food, constituting potential green infrastructure. These patches should be recognized as locally specific resources. This study investigates the transformation characteristics of the landscape patterns of SAP, along with their elements, across urban-rural gradients, providing foundational support for the planning of conservation and utilization strategies. Location: Yongchuan area of Chongqing in southwest China. Methods: The study investigated urban-rural gradient-based transformation of SAP. A four-direction transect space was selected and divided into uniform sample squares. Fragstats 4.3 calculated the landscape index of SAP, and Spearman correlation coefficients in SPSS were used to evaluate the influence of landform and artificial factors on agroforestry patch transformation. Results: 1) Patch area (PA), Plaque density (PD), and CLUMPY of SAP systematically transform, showing increased SAP areas and higher connectivity with greater distance from the city, resulting in reduced patch fragmentation. However, Edge density (ED) and Largest plaque index (LPI) exhibit irregular transformation patterns. 2) With increased distance from the city, woodland patch area and connectivity increase, leading to decreased fragmentation, dispersal, and lower fractal dimensions. Conversely, Cultivated land follows the opposite trend. garden land patch area, fractal dimension, fragmentation, and dispersal all increase, while connectivity decreases. 3) Landscape pattern transformations of SPA are significantly influenced by artificial factors. Woodland patches are influenced similarly by landform and artificial factors. In contrast, cultivated land and garden land patches are more significantly impacted by artificial factors. Conclusions: SAP exhibit differentiated conservation and utilization measures across urban-rural gradients. Small patches located far from urban centers may benefit from systematic conservation measures to prevent further fragmentation and erosion in peri-urban areas.

To decrease privacy risks, consumers may choose to misrepresent when they are asked to offer personal information. This paper examines the impact of consumer misrepresentation on a monopolistic firm and consumers with an economic model. The results show that consumer misrepresentation may benefit the firm but hurt consumers. In addition, consumers misrepresentation may encourage the firm to provide higher personalized service level in certain scenarios, such as, when the unit cost of personalized service is low. Thirdly, when consumers misrepresent, and the firm only covers part of the market, a greater unit value of consumer’s privacy information will reduce the firm’s profit, but a greater unit cost of personalized service increases the firm’s profit.

The key goal of the current study was to determine suitable areas of water pumping for drinking and agricultural harvest in Tabriz aquifer, located in East Azerbaijan province, northwest Iran. In the study area, groundwater is the key foundation of water for drinking and farming requirements. Groundwater compatibility study was conducted by analyzing Electrical conductivity (EC), Total dissolved solids (TDS), Chloride (Cl), Calcium (Ca), Magnesium (Mg), Sodium (Na), Potassium (K), Sulfate (SO4), Total hardness (TH), Bicarbonate (HCO3), pH, carbonate (CO3) and Sodium Adsorption Ratio (SAR) obtained from 39 wells in the period of 2003 to 2014. For this purpose, the Water Quality Index (WQI) and irrigation water quality (IWQ) index were utilized. The WQI index zoning exposed that the groundwater of the study area for drinking purposes is categorized as excellent, good and poor water. Most drinking water harvested for urban and rural areas are in the class of 'excellent water'. The results revealed that about 37 percent (296 km2) of groundwater has high compatibility, and 63 percent of the study area (495 km2) has average compatibility for agricultural purposes. The trend of IWQ and WQI indexes demonstrates that the groundwater is getting worse over the time.

Oil-Gan, also known as emblica, is the fruit of the genus Phyllanthus emblica L. The fruits of these trees are high in nutrients and have excellent effects on health care and development values. There are many methods for the management of diabetic nephropathy (DN). However, there is a lack of effective drugs for treating DN from throughout the disease course. The primary aim of this study was to examine the protective effects (including urine analyses, the measurement of inflammatory cytokine levels, and histological examination) and mechanisms of action of an ethyl acetate extract of P. emblica (EPE) on db/db mice, an animal model of diabetic nephropathy, as well as a secondary aim of examining the expression levels of protein kinase Cα (PKCα) in the kidney and its downregulation of vascular endothelial growth factor (VEGF) and fibrosis gene transforming growth factor-β1 (TGF-β1) by Western blot analyses. Eight db/m C57BLKS/J mice were used as the control group. Forty db/db mice were randomly divided into five groups. Treatments included vehicle, EPE1, EPE2, EPE3 (at doses of 100, 200, or 400 mg/kg EPE), or the comparative drug aminoguanidine for 8 weeks. After 8 weeks of treatment, blood samples were collected for analyses of blood glucose, insulin, and HbA1c levels, and pancreatic and kidney tissues were dissected for histological analyses. The present study indicated that the administration of EPE to db/db mice effectively controlled hyperglycemia and hyperinsulinemia by markedly lowering blood glucose, insulin, and glycosylated HbA1c levels. Administration of EPE to db/db mice decreased the levels of BUN and creatinine both in blood and urine and reduced urinary albumin excretion and the albumin creatine ratio (UACR) in urine. Moreover, EPE treatment decreased the blood levels of inflammatory cytokines, including kidney injury molecule-1 (KIM-1), C-reactive protein (CRP), and NLR family pyrin domain containing (NLRP3). Our findings showed that EPE not only had antihyperglycemic effects but also improved renal function in db/db mice. Histological examination of the kidney by immunohistochemistry indicated that EPE can improve kidney function by ameliorating glomerular morphological damage following glomerular injury; alleviating proteinuria by upregulating the expression of nephrin, a biomarker of early glomerular damage; and inhibiting glomerular expansion and tubular fibrosis. Moreover, the administration of EPE to db/db mice increased the expression levels of p- PKCα/t-PKCα but decreased the expression levels of VEGF and renal fibrosis biomarkers (TGF-β1, collagen IV, p-Smad2, p-Smad3, and Smad4), as shown by Western blot analyses. These results implied that EPE as a supplement has a protective effect against renal dysfunction through the amelioration of insulin resistance as well as the suppression of nephritis and fibrosis in a DN model.

Multimodal remote sensing data classification can enhance the model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks is the focus of researchers. Inspired by human feedback learning mechanisms, causal reasoning mechanisms, and knowledge induction mechanisms, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposed the causal meta-reinforcement learning (CMRL). First, based on feedback learning mechanisms, we have overcome the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we help them understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information. Second, based on the causal inference mechanism we designed causal distribution prediction action, classification reward, and causal intervention reward, capturing pure causal factors in multimodal data and cutting off false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets show that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

Excretory-secretory products (ESPs) are the main research targets for investigating the hosts and helminths interaction. Parasitic worms can migrate to parasitic sites and avoid the host immune response by secreting this product. Angiostrongylus cantonensisis an important food-borne zoonotic parasite that causes severe neuropathological damage and symptoms, including eosinophilic meningitis or meningoencephalitis in humans. Benzaldehydes are organic compounds composed of a benzene ring and formyl substituents. This compound has anti-inflammatory and antioxidation properties. Previous studies showed that 3-hydroxybenzaldehyde (3-HBA) and 4-hydroxybenzaldehyde (4-HBA) can reduce apoptosis in A. cantonensis ESPs treated astrocytes. These results on the protective effect underlying benzaldehyde have primarily focused on cell survival. The study was designed to investigate the molecular mechanisms of endoplasmic reticulum stress (ER stress) and oxidative stress in astrocytes in A. cantonensis ESPs treated astrocytes and to evaluate the therapeutic consequent of 3-HBA and 4-HBA. First, we initially established the RNA-seq dataset in each group, including Normal, ESPs, ESPs+3-HBA, and ESPs+4-HBA. We also found that benzaldehyde (3-HBA and 4-HBA) can stimulate astrocytes to express ER stress-related molecules after ESP treatment. The level of oxidative stress could also be decreased in astrocytes by elevating antioxidant activity and reducing ROS generation. These results suggested that benzaldehyde may be a potential therapeutic compound for human angiostrongyliasis to support brain cell survival by inducing the expression levels of ER stress- and oxidative stress-related pathway.

The International Roughness Index (IRI) is the standard scale for evaluating road roughness in many countries in the world. The Taipei City government actively promotes a Road Smoothing Project and plans to complete the rehabilitation of the main and minor roads within its jurisdiction. This study aims to detect the road surface roughness in Taipei City and recommend appropriate IRI thresholds for road rehabilitation. A total of 171 asphalt concrete pavement sections in Taipei City with a total length of 803.49 km were analyzed and compared by IRI. The longitudinal profile of the detected road sections was measured using an inertial profiler. The statistical analysis showed that the IRI value prior to road leveling was mainly distributed between 5 and 8 m/km, while the IRI value after road leveling was mainly distributed between 3 and 4.5 m/km. This confirms that the implementation of the Road Smoothing Project has a significant effect on improving road smoothness. Moreover, based on the analysis results, it is recommended that the IRI threshold value for road rehabilitation in Taipei City be set at 4.50 m/km.

Farmers and seed companies constantly require high-quality seeds with excellent agronomic performance. However, faced with environmental adversity, limited natural resources and increasing food demand around the globe, more attention has turned to improving crop plant production by implementing efficient strategies. Seed priming technology has shown promising biological improvements leading to suitable agronomic performance in crop plants under adverse environmental conditions. Seeds are subjected to controlled conditions that are conducive to complex physiological, biochemical, and molecular changes, conferring specific stress tolerance to subsequent germination and growth conditions. In this review paper, we aimed to study the recent approaches in the efficiency of hydropriming, osmopriming, chemopriming, hormopriming, nanopriming, matrix priming, biopriming, physical priming and hybrid priming procedures in the production of crop plants under environmental adversity, as well as their biological mechanism changes. All priming methods demonstrated relevant changes in the biological mechanism related to crop plant production by mitigating salinity effects, heavy metals, and flooding stress and enhancing chilling, heat, drought and phytopathogen tolerance. We strongly recommend that researchers combine multiple priming methods, known as hybrid priming, in their investigations to provide novel technologies and additional biological approaches to enhance the knowledge of crop plant science. Thus, the findings shed light on the use of seed priming technology as a key strategy to increase crop plant production under environmental adversity by acquiring stress tolerance and enhancing agronomic traits to meet the global food demand.

This paper reviews the requirements for the production, quality, and quality assurance of solid recovered fuels (SRF) that are increasingly used for the substitution of fossil fuels in the indus-trial utilities. The economical, technological and environmental aspects should be considered before using SRF as an alternative fuel. In general, the most important factors determining the commercial production of SRF referred to the calorific values and their available amounts adopted in the starting materials. Furthermore, the low-melting impurities and undesired constituents/elements, including chlorine, sulfur, potassium and sodium, may pose negative impacts on air pollutant emission and slagging & fouling in the process utilities. In this regard, the vent emitted from the industrial utilities must comply with the effluent standards of station-ary sources like particulate, sulfur oxides, nitrogen oxides, heavy metals (e.g., lead, cadmium, and mercury) and dioxins. For these reasons, this work will summarize the legal or regulatory requirements under development in Asian countries (i.e., Japan, South Korea, and Taiwan) and European Union (EU). Finally, the outlook of solid recovered fuel (SRF) in the waste manage-ment and industrial sectors will be addressed to echo the sustainable development goals (SDGs).

A typhoon-induced storm surge is considered one of the most severe coastal disasters in Taiwan. However, the combination of the storm surge and the astronomical tide called the storm tide can actually cause extreme flooding in coastal areas. This study implemented a two-dimensional hydrodynamic model to account for the interaction between tides and storm surges on the coast of Taiwan. The model was validated with observed water levels at Sauo Fish Port, Hualien Port, and Chenggong Fish Port under different historical typhoon events. The model results are in reasonable agreement with the measured data. The validated model was then used to evaluate the effects of the typhoon's intensity, bathymetric change, and the combination of the typhoon’s intensity and bathymetric change on the maximum storm tide and its distribution along the east coast of Taiwan. The results indicated that the maximum storm tide rises to 1.92 m under a typhoon with an intensity of a 100-year return period. The maximum storm tide increased from a baseline of 1.26 m to 2.63 m for a 90% bathymetric rise at Sauo Fish Port under the conditions of Typhoon Jangmi (2008). The combination of the intensity of a typhoon with a 100-year return period and a 90% bathymetric rise will result in a maximum storm tide exceeding 4 m, 2 m, and 3 m at Sauo Fish Port, Hualien Port, and Chenggong Fish Port, respectively. We also found that the distribution of the maximum storm tide on the east coast of Taiwan can expand significantly subject to the bathymetric rise.

In this work, optimal consumption and investment strategies are derived for risk-averse investors under the 4/2 stochastic volatility class of models. We work under an expected utility (EUT) framework and consider a Constant Relative Risk Aversion (CRRA) investor, who might also be ambiguity-averse. The corresponding Hamilton-Jacobi-Bellman (HJB) and HJB-Isaacs (HJBI) equations are solved in closed-form for a subset of the parametric space and under some restrictions on the portfolio setting, for complete markets. Conditions for proper changes of measure and well defined solutions are provided. These are the first analytical solutions for the 4/2 stochastic volatility model and the embedded 3/2 model for the type of excess returns established in .

Among the different applicable irrigants for root canal disinfection, sodium hypochlorite 5.25% is one of the most attractive ones. The quality of root canal disinfection is dependent on some factors such as the employed approach, type of flow rate of irrigant and the size of needle. The majority of studies in the field of root canal disinfection are experimentally carried out. In the current article, Computation Fluid Dynamic (CFD) is used for modeling the antimicrobial liquid flow in the root canal and evaluate the effects of needle size and flow rate. Two needles, G28 and G30, are used for irrigation in three volumetric rates of flow including 0.10 mL⁄s , 0.20 mL⁄s and 0.30 mL⁄s. The results of numerical simulations revealed the improved quality of root canal disinfection by augmentation in the rate of flow and decrease in the inner diameter of the needle. According to the outcomes of the modeling, the highest average wall shear stress obtained in the case of using G28 needle and 30 mL⁄s flow rate, which was approximately 10.21 Pa.

Infectious keratitis (IK) represents a major cause of corneal blindness. This study aims to investigate the demographics, risk factors, microbiological characteristics and antibiotic susceptibility patterns of IK in Taiwan over the past 15 years. A retrospective population-based study was conducted using the Chang Gung Research Database. Patients with IK were identified by diagnostic codes for corneal ulcer from 2004 to 2019. Of 7807 included subjects, 45.2% of patients had positive corneal cultures. The proportion of contact lens-related IK declined, while that of IK related to systemic diseases grew. The percentage of isolated gram-positive bacteria surpassed that of gram-negative bacteria in the 15-year period. The prevalence of Pseudomonas aeruginosa showed a decreasing trend (p = 0.004), whereas coagulase-negative Staphylococcus (CNS) and Propionibacterium species were increasingly detected (p &lt; 0.001). Overall, the trend of antibiotic susceptibility of both gram-positive and gram-negative bacteria did not change throughout the study period. The susceptibility to the test antibiotics maintained over 90% in gram-negative isolates during 15 years. Vancomycin preserved 100% susceptibility to all gram-positive isolates. Since most tested antibiotics exhibited stable susceptibility over decades, this study reinforced that fluoroquinolones and fortified vancomycin continue to be good empiric therapies for treating bacterial keratitis in Taiwan.

The morphological and compositional complexities of keratinized components make feathers ingenious skin appendages adapted to diverse ecological needs. Frizzling feathers, characterized by their distinct curling phenotypes, offer a unique model to explore the intricate morphogenesis in developing a keratin-based bioarchitecture over a wide range of morphospace. Here, we investigated the heterogeneous allocation of α- and β-keratins in flight feather shafts of homozygous and heterozygous frizzle chickens by analyzing the medulla-cortex integrations using quantitative morphology characterizations across scales. Our results reveal intriguing construction of frizzling feather shaft through modified medulla development, leading to a perturbed balance of the internal biomechanics and, therefore, introducing the inherent natural frizzling compared to those from wild-type chickens. We elucidate how the localized developmental suppression of the α-keratin in the medulla interferes with the growth of the hierarchical keratin organization by changing the internal stress in the frizzling feather shaft. This research not only offers insights into the morphogenetic origin of the inherent bending of frizzling feathers but also facilitates our in-depth understanding of the developmental strategies toward the diverse integuments adapted for ecological needs.

The diffusion of molecules in aqueous solutions in the domain of membrane technology is very critical in the efficiency of chemical engineering and purification processes. In this study, the diffusion in high and low concentration regions is simulated with finite difference method (FDM), and then the results of numerical computations are coupled with adaptive network-based fuzzy inference system (ANFIS) and bilayered neural network method (BNNM). Machine learning approach can individually predict diffusion phenomena across the domain based on understanding of the machine instead of the discretization of an ordinary differential equation (ODE). The findings of the machine learning method are in good agreement with those of FDM at different times of the simulation. In addition to numerical computation, the error of the system is computed for different iterations. The results show that by increasing the number of iterations and training datasets, all errors reduce significantly for both training and testing. BNN method is also used to train the prediction process of diffusion for a more accurate comparison. This technique is similar to ANFIS method in terms of prediction capability. According to the findings, ANFIS approach predicts diffusion slightly better than BNN method. In this regard, ANFIS technique produces R>0.99 while BNN method produces R around 0.98. Both machine learning methods are accurate enough to predict diffusion throughout the domain for different time steps. The computational time for both algorithms is less than that of FDM method to predict low and high concentrations in the domain. Besides, based on the results, artificial intelligence (AI) can find the relationship between inputs and outputs and determine which input has the main influence on the output in this study to optimize the process. As such, future studies can be focused on AI and other methods for faster prediction and optimization processes.

There is a current worldwide outbreak of a new type of coronavirus (2019-nCoV), which originated from China and has now spread to 17 other countries. Governments are under increase pressure to stop the outbreak spiraling into a global health emergency. At this stage, preparedness, transparency and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak sites, and from laboratories supporting the investigation. This paper aggregates and consolidates the virology, epidemiology, clinical management strategies from both English and Chinese literature, official news channels and other official government documents. In addition, by fitting the number of infections with a single-term exponential model, we report that the infection is spreading at an exponential rate, with a doubling period of 1.8 days.

The utilization of optical antennas for active control of far-field radiation at the subwavelength scale is crucial in various scientific and technological applications. We propose a thermally tunable disk design of indium tin oxide (ITO) and aluminum gallium nitride (Al0.18Ga0.82As), enabling a switch between absorption and scattering. Furthermore, the control of far-field radiation pattern can be easily realized by combining ITO and Al0.18Ga0.82As to enhance or suppress emission. Our results demonstrate that hybrid structure can be dynamically tuned with temperature variations. In the proposed design, a frequency is achieved at the epsilon-near-zero wavelength of 1240 nm. The thermal tunability of hybrid structure introduces new multifunctional possibilities for light manipulation, thereby enhancing the potential applications of new devices in the near-infrared range.

Isokinetic testing of the knee joint is the “gold standard” in strength assessment in sport especially in soccer, where it is part of pre-season screening to establish norms in case of injuries. There are many devices on the market that make it difficult to use of normative data. Isokinetics literature in females’ soccer players is scarce in comparison to males. Furthermore, evaluation protocols strongly differ from each other, using different ranges of speeds, movement patterns and output data. Thus, it is difficult for the researcher and the clinician to choose a correct protocol for isokinetic evaluation in female soccer players. The indexes of isokinetic performance that are widely accepted are the hamstring/quadriceps (H/Q) ratio and dominant/non-dominant (D/ND) ratio as reference. In this review are highlighted some common features of isokinetic testing in female soccer players of different levels of qualification. From this study, it emerges that, for a reliable test, 3 reps at the speed of 60°/sec in knee flexion/extension is preferable due to the high reliability and that an optimal H/Q ratio in healthy female soccer players of different level of qualification and age is 50%, while the interlimb differences (D/ND) should be below 10%.

Predicting the outcome of a future game between two National Basketball Association (NBA) teams poses a challenging problem of interest to statistical scientists as well as the general public. In this article, we formalize the problem of predicting the game results as a classification problem and apply the principle of maximum entropy to construct NBA maximum entropy (NBAME) model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs by the NBAME model. The best NBAME model is able to correctly predict the winning team 74.4 percent of the time as compared to some other machine learning algorithms which is correct 69.3 percent of the time.

[Introduction]: In the contemporary international financial environment, marked by intricate complexities and increasing uncertainties, the impact of exchange rate fluctuations on national economies has become increasingly significant. In this context, this study uses the Chinese economy as a case study to delve deeply into the interaction effects between macroprudential and monetary policies, analyzing how these policies jointly address economic instability. The aim is to offer new perspectives and strategies for stabilizing the global economic and financial landscape. [Methods]: This paper constructs an open-economy Dynamic Stochastic General Equilibrium (DSGE) model specifically tailored for China, incorporating Bayesian estimation and numerical simulation techniques. The study meticulously examines how macroprudential and monetary policies can be coordinated to achieve economic stability in response to exchange rate shocks. [Results]: The research finds that (1) targeted coordination of macroprudential and monetary policies towards asset prices and price levels can maintain stability under exchange rate shocks; (2) the effective implementation of such coordinated policies not only mitigates the fluctuations in asset prices due to exchange rate shocks but also enhances the level of societal welfare; (3) in terms of choosing an exchange rate regime, a managed floating rate system can prevent policy overlaps and conflicts that might occur in a floating rate system during the coordination of macroprudential and monetary policies, thus offering comparative advantages. [Discussion]: The findings underscore the importance of policy integration in a dynamic and complex global economic environment. They provide policy recommendations for China and other emerging market countries on how to effectively maintain economic stability through the synergistic interplay of macroprudential and monetary policies amid fluctuations in exchange rates and asset prices.

In this paper we overview all analytical engineering solutions for corrosion problems in pressure vessels delivered over the past 60 years. We briefly detail strengths and weaknesses of existing approaches, thus demonstrating the need for novel uniform corrosion analysis methods for both current and new applications. To complement the review, we also present a new analytical model allowing to estimate the lifetime of pressured elastic vessels with mechanically assisted corrosion. Both internal/external pressure and internal/external corrosion are captured and dissolution driven corrosion is also considered. The readily implemented method improves existing analytical approaches and is shown to be effective for thin and thick shells, as well as various loading and corrosion intensity and geometrical conditions.

Climate change impacts and adaptations is subject to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The results of this study revealed that climate change does not affect the wind climate over the study area, remarkably. However, a small decrease was projected for future simulation revealing a slightly decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution when winter and summer have the highest values of power. The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.

During the past two decades of e-commerce growth, the concept of a business model has become increasingly popular. More recently, the research on this realm has grown rapidly, with diverse research activity covering a wide range of application areas. Considering the sustainable development goals, the innovative business models have brought a competitive advantage to improve the sustainability performance of organizations. The concept of the sustainable business model describes the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural, or other contexts, in a sustainable way. The process of sustainable business model construction forms an innovative part of a business strategy. Different industries and businesses have utilized sustainable business models’ concept to satisfy their economic, environmental, and social goals simultaneously. However, the success, popularity, and progress of sustainable business models in different application domains are not clear. To explore this issue, this research provides a comprehensive review of sustainable business models literature in various application areas. Notable sustainable business models are identified and further classified in fourteen unique categories, and in every category, the progress -either failure or success- has been reviewed, and the research gaps are discussed. Taxonomy of the applications includes innovation, management and marketing, entrepreneurship, energy, fashion, healthcare, agri-food, supply chain management, circular economy, developing countries, engineering, construction and real estate, mobility and transportation, and hospitality. The key contribution of this study is that it provides an insight into the state of the art of sustainable business models in various application areas and future research directions. This paper concludes that popularity and the success rate of sustainable business models in all application domains have been increased along with the increasing use of advanced technologies.

Intermetallic compounds are increasingly being expected to utilize in tribological environments, but to date the works are hindered by insufficient ductility at low and medium temperature. This paper presents a novel multiphase intermetallic alloy with the chemical composition of Mo-40Ni-13Si (at.%). Microstructure characterization reveals that a certain amount of ductile Mo phases formed during the solidification process of ternary Mo-Ni-Si alloy melt, which is undoubtedly beneficial to the ductility improvement of intermetallic alloy. Tribological properties of the designed alloy, including wear resistance, friction coefficient and metallic tribological compatibility, were evaluated under dry sliding wear test conditions at room temperature. Results suggested that the multiphase alloy possesses an excellent property for room-temperature wear applications, which is attributed to the unique microstructural features and thereby good combination in hardness and ductility. The corresponding wear mechanism is also reported by observing the worn surface, subsurface and wear debris of alloy, which to be found is soft abrasive wear.

Microvascular self-healing composite materials have significant potential for application and their mechanical properties need in-depth investigation. In this paper, tensile and compressive properties of woven fabric carbon fiber reinforced polymers (CFRP) laminates containing three-dimensional microvascular channels were investigated experimentally. Several detailed finite element (FE) models were established to simulate the mechanical behavior of the laminate and the effectiveness of different models were examined. The damage propagation process of the microvascular laminates and the influence of microvascular parameters were studied by the validated models. The results show that microvascular channels arranged along the thickness direction (z-direction) of the laminates are critical locations under the loads. The channels have minimal effect on the stiffness of the laminates but causing a certain reduction in strength, which varies approximately linearly with the z-direction channel diameter within its common design range of 0.1~1mm. It is necessary to consider the resin-rich region formed around microvascular channels in the warp and weft fiber yarns of the woven fabric composite when establishing the FE model. And the layers in model should be assigned with equivalent unidirectional ply material in order to calculate the mechanical properties of laminates correctly.

We present three reduced integrable hierarchies of nonlocal integrable NLS type equations from a vector integrable hierarchy associated with a matrix Lie algebra, not being A type. Three similarity transformations are taken to keep the invariance of the transformed zero curvature equations. The key step is to formulate a solution to a reduced stationary zero curvature equation so that the zero curvature formulation works for a reduced case.

A feeding study was conducted to evaluate the different diets supplemented with vitamin solution effect on broodstock reproduction, growth performances, and the survival rates of larvae and fry of Pangasius catfish during over-spawning season. The experiment of broodstock cultivation was set up as a diet supplement design with six different diets consisting of vitamin solution, each diet fed to triplicate groups of fish, the stocking density of 10 broodstock fish (07 females and 03 males) per hapa net. Fish larvae and fry were reared in the hatchery for 45 days after hatching to evaluate growth performances and survival rates of fish fingerling. The growth and reproductive performances of breeders fed with different test diets showed that there were differences among the six tested diets (P < 0.05). The highest final body weight (FBW), weight gain (WG), daily weight gain (DWG), specific growth rate (SGR) of broodstock and survival rate of Pangasius fingerlings were found in Treatment 5, which contained 0.6% H-OVN for Pangasius Broodstock mixed with 12.6% algal oil and Treatment 3, which contained 0.6% vitamin premix H-OVN for Pangasius Broodstock compared to other Treatments. With these test diets, the Gonadosomatic index (GSI), relative fecundity index (RFI), fertilized egg, hatching rates of eggs, and survival rate of fingerling ranged between a 6.7–12.8%, 104,267–199,512 eggs/kg, 62.2–93.6%, 43.5–76.2%, and 45.3–66.3%, respectively (P < 0.05). The findings of this study showed that the diet containing 35% CP contents supplemented with 0.6% vitamin premix H-OVN mixed with algal oils showed the highest results in terms of growth, reproductive performance indices, and survival rates of Pangasius catfish fingerling.

We employed the Riemannian Face Manifold (RFM) as an alternative approach to the conventional linear Euclidean space for explaining the atypicality bias in face likeliness judgments. The RFM posits that the mental representation of faces is better captured as a manifold of stable states, accounting for the nonlinearity in the physical properties of faces. To examine the relationship between physical and psychological distance of morph and typical/atypical faces, we manipulated the parameter k and incorporated a weight function in the Riemannian metric. Our results indicate that the psychological distance between the morph and typical face was longer than that between the morph and atypical face, consistent with prior research on the atypicality bias in perceptual similarity. The RFM approach provides mathematical support and is a powerful tool for studying face perception and recognition, offering potential implications for explaining the other-race effect. Future research can utilize the RFM to investigate individual differences in face recognition abilities and expertise, and how they impact psychological distance and face processing.

Nanomedicine holds great potential to devise better drug delivery systems (DDSs). However, many reported nanomedicines still fall short of commercial requirements including specific targetability, scale-up manufacturing and safety. Cell/tissue based carriers, including cell membrane vehicle and exosome, are biocompatible and targeting platforms but usually suffered from low yields and unstable reproducibility. Here in this study, we proposed the concept and preparation of reconstituted lipid nanoparticles (rLNPs) to develop highly reproducible cell/tissue based lipid nanoparticles (LNPs) for drug delivery, which holds the potential as a versatile drug delivery platform. The whole lipids of cell or tissue were firstly extracted and then prepared into rLNPs using solvent diffusion method. In this way, the preparation of ultra-small (~20 nm) rLNPs can be easily applied to both cell (mouse breast cancer cell line, 4T1) and tissue (mouse liver tissue). Our results demonstrated that mouse liver tissue derived rLNPs can be further labeled/modified with imaging, targeting or other functional moieties. Furthermore, rLNPs were highly biocompatible and capable of loading different drugs including doxorubicin hydrochloride (Dox) and curcumin (Cur). Most importantly, Dox loaded rLNPs (rLNPs/Dox) showed preferable in vitro and in vivo anticancer performance. Therefore, rLNPs might be a versatile drug delivery platform for future application in the treatment of a variety of diseases.

This article analyzes in detail the influence of Jupiter on the measurement of the gravitational constant. Including a specific estimate of the change in the gravitational constant caused by the change of the earth's orbital position every half month. The influence of Jupiter's perihelion and aphelion on the gravitational constant is also estimated. On this basis, this article analyzes the data with specific time records such as BIPM-01, BIPM-14, JILA-10, UCI-14, HUST-09. The analysis results show that after considering the influence of Jupiter, the experimental results have been significantly improved.

Restricted Boltzmann machines (RBMs) are the building blocks of some deep learning networks. However, despite their importance, it is our perception that some very important derivations about the RBM are missing in the literature, and a beginner may feel RBM very hard to understand. We provide here these missing derivations. We cover the classic Bernoulli-Bernoulli RBM and the Gaussian-Bernoulli RBM, but leave out the ``continuous'' RBM as it is believed not as mature as the former two. This tutorial can be used as a companion or complement to the famous RBM paper ``Training restricted Boltzmann machines: An introduction'' by Fisher and Igel.

A portable Fourier Transform Spectrometer (B3M-IR) is built and used to measure atmospheric trace gases in the city of Beijing during Olympic Games in 2008. A short description of the instrument is first provided in this paper. A detailed spectral analysis is then presented. The total columns of ozone (O3), carbon monoxide (CO), methane (CH4) and nitrous oxide (N2O) are retrieved from the ground-based solar absorption spectra recorded by the B3M-IR during the Olympic Games. Lacking validation data, only the retrieved total column of O3 is compared with that retrieved by MAX-DOAS, which is deployed at the same station. The mean difference between the two methods of measurement is 6.5%, demonstrating the performance and reliability of B3M-IR.

In this paper we present the exploration of indoor positioning technologies for UAVs, and navigation techniques for path planning and obstacle avoidance. The objective is to perform warehouse inventory tasks using a drone to search for barcodes or markers to identify the objects. For the indoor positioning techniques, we employ Visual-Inertial Odometry (VIO), Ultra-Wideband (UWB), AprilTag fiducial markers, and Simultaneous Localization and Mapping (SLAM). These algorithms encompass global positioning, local positioning, and pre-mapping positioning, comparing the merits and drawbacks of various techniques and trajectories. In UAV navigation, we combine SLAM-based RTAB-Map indoor mapping and navigation path planning of the ROS for indoor environments. This system enables precise drone positioning indoors and utilizes global and local path planners to generate flight paths that avoid dynamic, static, unknown, and known obstacles, demonstrating high practicality and feasibility. To achieve the warehouse inventory inspection, a reinforcement learning approach is proposed to recognize the markers by adjusting the UAV's viewpoint. We address several main problems in inventory management, including efficiently planning paths while ensuring a certain level of detection rate. Two reinforcement learning techniques, AC (Actor-Critic) and PPO (Proximal Policy Optimization), are implemented based on AprilTag identification. Testing is performed in both simulated and real-world environments, and the effectiveness of the proposed method is validated.Code is available at https://github.com/kellen080/Navigation and https://github.com/kellen080/Indoor\_Positioning.

Blisks subjected to rough machining for channel creation must undergo finishing processes, and such processes must achieve the required tolerance limits. A high-quality surface finish and predictable long tool life are critical for the finish milling of blisks. Accordingly, the aim of this study was to optimize parameters for the finish machining of an Inconel 718 alloy monolithic blisk. Ball-cone mills were used to machine the blade surface at a constant depth. A sensory tool holder was used to collect cutting force signals during machining, and a digital microscope was used to examine tool wear. The surface texture measuring instrument was used to measure blisk blade surface roughness to evaluate processing quality. This study manipulated two cutting parameters, namely cutting speed and feed per tooth, and investigated their effects. The relationship between cutting conditions and machining efficiency was analyzed. According to the experimental results, we identified a set of optimal parameters for marginal tool wear and fast cutting speeds and then estimated the corresponding tool life by using the derived parameters.

As customer demand for green products increases in the digital economic era, this study analyses the green innovation of the e-tailer and supplier in drop shipping models. Moreover, drop shipping e-tailers and suppliers with a drop shipping option need to make choices regarding whether to provide green or normal products to the market. When a supplier with a drop shipping option produces green products, more fees may be invested on the production of green products than on normal products. The drop shipping e-tailers and suppliers with a drop shipping option can also choose to sell normal products at a low cost, as before. This study designs four models of drop shipping e-teilers and suppliers with a drop shipping option under different choices, analyzes their operational process in drop shipping models, and investigates five theorems. The optimal pricings decisions and green degree of drop shipping e-tailers and suppliers with a drop shipping option were evaluated in this study. The impacts of the green innovation factor, green elasticity coefficient, manufacturing and distribution costs on the drop shipping e-tailers and suppliers with a drop shipping option, and the effect of other environmental parameters on the green degree of green products are also analyzed through computer simulation. The findings of the simulation analysis provide valuable guidance for e-tailers and suppliers with green innovation in drop shipping models and offer important academic and practical implications for e-commerce and the digital economy.

Consumption of refined foods and beverages high in sugar make the teeth susceptible to the formation of biofilm, and lead to dental caries and diseases of the oral cavity such as periodontitis and periodontal disease. The aim of the present study was to determine the ability of selected probiotics to inhibit growth and biofilm formation by the cariogenic bacterium Streptococcus mutans in vitro. We screened strains of latic acid bacteria (LAB) (n=120) from the Bioresources Collection and Research Center (BCRC), saliva of healthy adults and infant stool. The antimicrobial activity of LAB in vitro was evaluated by agar spot culture and co-culture of the S. mutans strains. We determined the effect of heating and dilution factors (2- and 4-fold) on the antagonistic activity of LAB. Antagonistic substances in the spent culture suspensions (SCS) of LAB were precipitated by extraction with ammonium sulphate and chloroform to characterise the protein and lipophilic fractions. Results of co-culturing show that the SCS of the three LAB strains (Lactobacillus pentosus 13-1, 13-4 and Lactobacillus crispatus BCRC 14618) subjected to heat treatment showed significantly high antimicrobial activity. We found that substances produced by L. pentosus 13-4 which have the potential to exhibit antimicrobial properties might be lipophilic proteins. Additionally, we infer that the mechanism of reducing biofilm formation by Lactobacillus strains is associated with sucrose-dependent cell–cell adhesion and the gtfC level of glucosyltransferases (Gtfs) in the biofilm. Native LAB strains screened in our study may be used in chewing gums and other processed foods for preventing tooth decay.

As the leader of the largest economy, President of the United States has substantive influence on addressing the global climate change problem. However, presidential election is often dominated by issues other than energy problems. This paper focuses on the on-going 2016 presidential election, examining the energy plans proposed by the leading Democrat and Republican candidates. Our data from the Iowa caucus survey in January 2016 suggests that voters are more concerned about terrorism and economic issues than environmental relative issues. We then compare the Democratic and Republican candidate’s view of American’s energy future, and evaluate their proposed renewable energy targets. We find that the view on renewable energy is polarized between Democratic and Republican candidates, while candidates from both parties agree on the need for energy efficiency. Results from our ordinal least squares regression models suggest that Democratic candidates have moderate to ambitious goals for developing solar and other renewable energy. The Republican candidates favor fossil fuel and they neglect to provide any plan for renewable energy. In addition, this trend of polarization has grown more significant when compared with the past three presidential elections. Our observation suggests that energy issues need to be discussed more to draw broader attention to salient issues of diversifying and decarbonizing the nation’s energy system.

Diastolic dysfunction, a prevalent condition characterized by impaired relaxation and filling of the left ventricle, significantly contributes to heart failure with preserved ejection fraction (HFpEF). Galectin-3, a β-galactoside-binding lectin, has garnered attention as a potential biomarker and mediator of fibrosis and inflammation in cardiovascular diseases. This comprehensive review investigates the impact of galectin-3 on diastolic dysfunction. We explore its molecular mechanisms, including its involvement in cellular signaling pathways and interaction with components of the extracellular matrix. Evidence from both animal models and clinical studies elucidates galectin-3's role in cardiac remodeling, inflammation, and fibrosis, shedding light on the underlying pathophysiology of diastolic dysfunction. Additionally, we examine the diagnostic and therapeutic implications of galectin-3 in diastolic dysfunction, emphasizing its potential as both a biomarker and a therapeutic target. This review underscores the significance of comprehending galectin-3's role in diastolic dysfunction and its promise in enhancing diagnosis and treatment approaches for HFpEF patients.

A co-curing resin system consisting of 9368 epoxy resin for prepreg and 6808 epoxy resin for resin transfer molding (RTM) was developed. A corresponding preparation method of novel polymer composite bolted T-joint with internal skeleton and external skin was proposed based on prepreg-RTM co-curing process, and novel T-joints were fabricated. A series of conventional configuration T-joints based on RTM process and T-joints made of 2A12 aluminum alloy were prepared simultaneously. Bending performances were studied on these T-joints experimentally. The results indicate that 9368 epoxy resin and 6808 epoxy resin exhibit good compatibility in rheological and thermophysical properties. The novel T-joints prepared with prepreg-RTM co-curing process show no obvious fiber local winding or resin-rich regions inside, and the interface quality between internal skeleton and external skin is excellent. The main failure modes of the novel T-joint under bending load include separation of skin and skeleton and fracture along the thickness on the base panel; the skeleton carries the main bending load, but there is still load transfer between external skin and internal skeleton through their interface. The internal damages of the novel T-joint are highly consistent with surface damages observed visually, facilitating the detection and timely discovery of damages. The initial stiffness, damage initiation load, and ultimate load of the novel T-joint are 1.65 times, 5.89 times, and 3.45 times that of conventional T-joint, respectively. When considering the influence of density, the relative initial stiffness and relative ultimate load of the novel T-joint are 1.44 times and 2.07 times that of aluminum alloy T-joint, respectively.

A set of polymer composite bolted T-joints with novel configuration consisting of internal skeleton and external skin was fabricated using a prepreg-RTM co-curing molding process. Experiments were conducted to study their mechanical properties under bending load. A finite element model with a polymer resin area between the skin and skeleton was established and verified by the experimental results. Then, the damage propagation process and failure mechanism of the joint and the influence of three factors related to the layer characteristics of the skin and skeleton were investigated by the validated models. The results show that the bending stiffness and the yield limit load of the novel composite T-joint are 0.81 times and 1.65 times that of the 2A12 aluminum T-joint respectively, while at only 55.4% of its weight. The damage of the joint initiates within the resin area and leads to a degradation of the joint bending performances. The preferred stacking sequence of the skeleton is [0/+45/90/-45]ns when primarily subjected to bending loads. The decrease in bending performances is within 5% with the inclining angle of the skeleton less than 12 degrees. The more layers of 90° in the skin, the better bending performances of the joints, while the more layer of 0°, the poorer bending performances.

Estimates of extreme water level return periods in river systems are crucial for hydraulic engineering design and planning. Recorded historical water level data of Taiwan’s rivers are not long enough for traditional frequency analyses when predicting extreme water levels for different return periods. In this study, the integration of a one-dimensional flash flood routing hydrodynamic model with the Monte Carlo simulation was developed to predict extreme water levels in the Danshuei River system of northern Taiwan. The numerical model was calibrated and verified with observed water levels using four typhoon events. The results indicated a reasonable agreement between the model simulation and observation data. Seven parameters, including the astronomical tide and surge height at the mouth of the Danshuei River and the river discharge at five gauge stations, were adopted to calculate the joint probability and generate stochastic scenarios via the Monte Carlo simulation. The validated hydrodynamic model driven by the stochastic scenarios was then used to simulate extreme water levels for further frequency analysis. The design water level was estimated using different probability distributions in the frequency analysis at five stations. The design high-water levels for a 200-year return period at Guandu Bridge, Taipei Bridge, Hsin-Hai Bridge, Da-Zhi Bridge, and Chung-Cheng Bridge were 2.90 m, 5.13 m, 6.38 m, 6.05 m, and 9.94 m, respectively. The estimated design water levels plus the freeboard are proposed and recommended for further engineering design and planning.

Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

In the present paper, an aerodynamic investigation of a high-speed train is performed. In the first section of this article, a generic high-speed train against a turbulent flow is simulated, numerically. The Reynolds-Averaged Navier-Stokes (RANS) equations combined with the SST turbulence model are applied to solve incompressible turbulent flow around a high-speed train. Flow structure, velocity and pressure contours and streamlines at some typical wind directions are the most important results of this simulation. The maximum and minimum values are specified and discussed. Also, the pressure coefficient for some critical points on the train surface is evaluated. In the following, the wind direction influence the aerodynamic key parameters as drag, lift, and side forces at the mentioned wind directions are analyzed and compared. Moreover, the effects of velocity changes (50, 60, 70, 80 and 90 m/s) are estimated and compared on the above flow and aerodynamic parameters. In the second section of the paper, various data-driven methods including Gene Expression Programming (GEP), Gaussian Process Regression (GPR), and random forest (RF), are applied for predicting output parameters. So, drag, lift and side forces and also minimum and a maximum of pressure coefficients for mentioned wind directions and velocity are predicted and compared using statistical parameters. Obtained results indicated that RF in all coefficients of wind direction and most coefficients of free stream velocity provided the most accurate predictions. As a conclusion, RF may be recommended for the prediction of aerodynamic coefficients.

An immiscible blend of poly(hydroxy ether of bisphenol-A) (phenoxy) and poly(1,4-butylene terephthalate) (PBT) with phase separation was observed in as-blended samples. However, compatibilization of the phenoxy/PBT blends can be promoted through chemical exchange reactions of phenoxy with PBT upon annealing. In contrast to the as-blended samples, the annealed phenoxy/PBT blends had a homogeneous phase with a single T_g that could be enhanced by annealing at 260°C. Infrared (IR) spectroscopy demonstrated that phase homogenization could be promoted by annealing of the phenoxy/PBT blend, where alcoholytic exchange occurred between the dangling hydroxyl group in phenoxy and the carbonyl group in PBT in the heated blends. The alcoholysis reaction changes the aromatic linkages to aliphatic linkages in carbonyl groups, which initially led to the formation of a graft copolymer of phenoxy and PBT with an aliphatic/aliphatic carbonyl link. The progressive alcoholysis reaction resulted in the transformation of the initial homopolymers into block copolymers and finally into random copolymers, which promoted phase compatibilization in blends of phenoxy with PBT. Due to the fact that the amount of copolymers increased upon annealing, crystallization of PBT was inhibited by alcoholytic exchange in the blends.

This article addresses the trajectory tracking between two non-identical systems with chaotic properties. We employ the Rossler chaotic and RCL-shunted Josephson junctions model in similar phase space to study trajectory tracking. In order to achieve the goal tracking, we afford two stages to approximate the target tracking. The first stage utilizes the active control technique to transfer the output signal from the RCLs-J system into the quasi-Rossler system. Then next, the RCLs-J system employs the proposed the iterative learning control scheme and the control signal from the drive system to trace the trajectory of Rossler system. The numerical results demonstrate the proposed method and the tracking system is asymptotically stable.

Embryo stage of oxidation of an epi Ge(001)-2×1 by atomic oxygen and molecular O₂ is studied via synchrotron radiation photoemission. The topmost surface buckled with the up- and down-dimer atoms and the first subsurface layer behave distinctly from the bulk by exhibiting surface core-level shifts in the Ge 3d core-level spectrum. The O₂ molecules become dissociated upon reaching the epi Ge(001)-2×1 surface. One of the O atom removes off the up-dimer atom, and the other bonds with the underneath Ge atom in the subsurface layer. Atomic oxygen adsorbed on the epi Ge(001)-2×1 preferentially in between the up-dimer atoms and the underneath subsurface atoms without affecting the down-dimer atoms. The electronic environment of the O-affiliated Ge up-dimer atoms becomes similar to that of the down-dimer atoms. Both exhibit an enrichment in charge, where the subsurface of the Ge layer is maintained in a charge-deficient state. The dipole moment originally generated in the buckled reconstruction no longer exists, thereby resulting in a decrease in the ionization potential. The down-dimer Ge atoms and the back-bonded subsurface atoms remain inert to atomic O and molecular O₂, a possible cause of low reliability in Ge-related metal-oxide-semiconductor (MOS) devices.

Public transportation scheduling aims to optimize the allocation of transportation resources, enhance transportation efficiency, and increase passenger satisfaction, which is crucial for building a sustainable urban transportation system. As a complement to public transportation, shared bikes provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, such as peak usage periods in the morning and evening and significant fluctuations in demand across different areas, the optimization of shared bike dispatch can better meet user needs, thereby reducing vehicle vacancy rates and increasing operating revenue. Addressing this issue, this paper designs a comprehensive decision-making approach for spatio-temporal demand prediction and bike dispatch optimization. For demand prediction, we designed a T-GCN based bike demand prediction model. In terms of dispatch optimization, we considered factors such as dispatch capacity, distance restrictions, and dispatch costs, and designed an optimization solution based on genetic algorithms. Finally, this paper validates the approach using shared bike operating data, shows that T-GCN can effectively predict short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution verifying the model’s effectiveness. The shared bike dispatch approach proposed in this paper, which combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

Due to the advantages of low latency, low power consumption and high flexibility of FPGA-based acceleration technology, it has been more and more widely studied and applied in the field of computer vision in recent years. An FPGA-based feature extraction and tracking accelerator for real-time visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) is proposed, which can realize the complete acceleration processing capability of the image front-end and directly output the feature point ID and coordinates to the backend. The accelerator consists of image preprocessing, pyramid processing, optical flow processing, and feature extraction and tracking modules. For the first time, it implements a hardware solution that combines features from accelerated segment test (FAST) corners with Gunnar Farneback (GF) dense optical flow, to achieve better feature tracking performance and provide more flexible technical route selection. In order to solve the scale invariance and rotation invariance lacking problem of FAST features, an efficient pyramid module with a five-layer thumbnail structure is designed and implemented. The accelerator is implemented on a modern Xilinx Zynq FPGA. The evaluation result shows that the accelerator can achieve stable tracking of features of violently shaking images, and is consistent with the results of MATLAB code running on PC. When operating at 100MHz, the accelerator can process 108 frames per second for 720P images and 48 frames per second for 1080P images. Compared to PC CPUs that consume seconds of time, the processing latency is greatly reduced to the order of milliseconds, making GF dense optical flow an efficient and practical technical solution on the edge side.

This research not only dedicated a less restrictive method of iteration-varying function for a learning control law to design a controller but also synchronize two nonlinear systems with free time-delay. In addition, the mathematical theory of system synchronization has proved rigorously and the theory verified through an example to demonstrate the behavior of each parameter in the theory. The design of a controller using the iterative learning control law is significant for robotic tracking. The controller in this research generates a feed-forward control input using the error dynamics among the drive-response systems. The error dynamics satisfies the Lyapunov function and the combination of output errors, which respectively represented relative estimated differences of the drive-response systems. The iterative learning control rule serves the function of a filter adding previous control error after the end of each iteration. The numerical example of a synchronous system is given a Lorenz system for driving and another with the iterative learning control law for response under different initial condition. The results verify and demonstrate the proposed mathematical theory. The simulation exhibits consistency in the behavior of each parameter to match mathematical theory.

This paper aims the automatic ball balancers (ABBs) used in passive balancing devices for suppressing vibration of the eccentric rotor. The system model describes which equipped with a skew-mounted ABB with angular deviation. The dynamic equilibrium equations of the system are deduced from the perspective of three-dimensional (3D) dynamics. The results obtained are consistent with those derived from the Euler-Lagrange equations. It is exciting that the spatial dynamics method reveals the spatial geometric characteristic of dynamic balance positioning of the balls when the system is balanced with vibration suppression. The spatial property emerges the perpendicular line from each ball to the rotating spindle of the system must pass through the central axis of the orbit perpendicular to the ABB plane. This characteristic is a general rule that can be used to explain the phenomenon of the stable equilibrium positions of the balls in all previously studied cases.

The outbreak of COVID-19 in 2020 caused significant disruptions to global shipping and the world economy. This paper aims to investigate the impact of the pandemic on global shipping by analyzing the Baltic Dry Index (BDI). The BDI is a metric that reflects the worldwide shipping costs and directs related to supply and demand conditions, making it an indicator of economic production. The study utilizes data from 2019 to 2021, before and after the outbreak of COVID-19, and considers 13 independent variables, including raw materials, energy, stock market indexes, global port calls, and confirmed COVID-19 cases to investigate how to influent the BDI. The study employs stepwise regression to select variables and build models before and after the pandemic. The findings reveal that the key factors affecting the freight index BDI before the outbreak are: international scrap steel prices, iron ore prices, and the Commodity Research Bureau Index. However, after the COVID-19 outbreak, the factors affecting the BDI changed to the Shanghai Index, global port calls, and the number of confirmed COVID-19 cases.

In all areas of medicine, especially in radiology, computers are increasing year by year. Filmless radiology, speech recognition software, electronic application forms, and teleradiology are recent developments that have greatly improved radiologists' performance. This research explores radiology software trends, predictions, and the challenges posed by informatics and historical trend analysis. The rationale behind this research is that information technology (IT) is overgrowing almost every day. We must continuously seek new ways to apply IT to make more use of resources. Consequently, IT becomes increasingly crucial to radiology organizations' innovative thinking, workflow, and business models. This study aimed to analyze all radiology software publications in the Science Citation Index (SCI). From 1991 to July 2021, SCI was used to search for publications systematically. We have also widely used this historical method in radiology software research. The findings and discussions are base on an assessment of trends, predictions, contributions, and challenges in radiology software, and we are exploring radiology software with six evolutionary stages. The gift of this research is that radiology managers realize that the use of new information technologies is closely related to survival in a competitive environment. Radiology companies can review these new technologies to develop more innovative business models and services to improve operational deficiencies.

The utilization of smart IoT devices, commonly referred to as digital twins, is aimed at the digitalization of human knowledge within aquaculture processes. This involves the incorporation of cutting-edge technologies, including information-based management with big data and modeling, to automate machinery and gain comprehensive insights into the aquaculture environment and fish farm conditions. The ultimate objective is to empower farmers to make informed decisions, furnishing them with objective data to enhance their capacity in monitoring and controlling the various factors impacting fish production. As a result, farming decisions can be fine-tuned to enhance fish health and optimize farm output. In the context of large and modern aquaculture farms, technological innovation becomes imperative to automate processes, minimize labor requirements, and streamline fish feeding operations. Remarkably, the literature currently offers limited discussions on the digital transformation of aquaculture through the application of digital twin methodologies. A prior study underscores the critical influence of factors such as market prices and fish survival rates on the profitability of offshore caging culture. In this study, we embark on an analysis of the prerequisites for establishing a digital twin infrastructure tailored to intelligent fish feeding management. This infrastructure is designed to facilitate the integration of technology and data-driven decision-making, ultimately enhancing the efficiency of fish feeding processes. The proposed architecture for the fish feeding digital twin encompasses various digital twin components, encompassing water quality forecasting, fish population assessment, fish metrics estimation, fish feed prediction, and evaluation of fish feeding intensity. Furthermore, we optimize the daily fish feeding process through reinforcement learning algorithms. Finally, we implement a cloud-based AIoT system that provides the runtime environment for executing digital twins and controlling our intelligent fish feeding machinery. Experimental findings underscore the efficacy of the proposed digital twin system in significantly improving traditional fish feeding processes, notably in terms of reducing food costs and labor requirements.