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نتیجه جستجو - تحلیل آماری

تعداد مقالات یافته شده: 30
ردیف عنوان نوع
1 PortiK: A computer vision based solution for real-time automatic solid waste characterization – Application to an aluminium stream
PortiK: یک راه حل مبتنی بر بینایی کامپیوتری برای شناسایی خودکار زباله جامد در زمان واقعی - کاربرد در جریان آلومینیوم-2022
In Material Recovery Facilities (MRFs), recyclable municipal solid waste is turned into a precious commodity. However, effective recycling relies on effective waste sorting, which is still a challenge to sustainable develop- ment of our society. To help the operations improve and optimise their process, this paper describes PortiK, a solution for automatic waste analysis. Based on image analysis and object recognition, it allows for continuous, real-time, non-intrusive measurements of mass composition of waste streams. The end-to-end solution is detailed with all the steps necessary for the system to operate, from hardware specifications and data collection to su- pervisory information obtained by deep learning and statistical analysis. The overall system was tested and validated in an operational environment in a material recovery facility. PortiK monitored an aluminium can stream to estimate its purity. Aluminium cans were detected with 91.2% precision and 90.3% recall, respectively, resulting in an underestimation of the number of cans by less than 1%. Regarding contaminants (i.e. other types of waste), precision and recall were 80.2% and 78.4%, respectively, giving an 2.2% underestimation. Based on five sample analyses where pieces of waste were counted and weighed per batch, the detection results were used to estimate purity and its confidence level. The estimation error was calculated to be within ±7% after 5 minutes of monitoring and ±5% after 8 hours. These results have demon- strated the feasibility and the relevance of the proposed solution for online quality control of aluminium can stream.
keywords: امکانات بازیابی مواد | شناسایی مواد زائد جامد | یادگیری عمیق | شبکه عصبی عمیق | بینایی کامپیوتر | Material recovery facilities | MRF | Solid waste characterization | Deep-learning | Deep neural network | Computer vision
مقاله انگلیسی
2 بازاریابی جاذبه ای دیجیتال: اندازه گیری عملکرد اقتصادی تجارت الکترونیکی خواروبار در اروپا و آمریکا
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 30
این تحقیق به بررسی رابطه هزینه-نتیجه اقدامات بازاریابی جاذبه ای مورد استفاده تجارت الکترونیکی خواروبار می پردازد. این تحلیل بر اساس به کارگیری مدل درفمن و استینر (1954) برای بودجه تبلیغات بهینه است که مولفین آن را با بازاریابی دیجیتال تطبیق می دهند و با تحلیل آماری تجاری تایید میکنند. با توجه به 29 شرکت عمده در شش کشور در افق زمانی شش سال، تحلیل ترکیبی تکنیک های بهینه سازی موتور جستجو و بازاریابی موتور جستجو هدف جذب کارکنان به صفحات وب شرکت ها را دنبال می کند. نتایج تایید می کند که تجارت الکترونیکی بازاریابی جاذبه ای دیجیتال را بهینه سازی می کند. تفاوت ها بسته به نوع فرمت و سطح کشور فرق دارند.
واژگان کلیدی: بازاریابی جاذبه ای | بازاریابی دیجیتال | تجارت الکترونیک | خرده فروشی | عملکرد اقتصادی | بهینه سازی سرمایه گذاری بازاریابی.
مقاله ترجمه شده
3 Gathering local ecological knowledge to augment scientific and management understanding of a living coastal resource: The case of Oregon’s nearshore groundfish trawl fishery
جمع آوری دانش زیست محیطی محلی برای تقویت درک علمی و مدیریت از یک منبع ساحلی زنده: مورد ماهیگیری ماهی قرمز مایل به قرمز اورگان-2021
Globally, coastal nearshore regions are an intersecting point for human and biological productivity, often serving as hotspots for subsistence, commercial, and recreational fishing activities. Despite this, many nearshore areas remain poorly understood, monitored or managed. This case study examined the nearshore sector of Oregon’s groundfish trawl fishery, which exists in shallow estuarine and continental shelf habitats common along the West Coast of North America; areas that are important for early life history stages of many commercial and recrea- tional fisheries. The West Coast groundfish fishery includes over 90 different species, 40 of which occur within Oregon’s nearshore (here defined as the portion of the shelf extending seaward to a water depth of 200 m). The very shallow portions of the Oregon Coast (the area of the shelf inshore of 55 m) have been subject to limited scientific survey monitoring, and much of the details of the ecology, health, and processes in these habitats remain poorly understood. The utilization of the nearshore region by the commercial groundfish trawl fleet is also minimally documented despite the fact that experiential knowledge (local ecological knowledge [LEK]; trawl logbooks, fish tickets, interviews) exists. This research explored the capacity of capturing LEK sources to inform and enhance understanding of the drivers of effort and the vitality of nearshore fishery resources. Our approach used statistical analysis and mapping of nearshore trawl effort from 1981 to 2017 and gathered semi- structured interviews of intergenerational fishermen to bolster data-poor areas. Insights provided by sampling strategies and historical to current knowledge of access to groundfish assemblages provide informed baselines for future management. Spatial mapping results revealed a decline in trawl effort on the Oregon continental shelf thought time. Logbook and interview data assessment illuminated market and ecological drivers of fishing behavior as well as a unique sector of the groundfish fleet in Oregon: the beach fleet, with unique market and socio-economic challenges. Findings indicate a mixed-methods approach can provide a more thorough assess- ment of long-term interest in Oregon’s nearshore groundfish fishery. Ensuring better understanding of coastal interfacing regions such as Oregon’s nearshore insights potential for better conservation and utilization of marine resources and improved monitoring in resource limited management contexts.
keywords: دانش زیست محیطی محلی | زمین های دریایی ساحل غربی ایالات متحده | اطلاعات وابسته به ماهیگیری | ماهیگیری ساحلی | Local ecological knowledge | US West Coast groundfishes | Fisheries-dependent data | Coastal fisheries
مقاله انگلیسی
4 Causal models accounted for research participation effects when estimating effects in a behavioral intervention trial
مدل‌های علّی تأثیرات مشارکت در پژوهش حسابی زمان تخمین تأثیرات در یک کارآزمایی مداخله رفتاری-2021
Objective: Participants in intervention studies are asked to take part in activities linked to the conduct of research, including signing consent forms and being assessed. If participants are affected by such activities through mechanisms by which the intervention is intended to work, then there is confounding. We examine how to account for research participation effects analytically. Study design and setting: Data from a trial of a brief alcohol intervention among Swedish university students is used to show how a proposed causal model can account for assessment effects. Results: The proposed model can account for research participation effects as long as researchers are willing to use existing data to make assumptions about causal influences, for instance on the magnitude of assessment effects. The model can incorporate several research processes which may introduce bias. Conclusions: As our knowledge grows about research participation effects, we may move away from asking if participants are affected by study design, toward rather asking by how much they are affected, by which activities and in which circumstances. The analytic perspective adopted here avoids assuming there are no research participation effects.
Keywords: Research participation effects | Behavioral interventions | Randomized controlled trials | Causal models | Bias | Statistical analysis
مقاله انگلیسی
5 Comparison of the impacts of empirical power-law dispersion schemes on simulations of pollutant dispersion during different atmospheric conditions
مقایسه تأثیر برنامه های پراکندگی قانون تجربی قدرت در شبیه سازی پراکندگی آلاینده در شرایط جوی مختلف-2020
Accurate and rapid predictions of air pollutant dispersion are important for effective emergency responses after sudden air pollution accidents (SAPA). Notably, dispersion parameters (σ) are the key variables that influence the simulation accuracy of dispersion models. Empirical dispersion schemes based on power-law formulas are probably appropriate choices for simulations in SAPA because of the requirement for only routine meteorological data. However, performance comparisons of different schemes are lacking. In this study, the performances during simulations of air pollutant dispersion of four typical empirical parameterised schemes, i.e. BRIGGS, SMITH, Pasquill-Gifford, and Chinese National Standard (CNS), were investigated based on the GAUSSIAN plume model with datasets for the classic Prairie Grass experiments, 1956. The performances when simulating peak and overall concentrations in different Pasquill atmospheric stability classes (A, B, C, D, E, F) were quantitatively analysed through different statistical approaches. Results showed that the performances of four schemes for peak and overall concentrations were basically consistent. Scheme CNS in unstable atmospheric conditions (A, B, and C) performed significantly better than the others according to performance criteria, which included the lowest mean of absolute value of fractional biases, lowest normalised mean square errors, and largest mean values of the fraction within a factor of two when predicting peak and overall concentrations, respectively. Schemes BRIGGS and P-G exhibited slightly better performances during the neutral condition (D) followed by scheme CNS. Schemes SMITH and CNS demonstrated slight merits in predicting concentrations compared to the other schemes during stable conditions (E and F). As a whole, scheme CNS generally performed well for the different atmospheric stability classes. These analysis results can help to fill in the data gaps and improve our understanding of the influence of typical power-law function schemes on simulations of air pollutant dispersion. The results are expected to provide scientific support for air pollution predictions, especially during emergency responses to SAPA.
Keywords: Empirical power-law dispersion schemes | Atmospheric stability | Performance evaluation | Statistical analysis | Emergency response | Sudden air pollution accidents
مقاله انگلیسی
6 Data imbalance in classification: Experimental evaluation
عدم تعادل داده ها در طبقه بندی: ارزیابی تجربی-2020
The advent of Big Data has ushered a new era of scientific breakthroughs. One of the com- mon issues that affects raw data is class imbalance problem which refers to imbalanced distribution of values of the response variable. This issue is present in fraud detection, network intrusion detection, medical diagnostics, and a number of other fields where neg- atively labeled instances significantly outnumber positively labeled instances. Modern ma- chine learning techniques struggle to deal with imbalanced data by focusing on minimizing the error rate for the majority class while ignoring the minority class. The goal of our pa- per is demonstrate the effects of class imbalance on classification models. Concretely, we study the impact of varying class imbalance ratios on classifier accuracy. By highlighting the precise nature of the relationship between the degree of class imbalance and the cor- responding effects on classifier performance we hope to help researchers to better tackle the problem. To this end, we carry out extensive experiments using 10-fold cross validation on a large number of datasets. In particular, we determine that the relationship between the class imbalance ratio and the accuracy is convex.
Keywords: Classification | Class imbalance | Data analysis | Machine learning | Statistical analysis | Supervised learning
مقاله انگلیسی
7 کیفیت حسابرسی و هم حرکتی بازده سهام: شواهدی از ویتنام
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 21
این مقاله با هدف بررسی رابطه بین کیفیت حسابرسی و سطح هم حرکتی بازده سهام در بازار نوظهور ویتنام انجام شده است. این مطالعه تجربی براساس روش کمی و رویکرد قیاسی طراحی شده است. مجموعه داده های ترکیبی شامل 256 شرکت از صنایع مختلف با 1115 مشاهده سالانه در بورس اوراق بهادار شهر "هوشی مینه" در بازه زمانی سال های 2014 تا 2018 است. ما در این تحقیق با استفاده از همزمانی بازده سهام به عنوان متغییر وابسته و کیفیت حسابرسی به عنوان متغیر مستقل، یک مدل رگرسیون اقتصادسنجی ایجاد کردیم. برخی از متغیرهای کنترلی نیز به مدل های رگرسیون اقتصادسنجی اضافه می شوند زیرا تاثیر این مدل ها بر همزمانی قیمت سهام در مطالعات پیشین به خوبی اثبات شده است. به منظور بهبود دقت ضرایب رگرسیون، در کنار حداقل مربعات معمولی، از مدل اثرات تصادفی و برای بهترشدن تحلیل آماری مجموعه داده های ترکیبی از مدل اثرات ثابت استفاده می کنیم. نتایج نشان می دهند که کیفیت حسابرسی با همزمان سازی قیمت سهام رابطه مثبت دارد. این یافته حاکی از آن است که بازده سهام شرکت هایی که سطح کیفی حسابرسی در آنها بالاتر است هماهنگی بیشتری با بازار دارند. نتایج سایر متغیرهای کنترلی نیز از استدلال ما در مورد یافته های اصلی پشتیبانی می کند.
کلیدواژه ها: کیفیت حسابرسی | همزمانی بازده سهام | محیط اطلاعاتی | اطلاعات خاص شرکت | ویتنام
مقاله ترجمه شده
8 A lab-on-a-carbon nanodot sensor array for simultaneous pattern recognition of multiple antibiotics
مجموعه ای از سنسورهای نانودوت کربن بر روی یک کربن برای تشخیص الگوی همزمان آنتی بیوتیک های متعدد-2019
Carbon nanodots (CDs) have gained extremely consideration in recent years as a result of their predominant fluorescence properties. Combining these unique advantages, herein, a “lab-on-a-carbon nanodot” based crossreactive sensor array for multiple antibiotics discrimination was introduced. Four types of CDs which were synthesized by simply mixing diphosphorus pentoxide, different amino acids (isoleucine, leucine and histidine) and water, were used as sensing receptors. Eight antibiotics can be well distinguished on account of the different fluorescence responses by linear discrimination analysis (LDA). Isothermal titration calorimetry (ITC) results indicated that the main interaction force between CDs and antibiotics are hydrogen bonding and van der waals forces. More importantly, binary and ternary antibiotics mixtures can also be adequately recognized in real samples such as human urine.
Keywords: Sensor array | Carbon nanodots | Antibiotics | Lab-on-a-carbon nanodot | Statistical analysis
مقاله انگلیسی
9 کیفیت رابطه به عنوان پیش بینی کننده وفاداری مشتری B2B در بخش داروسازی: شاهدی از کشور اردن
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 17 - تعداد صفحات فایل doc فارسی: 24
این مقاله با هدف بررسی تاثیر پذیری ابعاد کیفیت رابطه ( به عنوان مثال اعتماد، رضایت و تعهد) بر جنبه های وفاداری یعنی وفاداری نگرشی و وفاداری رفتاری است. روش نظرسنجی کمی برای دستیابی به اهداف مطالعه مورد استفاده قرار گرفت. علاوه بر این، یک تکنیک نمونه گیری آسان برای انتخاب نمونه پزشکان در حال کار در بخش مراقبت های بهداشتی دولتی در کشور اردن به کار گرفته شد. در مجموع 500 پرسشنامه توزیع شد که408 پرسشنامه در تجزیه و تحلیل آماری مورد استفاده قرار گرفت. داده ها بوسیله مدلسازی معادلات ساختاری اعمال شده برای تست مدل مطالعه مورد تجزیه و تحلیل قرار گرفت و فرضیه ها نیز از نظر کمی مورد تست و از نظر کیفی مورد بحث و بررسی قرار گرفتند. نتایج نشان داد که هر دو جنبه وفاداری مشتری ( یعنی وفاداری رفتاری و وفاداری نگرشی) به صورت مثبت بر ابعاد کلی کیفیت رابطه ( یعنی اعتماد ، رضایت و تعهد) تاثیر می گذارند و یک رهنمود را برای شرکت های داروسازی در کشور اردن در راستای تمرکز بر بهبود کیفیت روابط بین نمایندگان دارویی و پزشکی شان و پزشکان فراهم می کند و این ناشی از اهمیت چنین عواملی در بهبود وفاداری مشتری است که در مدیریت مثبت و موثر مشتریان شان و افزایش فرصت های کسب و کاری در آینده منعکس می شود.
کلمات کلیدی: وفاداری نگرشی | B2B | وفاداری رفتاری | کیفیت رابطه | SEM
مقاله ترجمه شده
10 Security analysis and new models on the intelligent symmetric key encryption
تجزیه و تحلیل امنیتی و مدلهای جدید روی رمزگذاری کلید متقارن هوشمند-2019
Data protection is achieved in modern cryptography by using encryption. Symmetric key cryptography is mainly responsible for the actual user data protection in various network protocols such as SSL/TLS and so on. The design of such encryption algorithms have always been one of the most important research targets, where heavy cryptanalysis works have been performed to evaluate the security margin. As a result, the research commu- nity is busy with fixing the security flaws based on the cryptanalysis results. Recently, the idea of building the automatic security protection scheme based on the neural network has been proposed. The encryption algorithm, which is a neural network is instead constructed by machine during the learning stage in an adversarial environment. This is a totally different approach compared with our current design principle, and could potentially change our understanding about how the (symmetric key) encryption works and what is the security requirement for the scheme. In this paper, we investigate the security of the underlined scheme which remains unexploited based on several statistical models. And fur- thermore, we strengthen the automatic encryption schemes by introducing much stronger adversaries. Our results showed that the security solutions based on the advanced deep learning techniques may start to play an important role in the future related directions.
Keywords: Neural network | Generative adversarial network | Statistical analysis | Security models | Tensorflow
مقاله انگلیسی
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