Optimistic Method Research Articles

Investigation of Cephalexin Removal Using Biochar Derived from Nephelium lappaceum Seeds

<p class="MsoNormal" style="line-height:200%;margin-bottom:0cm;mso-layout-grid-align:none;text-align:justify;text-autospace:none;"><span style="font-family:"Times New Roman",serif;font-size:12.0pt;line-height:200%;" lang="EN-US">The improper disposal of drugs into bodies of water has become a severe environmental and health issue. Biochar synthesized from different agro residues could be a potential material for the adsorption of pollutants in aqueous environments. </span><em><i style="mso-bidi-font-style:normal;"><span style="font-family:"Times New Roman",serif;font-size:12.0pt;line-height:200%;" lang="EN-US">Nephelium lappaceum</span></i></em><span style="font-family:"Times New Roman",serif;font-size:12.0pt;line-height:200%;" lang="EN-US"> seeds were pyrolyzed at 600 °C in a pyrolysis reactor incorporating a slow pyrolysis process for carbonization. Fourier-Transform Infrared (FTIR), Scanning Electron Microscope (SEM), and X-ray diffraction (XRD) were used to characterize the biochar before and after adsorption of the pharmaceutical pollutant cephalexin. The effects of several factors, including initial cephalexin concentration, time of contact, dosage of adsorbent, and pH, were considered for the sorption. The findings showed that the Freundlich model offered the greatest fit for adsorption. The better-fitted kinetic model was the pseudo-second-order kinetic. Based on Langmuir isotherm model, the maximum adsorption capacity of cephalexin was obtained as 63.69 mg/g. The adsorption process involves hydrogen bonding, surface complexation, electrostatic and π−π EDA interactions. The current study offers a feasible and optimistic method for using agricultural waste and an alternative adsorbent substance for recovering highly concentrated cephalexin from water-based solutions.<o:p></o:p></span></p>

Women may not benefit from repeated frozen embryo transfers: a retrospective analysis of the cumulative live birth rate of 43972 women.

Which specific groups of women would not benefit from repeated frozen embryo transfers (FETs)? Women over 45 years of age should stop treatment after three FET attempts due to the absence of further benefits, while women aged 40-45 years and those with a diminished ovarian reserve and other causes of infertility have a lower chance of improving their cumulative live birth rate (CLBR) within five FET cycles and experience fewer advantages from repeated transfers. In real-life scenarios of ART, women who fail to achieve a live birth often choose to undergo repeated FETs via the freeze-all strategy. This retrospective study included 43972 women who underwent 86496 oocyte retrieval cycles and 82022 FET cycles between January 2010 and March 2023 under the freeze-all strategy. We categorized the population based on the female's age at the first oocyte pick-up (OPU) cycle (Groups 1-6: <30, 30-34, 35-39, 40-42, 43-44, and ≥45 years of age), number of retrieved oocytes at the first OPU cycle (Groups 1-5: 1-5, 6-10, 11-15, 16-20, and >20 oocytes), and causes of infertility (Groups 1-9: tubal factor, male factor, polycystic ovary syndrome, diminished ovarian reserve, endometriosis, other uterine factors, combined factors, unexplained infertility, and other infertility) to analyse their CLBRs within different FET cycles via Kaplan-Meier analysis (optimistic method) and the competing risk method (conservative method). We utilized multivariate Cox and Fine-Gray models to examine the associations between the CLBR and age, the number of retrieved oocytes, and nine causes of infertility. The CLBR decreased with increasing female age over five FET cycles (Groups 1-6: optimistic method: 96.4%, 94.2%, 86.0%, 50.2%, 23.1%, and 10.1%; conservative method: 87.1%, 82.0%, 67.8%, 33.9%, 13.8%, and 3.5%, respectively). Moreover, there was an increasing trend in the number of retrieved oocytes (Groups 1-5: optimistic method: 82.5%, 91.7%, 93.6%, 94.1%, and 96.2%; conservative method: 58.6%, 76.7%, 84.8%, 88.0%, and 92.5%, respectively). Furthermore, the CLBR varied across different causes of infertility (Groups 1-9: optimistic method: 91.7%, 93.1%, 96.6%, 79.2%, 89.9%, 76.1%, 90.0%, 92.9%, and 35.4%; conservative method: 77.3%, 79.4%, 88.9%, 46.7%, 72.7%, 62.1%, 74.4%, 78.8%, and 20.1%, respectively). Calculating the actual CLBR for each person is difficult because some patients have remaining embryos that have not been transferred; additionally, the current statistical methodology uses both optimistic and conservative methods to calculate the CLBR, and in real life, the CLBR falls between the optimistic and conservative curves. Our study is the first to identify specific subgroups of women who fail to benefit from repeated FETs and who require rational discontinuation of treatment following unsuccessful transfer. This study was financially supported by grants from the National Natural Science Foundation of China (grant numbers: 82271732 to Y.K., 82071603 to L.W., 82001502 to Y.L., and 82201888 to X.S.). The authors declare that they have no conflicts of interest in the present study. N/A.

The cumulative live birth rates of 18 593 women with progestin-primed ovarian stimulation-related protocols and frozen-thawed transfer cycles.

What are the odds of achieving pregnancy when adopting progestin-primed ovarian stimulation (PPOS)-related protocols combined with repetitive frozen-thawed transfer (FET) cycles in patients with different clinical characteristics? The cumulative live birth rates (CLBRs) of women undergoing different PPOS-related protocols can be significantly and consistently enhanced within six FET cycles when the female age is <40 years (or even <45 years) and when >5 oocytes are retrieved, regardless of antral follicle count (AFC). There have been numerous studies on the live birth rate of the first FET cycle in patients with PPOS-related protocols. These studies have focused mainly on comparing pregnancy outcomes with those of other stimulation protocols. However, owing to the unique features of the PPOS-related strategy, such as its flexible timing of oocyte retrieval and repeated transfer of frozen embryos, studies using the CLBR as an overall indicator of success and investigating which types of patients would benefit from this protocol are lacking. This retrospective cohort study included 18 593 women who underwent PPOS-related protocols (dydrogesterone + hMG, medroxyprogesterone acetate + hMG, micronized progesterone + hMG treatment, and luteal-phase ovarian stimulation protocol) from 1 March 2011 to 31 September 2022 in our centre. The population was categorized by female age, number of oocytes retrieved, and AFC in the analysis of CLBR within six FET cycles. The age groups (Groups 1-5, respectively) were <30, 30-34, 35-39, 40-44, and ≥45 years. The number of oocytes retrieved was grouped as 1-5, 6-10, 11-15, 16-20, and >20. AFC was grouped as <5, 5-10, 11-15, and >15. The Kaplan-Meier analysis (optimistic method), which hypothesized that patients who did not continue treatment had the same chance of achieving a live birth as those who continued, and the competing risk method (conservative method) which hypothesized they had no chance of achieving a live birth, were applied. In further analyses, the Cox model and Fine-Gray model were adopted: the former corresponds to the optimistic scenario, and the latter corresponds to the pessimistic scenario. CLBR had a declining trend with female age over six FET cycles (Groups 1-5, respectively: optimistic: 96.9%, 96.6%, 91.4%, 67.3%, and 11.7%; conservative: 87.3%, 85.0%, 74.0%, 41.3%, and 7.5%), requiring more FET cycles to achieve a success rate of at least 50% (Groups 1-5, respectively: optimistic: 2, 2, 2, 4, and >6 cycles; conservative: 2, 2, 2, >,6 and >6 cycles). CLBR showed an increasing trend with the number of oocytes retrieved (Groups 1-5, respectively: optimistic: 93.8%, 94.3%, 95.8%, 96.0%, and 95.6%; conservative: 66.2%, 78.3%, 85.6%, 88.9%, and 91.0%). All groups needed the same number of FET cycles to achieve a success rate of at least 50% (Groups 1-5, respectively: optimistic: 2, 2, 2, 2, and 2 cycles; conservative: 2, 2, 2, 2, and 2 cycles). Furthermore, the CLBR within six FET cycles had an increasing trend with AFC number (Groups 1-4, respectively: optimistic: 89.2%, 94.8%, 95.9%, and 96.3%; conservative: 67.4%, 78.2%, 83.9%, and 88.1%), with all four groups achieving a success rate of at least 50% by the second FET cycle. The current research is limited by its retrospective design and single-centre nature, which may restrict the generalizability of our findings. This work describes two models (the Kaplan-Meier analysis and the competing risk method) to evaluate the clinical outcome of patients using PPOS-related protocols, which are especially useful for patients of advanced age or those with diminished ovarian reserve. Our findings encourage patients below 45 years old, especially younger than 40 years, and patients with lower AFCs and fewer retrieved oocytes to try this new protocol. Moreover, this study demonstrates the degree of improvement in the CLBR within six FET cycles for patients with different clinical characteristics, providing a valuable point of reference to determine whether to continue ART after a transfer failure. The study was supported by grants from the National Natural Science Foundation of China (82071603 to L.W., 82001502 to Y.L.). There are no conflicts of interest to declare. N/A.

Microencapsulation of riboflavin-producing Lactiplantibacillus Plantarum MTCC 25,432 and Evaluation of its Survival in Simulated Gastric and Intestinal Fluid.

Microencapsulation is an optimistic method for the delivery of live microbial cells through different food products. In this study, riboflavin-producing probiotic strain Lactiplantibacillus plantarum MTCC 25,432 was encapsulated using a spray drying technique with different wall materials including Inulin, maltodextrin (MD), and MD + Inulin (1:1). The obtained spray dried powder was investigated for probiotic viability, encapsulation efficiency, particle size, water activity, moisture content, hygroscopicity, bulk and tapped densities, storage stabilities, Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA). Besides this, the viability of the free and encapsulated probiotic cells was tested under simulated gastric and intestinal fluid conditions. In the results, microcapsules produced with the combination of MD + Inulin showed higher dry powder yield (36.5%) and viability of L. plantarum MTCC 25,432 (7.4 log CFU / g) as compared with individual coating materials. Further characterization revealed that MD + Inulin microcapsules are spherical (3.50 ± 1.61μm in diameter) in shape with concavities, showed the highest encapsulation efficiency (82%), low water activity (0.307), moisture content (3.67%) and good survival ability at low pH (pH 2.0 and 3.0), high bile salt concentrations (1.0% and 2.0%), and long storage conditions. No differences in FTIR spectra were observed among the tested samples. However, TGA showed enhanced thermal stability of probiotic-loaded microcapsules when MD + Inulin was used together. In conclusion, MD + Inulin could be a potential encapsulation material for riboflavin-producing probiotic bacteria L. plantarum MTCC 25,432.

Optimal design and scheduling of carbon capture power plant based on uncertainty decision-making methods

Post-combustion capture is one of the most mature carbon reduction technologies which plays an important role in pushing forward the process of "carbon neutrality". However, most previous efforts focused merely on the fixed operation of power plants and carbon capture devices, without taking into account the effects of fluctuating electricity prices and the subsequent variations in energy penalty of solvent regeneration process. As a result, the ability of carbon capture power plants (CCPP) to withstand fluctuations has deteriorated, leading to a decrease in robustness. In order to take advantage of the flexible operation characteristics of the power plant and the carbon capture device, the deterministic hourly electricity price and power load demand, as well as uncertainty of power load demand in future are considered in the study to determine the optimal network configuration and the flexible scheduling of carbon capture power plants based on decision-making theories. The uncertain parameters are presented by scenarios, and through MINLP-based optimization, the optimal system configuration under each scenario is obtained. Then these configurations are applied to other scenarios to obtain the scheduling and economic performance of the system. Five uncertainty decision-making theories (optimistic method, pessimistic method, optimistic coefficient method, equal probability method, minimum maximum regret value method) are adopted to select the most appropriate system scheme. Finally, a case study is investigated to demonstrate the proposed method, and the effectiveness and the feasibility are proved by the obtained results. Two network configurations with daily profit from $30,000 to $240,000 by scenarios are obtained and investigated. Results show that the turbine layout in the configuration chosen by optimistic method is radical, while by pessimistic criterion the turbine system shows margin in operation. Configuration selected by the equal possible value method and minimax regret method can achieve good economic performance operating in various scenarios. This research can help decision makers to make scientific decisions and reduce operating risks under uncertainties.

NanoTafla Nanocomposite as a Novel Low-Cost and Eco-Friendly Sorbent for Strontium and Europium Ions.

Now the wide use of nanooxides is attributed to their remarkable collection of properties. Nanocomposites have an impressive variety of important applications. A thermal decomposition approach provides a more optimistic method for nanocrystal synthesis due to the low cost, high efficiency, and expectations for large-scale production. Therefore, in this study a new eco-friendly nanooxide composite with sorption characteristics for europium (Eu(III)) and strontium (Sr(II)) was synthesized by a one-step thermal treatment process using earth-abundant tafla clay as a starting material to prepare a modified tafla (M-Taf) nanocomposite. The synthesized nancomposite was characterized by different techniques before and after sorption processes. Different factors that affected the sorption behavior of Eu(III) and Sr(II) in aqueous media by the M-Taf nanocomposite were studied. The results obtained illustrated that the kinetics of sorption of Eu(III) and Sr(II) by the M-Taf nanocomposite are obeyed according to the pseudo-second order and controlled by a Langmuir isotherm model with maximum sorption capacities (Qmax) of 25.5 and 23.36 mg/g for Eu(III) and Sr(II), respectively. Also, this novel low-cost and eco-friendly sorbent has promising properties and can be used to separate and retain some radionuclides in different applications.

Effect of Female and Male Body Mass Index on Cumulative Live Birth Rates in the Freeze-all Strategy.

The impact of parental overweight/obese on cumulative live birth rate in in vitro fertilization/intracytoplasmic sperm injection using a freeze-all strategy is still unknown. To explore the effect of parental body mass index (BMI) on cumulative live birth rate (CLBR) in a freeze-all strategy over 1.5 years. This was a retrospective study in a tertiary care academic medical center; 23 482 patients (35 289 frozen-thawed embryo transfer cycles) were divided into 4 groups according to Asian BMI classification. The main outcome measure was CLBR. Female overweight/obesity had a lower tendency in CLBR (groups 1-4: optimistic: 69.4%, 67.9%, 62.3%, and 65.7%; conservative: 62.9%, 61.1%, 55.4%, and 57.6%) and prolonged time (groups 1-4: 11.0, 12.2, 15.9, and 13.8 months for 60% CLBR in the optimistic method; 8.7, 9.5, 11.7, 11.0 months for 50% CLBR in the conservative method). The same trend to a lesser extent was also observed in male BMI groups. When combining parental BMI, "parental overweight/obesity" had lower CLBR and longer time for reaching CLBR >50% (optimistic: 4.5 months for 60% CLBR; conservative: 3 months for 50% CLBR), followed by "only female high BMI" (optimistic: 2.1 months for 60% CLBR; conservative: 1.7 months for 50% CLBR), while "only male high BMI" had no influence. Our results showed the importance of parental BMI, female BMI, and male BMI on the 1.5-year CLBR using a freeze-all strategy; the time to reach the CLBR (60% in optimistic, 50% in conservative) for overweight and obese patients was several months, but it is not as long as losing weight.

A Primal-Dual Online Algorithm for Online Matching Problem in Dynamic Environments

Recently, the online matching problem has attracted much attention due to its wide application on real-world decision-making scenarios. In stationary environments, by adopting the stochastic user arrival model, existing methods are proposed to learn dual optimal prices and are shown to achieve a fast regret bound. However, the stochastic model is no longer a proper assumption when the environment is changing, leading to an optimistic method that may suffer poor performance. In this paper, we study the online matching problem in dynamic environments in which the dual optimal prices are allowed to vary over time. We bound the dynamic regret of online matching problem by the sum of two quantities, including a regret of online max-min problem and a dynamic regret of online convex optimization (OCO) problem. Then we propose a novel online approach named Primal-Dual Online Algorithm (PDOA) to minimize both quantities. In particular, PDOA adopts the primal-dual framework by optimizing dual prices with the online gradient descent (OGD) algorithm to eliminate the online max-min problem's regret. Moreover, it maintains a set of OGD experts and combines them via an expert-tracking algorithm, which gives a sublinear dynamic regret bound for the OCO problem. We show that PDOA achieves an O(K sqrt{T(1+P_T)}) dynamic regret where K is the number of resources, T is the number of iterations and P_T is the path-length of any potential dual price sequence that reflects the dynamic environment. Finally, experiments on real applications exhibit the superiority of our approach.

Artificial Intelligence Approaches to Estimate the Transport Energy Demand in Turkey

In this study, eight parameters are selected and their historical data are collected to predict the future of the energy demand of Turkey. The initial eight parameters were the gross domestic product (GDP) of Turkey, average annual US crude oil price (COP), inflation for Turkey in percentages (INF), the population of Turkey, total vehicle travel in kilometers for Turkey, total amount of goods transported on motorways, employment for Turkey, and trade of Turkey. However, after these eight parameters data are analyzed using Pearson and Spearman correlation methods, it is found out that five of these parameters are highly correlated. The remaining three parameters are the GDP of Turkey, COP, and INF for Turkey. Afterward, five separate scenarios are developed to forecast the future of the energy demand of Turkey. The first two scenarios involve the third- and fourth-order polynomial fitting, the third and fourth scenarios employ static and recurrent neural networks, and the fifth scenario utilizes autoregressive models to predict the future energy demand of Turkey. The efficient hybridization of the seagull optimization and very optimistic method of minimization metaheuristic algorithms is carried out to achieve the polynomial fitting of the data. The optimization performance of the hybrid algorithm is assessed by applying the algorithm on benchmark optimization problems and comparing the results with that of some other metaheuristic optimizers. Moreover, it is seen that the forecasts of the first scenario agree well with the Ministry of the Energy and Natural Resources estimates.

DMTC: Optimize Energy Consumption in Dynamic Wireless Sensor Network Based on Fog Computing and Fuzzy Multiple Attribute Decision‐Making

Advances in wireless technologies and small computing devices, wireless sensor networks can be superior technology in many applications. Energy supply constraints are one of the most critical measures because they limit the operation of the sensor network; therefore, the optimal use of node energy has always been one of the biggest challenges in wireless sensor networks. Moreover, due to the limited lifespan of nodes in WSN and energy management, increasing network life is one of the most critical challenges in WSN. In this investigation, two computational distributions are presented for a dynamic wireless sensor network; in this fog‐based system, computing load was distributed using the optimistic and blind method between fog networks. The presented method with the main four steps is called Distribution‐Map‐Transfer‐Combination (DMTC) method. Also, Fuzzy Multiple Attribute Decision‐Making (Fuzzy MADM) is used for clustering and routing network based on the presented distribution methods. Results show that the optimistic method outperformed the blind one and reduced energy consumption, especially in extensive networks; however, in small WSNs, the blind scheme resulted in an energy efficiency network. Furthermore, network growth leads optimistic WSN to save higher energy in comparison with blinded ones. Based on the results of complexity analysis, the presented optimal and blind methods are improved by 28% and 48%, respectively.

Study of structural optimization design on a certain vehicle body-in-white based on static performance and modal analysis

Abstract The intensity analysis was proposed for a certain all-aluminum vehicle body-in-white, and an optimistic method of the vehicle body was carried out based on lightweight target. The finite element model of vehicle body-in-white was established, and the flexural rigidity and torsional rigidity of the vehicle body are calculated according to the actual operating conditions of the vehicle. The flexural rigidity and torsional rigidity of the vehicle body are calculated, and the main opening deformation of the vehicle body under these two working conditions was studied. Finally, based on the thickness of the body panel, the structural sensitivity analysis of the vehicle body was carried out. According to the sensitivity analysis data, the optimization variable is determined with lightweight as the target, and the vehicle body structure is optimized. The results showed that the static and dynamic performance indexes of the white body were higher than the standards of similar motorcycle type at home and abroad, the bending stiffness of the structurally improved basic model after sensitivity optimization was reduced by 2.1% compared with before optimization, but still much higher than the target reference value (122,000 N/mm). And the quality of the vehicle body was reduced, which met the lightweight requirements of optimized design after the optimization of the thickness of the improved basic model.

Emphasis on switch selection and its switching loss comparison for on‐board electric vehicle charger

A DC/DC converter plays a vital role in on-board electric vehicle (EV) charger. Among the many converter topologies, the phase-shifted full-bridge DC/DC converter (PSFBDC) is widely used. For improved efficiency, PSFBDC is embedded with passive auxiliary networks resulting in several unique configurations for achieving soft-switching in entire load range. The architecture of the chosen DC/DC converter configuration should comply with the society of automotive engineers (SAE) standards. Although, the conduction loss of the converter can be minimised by choosing a proper topology configuration and proper switching of the configuration, the selection of appropriate for PSFBDC plays an important role in enhancing the overall efficiency. An optimistic method for the selection of switch configuration satisfying the reduced power loss and cost-effective aspects is presented. For demonstrating the proposed method, a 1 kW 100 kHz system with commercially available switches IRFP460, IXFN64N60P, and IPP65R045C7 is chosen and the obtained results are presented.

Optimal design of distributed energy resource systems under large-scale uncertainties in energy demands based on decision-making theory

This study focuses on the optimal design of distributed energy resource systems with consideration of large-scale uncertainty of energy demands based on decision- making theory. Five integrated modeling and optimization frameworks are developed through the combined use of mixed integer linear programming and uncertainty decision-making criteria (including optimistic criterion, pessimistic criterion, Hurwicz criterion, Laplace criterion, and minimax regret criterion). Superstructure-based mixed integer linear programming models are used for the optimal design and optimal operation of the system where the objective function is to minimize the annual cost. The uncertainty of energy demands is represented by assuming a set of possible scenarios. The proposed methods are applied to the planning of a distributed energy resource system for a hotel in city of Guangzhou, China and their validity and effectiveness are verified. Results show that each method has its specific feature. Optimistic method is risky and recommends a relative small-scale system, while pessimistic method is conservative presenting a relative large-scale system. Hurwicz method is with great subjectivity, making different decisions at different values of optimism coefficient. Both Laplace method and minimax regret method identify a moderate-scale system as the best alternative. Sensitivity analyses on the energy demand scenarios are conducted and results show that the five methods have high sensitivity to the choice of scenarios.

MAMOGRAM IMAGE CLASSIFICATION USING EXTREME LEARNING MACHINE

Objective: To make early diagnosis of breast cancer from mammogram image. Methods/Statistical analysis: To avoid the misdiagnosis, we proposed a system to sort the suspicious masses from the mammogram image by using Extreme learning machine algorithm. The ELM based classifier is used to classify the input data as malignant and benign classes with the abnormal class. The effectiveness of the ELM algorithm is superior to the other existing algorithms for mammogram classification problems with its reduced training time and classification accuracy. Findings: We provide an optimistic method for binary class classification of mammograms using extreme learning machine algorithm. Mammography is a technique which is preferred for early diagnosis of breast cancer. On the other hand, in most cases, it is not easy to differentiate benign and malignant tumor without biopsy, hence misdiagnosis is always possible. The machine learning algorithm provides high accuracy than other techniques and also the execution time is very low when compared to normal diagnosis. The existing methods are very slow compared to this proposed technique. The input images are the mammogram image and the segmentation, preprocessing is performed to remove the noises present. Application/Improvements: The main application of the system is the early diagnosis of cancerous cell present and also classifies the normal and abnormal images. Keywords: Breast Cancer, Extreme Learning Machine, Mammography

A very optimistic method of minimization (VOMMI) for unconstrained problems

Numerous nontraditional optimization techniques have been suggested in literature to solve highly complicated multimodal mathematical functions. The complexity involved in solving the functions increases with the increase in the number as well as the range of the variables. Most of the optimization algorithms involve much complexity in their understanding and implementation and this complexity is found to be proportional to the number of algorithm specific parameters. Hence, there is a need to find simple but effective algorithms with minimum number of algorithm specific parameters. The present work aims to introduce a novel and simple methodology for optimization without sacrificing the effectiveness. It relies on following the ‘existing best practices’ to achieve improvement. The algorithm employs two factors represented as luck and effort factors and solves many complex unconstrained optimization problems effectively. The proposed algorithm is found to be successful in providing better solutions than some popular algorithms for certain complicated problems, reported in literature.

The need to approximate the use-case in clinical machine learning.

The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map those data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is vital to reliably quantify their prediction accuracy. Cross-validation (CV) is the standard approach where the accuracy of such algorithms is evaluated on part of the data the algorithm has not seen during training. However, for this procedure to be meaningful, the relationship between the training and the validation set should mimic the relationship between the training set and the dataset expected for the clinical use. Here we compared two popular CV methods: record-wise and subject-wise. While the subject-wise method mirrors the clinically relevant use-case scenario of diagnosis in newly recruited subjects, the record-wise strategy has no such interpretation. Using both a publicly available dataset and a simulation, we found that record-wise CV often massively overestimates the prediction accuracy of the algorithms. We also conducted a systematic review of the relevant literature, and found that this overly optimistic method was used by almost half of the retrieved studies that used accelerometers, wearable sensors, or smartphones to predict clinical outcomes. As we move towards an era of machine learning-based diagnosis and treatment, using proper methods to evaluate their accuracy is crucial, as inaccurate results can mislead both clinicians and data scientists.

Decision-Making Method on Feasibility Study Stage of Software Development Project

Scheme selection and decision-making is the important link of the feasibility study phase, determines the success or failure of the software project and benefit, determines the future and destiny of the entire project. For software project scheme selection and decision-making difficulties on the basis of study selection and decision theory method, show the specific examples of decision-making. The main research study the common methods of operational research, including definite decision-making, Uncertain decision-making, risk decision-making etc, in which Uncertain decision-making studied max-min regret rate method and optimistic coefficient method. The results show that this research method has important practical value for software project scheme selection and decision-making, specific application should be combined with the software project reality, take various factors into account, make scientific decision-making.

Design Thinking and Cloud Manufacturing: A Study of Cloud Model Sharing Platform Based on Separated Data Log

To solve the product data consistency problem which is caused by the portable system that cannot conduct real-time update of product data in mobile environment under the mass customization production mode, a new product data optimistic replication method based on log is presented. This paper focuses on the design thinking provider, probing into a manufacturing resource design thinking cloud platform based on manufacturing resource-locating technologies, and also discuss several application scenarios of cloud locating technologies in the manufacturing environment. The actual demand of manufacturing creates a new mode which is service-oriented and has high efficiency and low consumption. Finally, they are different from the crowd-sourcing application model of Local-Motors. The sharing platform operator is responsible for a master plan for the platform, proposing a open interface standard and establishing a service operation mode.

A Concurrency Control Method Based on Commitment Ordering in Mobile Databases

Disconnection of mobile clients from server, in an unclear time and for an unknown duration, due to mobility of mobile clients, is the most important challenges for concurrency control in mobile database with client-server model. Applying pessimistic common classic methods of concurrency control (like 2pl) in mobile database leads to long duration blocking and increasing waiting time of transactions. Because of high rate of aborting transactions, optimistic methods aren`t appropriate in mobile database. In this article, OPCOT concurrency control algorithm is introduced based on optimistic concurrency control method. Reducing communications between mobile client and server, decreasing blocking rate and deadlock of transactions, and increasing concurrency degree are the most important motivation of using optimistic method as the basis method of OPCOT algorithm. To reduce abortion rate of transactions, in execution time of transactions` operators a timestamp is assigned to them. In other to checking commitment ordering property of scheduler, the assigned timestamp is used in server on time of commitment. In this article, serializability of OPCOT algorithm scheduler has been proved by using serializability graph. Results of evaluating simulation show that OPCOT algorithm decreases abortion rate and waiting time of transactions in compare to 2pl and optimistic algorithms.

지구통계학적 기법을 이용한 연약 지반 분포 추정

해상풍력발전단지 건설 시 기초지반이 해상 점성토층의 지반일 경우 플랜트 자체 하중의 영향과 풍력과 같은 외부 모멘트 영향으로 인해 침하 발생을 야기할 수 있기 때문에 연약지반 분포를 파악하는 것은 풍력플랜트 입지를 위해 매우 중요한 사항이다. 이러한 연약지반 분포를 파악하는 방법은 시추조사 방법이 가장 최적의 방법이지만, 현장의 여건이나 경제적 제약이 큰 단점을 가지고 있다. 따라서 이러한 문제점을 보완하기 위해 현재 지구통계학적 방법을 이용한 연구가 활발하게 진행되고 있다. 본 연구에서는 풍력 플랜트 설치를 가정한 해상 영역을 설정하여 지구통계학적 기법을 통하여 연약 지반층 두께 분포의 추정을 연구 수행하였다. 연약 지반층은 표준 관입시험치의 결과를 이용하여 구분하였으며, 지구통 계학적 기법은 정규크리깅과 순차가우시안 시뮬레이션을 이용하여 결과를 비교하였다. 그 결과 비슷한 영역에서 최대 점성토의 두께를 가지는 영역이 나타남을 파악할 수 있었으며, 그 결과의 불확실성을 정량적으로 평가할 수 있었다.