Sophisticated Features Research Articles

A shared page-aware machine learning assisted method for predicting and improving multi-level cell NAND flash memory life expectancy

NAND flash memory is widely used in consumer electronics, personal computers, and enterprise data storage servers. The most prevalent source of flash memory errors is retention errors, which are mainly caused by leakage of charges. It has been observed that inside the chip, some flash memory blocks exhibit greater endurance to the retention errors than others. In an effort to prolong life expectancy, previous studies focus on improving the Raw Bit Error Rate (RBER) metric at page and block levels, without due consideration of the geometry of shared pages. In this paper, the first insight we provide is that groups of shared pages have different RBER values, and should be analyzed separately. We use this insight to construct a machine learning model to predict the blocks which have less endurance, using the characterization data of new unused flash memory chip(s). This is accomplished by extracting location-sensitive and value-sensitive features from the shared pages group, and engineering them into more sophisticated and explainable features. Furthermore, we describe how our proposed prediction model can be used in combination with the existing flash translation layer (FTL) wear-leveling algorithm to increase lifetime. We evaluated the proposed prediction and lifetime improvement method for four different machine learning techniques, among which Support Vector Machine (SVM) achieved superior accuracy up to 85% at a lower computational overhead.

The Future Of The Arabic Language

The purpose of this research is to follow these people who are interested in Arabic in Indonesia and want Arabic to be a second language in Indonesia, and so the updated comprehensive station to reach this purpose is the bases of the desired cultural renaissance on a factual scientific, and critical vision of local, regional and global levels, with the need to adjust to the age and other opinions. Especially the future of the Islamic world, such as Indonesia. The method used in this research was library research. The data were collected, quoted, adapted, and analyzed using a content analysis approach toward the representative and relevant literature before finally being discussed and concluded. The results expected from this research were; 1). Arabic is distinguished from other languages as it is not only a means of communication but also a soul and official language among Muslims. All the fundamental main reference books of Tauhid and Syariah, which refer to Al Qur’an and As-Sunnah, were written in Arabic, 2). Arabic has been a language of thought, and a global science in medieval, which in many ways has passed European movement and development were, with its sophisticated features, it managed to be the answer to every developmental need of humans, 3). The Arabian people's economic, social, and cultural modern roles have boldened the internationalization of the Arabic language, which has been one of the official languages in the UN, which has affected more interest in learning it.

Convolutional Neural Network Classification of Pretreatment Biopsies of Rectal Adenocarcinoma Predicts Complete Clinical Response to Short-Course Radiation and Consolidation Chemotherapy

<h3>Purpose/Objective(s)</h3> Nonoperative treatment of rectal cancer (RCa) with short course radiation (SCRT) (25 Gy in 5 fractions) followed by chemotherapy results in high treatment efficacy while maintaining rectal function. Identifying patients likely to experience a complete response to nonoperative management could prevent overtreatment with chemotherapy and reduce the risk of metastatic progression for incomplete responders. We constructed a convolutional neural network (CNN) classifier of pre-treatment histopathological images to identify patients who will experience a complete response to SCRT and consolidation chemotherapy. <h3>Materials/Methods</h3> Image files of hematoxylin and eosin-stained pre-treatment rectal adenocarcinoma biopsies (n=15) were acquired using the Zeiss microscopy software suite. Files were segmented using grid tools in the ZEN software package. Grid images comprised largely of whitespace were manually removed. Grid images were exported to .jpeg format, preserving micron scale pixel resolution. This analysis utilized 32 .jpeg images belonging to complete responders and 37 .jpeg images belonging to partial responders as the basis for data augmentation to generate a more robust training cohort. We employed the architecture and weights of the well-known VGG16 model with fine tuning of the later layers of the model during training. Regularization, dropout, and early stopping were implemented to prevent overfitting. <h3>Results</h3> Utilizing the VGG16 model with frozen nodal weights resulted in poor classification accuracy and failure to improve via monitoring loss function (training accuracy: 0.3152, validation accuracy: 0.33). Unfreezing, or pruning, after the 7th layer of the VGG16 model and allowing fine tuning of the transferred architecture improved this approach. Later network architectural layers are hypothesized to represent the most sophisticated image features. When nodal weights in VGG16's later layers were allowed to be updated based on training from histopathological slide images the classifiers accuracy improved (training accuracy: 0.9457, validation accuracy: 0.944). Further, loss function was reliably minimized (Epoch 10 validation set binary cross-entropy loss: 0.610, Epoch 50 validation set binary cross-entropy loss: 0.0648). <h3>Conclusion</h3> This is the first study to utilize imaging features extracted by a CNN to predict biological behavior or responsiveness to therapy in rectal cancer. We demonstrated that a transfer learning and layer pruning approach outperforms classical transfer learning utilizing previously deeply trained architectures. While future studies to refine and validate this CNN classifier are warranted, deep learning methods can play a role in preventing overtreatment vs metastatic progression in patients receiving SCRT for RCa.

Securing the Future: Strategies for Modernizing Legacy Systems and Enhancing Cybersecurity

Legacy systems, the longstanding pillars of countless organizations, are rapidly morphing into security nightmares. Built with technologies and coding practices from a bygone era, these systems lack the sophisticated security features and robust architecture of their modern counterparts. This disparity creates a gaping security hole, making legacy systems prime targets for cyberattacks with increasingly sophisticated techniques. This article explores the inherent vulnerabilities plaguing legacy systems and presents modernization as the cornerstone solution for safeguarding sensitive data and fortifying an organization’s security posture in today’s dynamic digital landscape.

Probing Crowdedness of Artificial Organelles by Clustering Polymersomes for Spatially Controlled and pH-Triggered Enzymatic Reactions.

Most sophisticated biological functions and features of cells are based on self-organization, and the coordination and connection between their cell organelles determines their key functions. Therefore, spatially ordered and controllable self-assembly of polymersomes to construct clusters to simulate complex intracellular biological functions has attracted widespread attention. Here, we present a simple one-step copper-free click strategy to cross-link nanoscale pH-responsive and photo-cross-linked polymersomes (less than 100 nm) to micron-level clusters (more than 90% in 0.5-2 μm range). Various influencing factors in the clustering process and subsequent purification methods were studied to obtain optimal clustered polymeric vesicles. Even when polymeric vesicles separately loaded with different enzymes (glucose oxidase and myoglobin) are coclustered, the overall permeability of the clusters can still be regulated through tuning the pH values on demand. Compared with simple blending of those enzyme-loaded polymersomes, the rate of enzymatic cascade reaction increased significantly due to the interconnected complex microstructure established. The connection of catalytic nanocompartments into clusters confining different enzymes of a cascade reaction provides an excellent platform for the development of artificial systems mimicking natural organelles or cells.

Spongy all-in-liquid materials by in-situ formation of emulsions at oil-water interfaces

Printing a structured network of functionalized droplets in a liquid medium enables engineering collectives of living cells for functional purposes and promises enormous applications in processes ranging from energy storage to tissue engineering. Current approaches are limited to drop-by-drop printing or face limitations in reproducing the sophisticated internal features of a structured material and its interactions with the surrounding media. Here, we report a simple approach for creating stable liquid filaments of silica nanoparticle dispersions and use them as inks to print all-in-liquid materials that consist of a network of droplets. Silica nanoparticles stabilize liquid filaments at Weber numbers two orders of magnitude smaller than previously reported in liquid-liquid systems by rapidly producing a concentrated emulsion zone at the oil-water interface. We experimentally demonstrate the printed aqueous phase is emulsified in-situ; consequently, a 3D structure is achieved with flexible walls consisting of layered emulsions. The tube-like printed features have a spongy texture resembling miniaturized versions of “tube sponges” found in the oceans. A scaling analysis based on the interplay between hydrodynamics and emulsification kinetics reveals that filaments are formed when emulsions are generated and remain at the interface during the printing period. Stabilized filaments are utilized for printing liquid-based fluidic channels.

Smart Wide-field Fluorescence Lifetime Imaging System with CMOS Single-photon Avalanche Diode Arrays.

Wide-field fluorescence lifetime imaging (FLIM) is a promising technique for biomedical and clinic applications. Integrating with CMOS single-photon avalanche diode (SPAD) sensor arrays can lead to cheaper and portable real-time FLIM systems. However, the FLIM data obtained by such sensor systems often have sophisticated noise features. There is still a lack of fast tools to recover lifetime parameters from highly noise-corrupted fluorescence signals efficiently. This paper proposes a smart wide-field FLIM system containing a 192×128 COMS SPAD sensor and a field-programmable gate array (FPGA) embedded deep learning (DL) FLIM processor. The processor adopts a hardware-friendly and light-weighted neural network for fluorescence lifetime analysis, showing the advantages of high accuracy against noise, fast speed, and low power consumption. Experimental results demonstrate the proposed system's superior and robust performances, promising for many FLIM applications such as FLIM-guided clinical surgeries, cancer diagnosis, and biomedical imaging.

HYSGUITS: A MATLAB Graphical User Interface (GUI) for hysteresis loop simulation in Vibrating-Sample Magnetometer (VSM) data

In rock magnetism, Vibrating-Sample Magnetometer (VSM) data displays magnetic moment in specific magnetic field applied in descending and ascending magnitude, which results in a pattern called hysteresis loop. This loop characterizes different magnetic materials depending on its shape. In recent years, the usage of computer software to analyze hysteresis loop has become necessary due to its precision. Easily executable, intuitive, and user-friendly open-source programs for analyzing VSM data are still not widely available despite their necessary utilization. HYSGUITS was designed with this issue in mind to further improve the development of tools in this field. HYSGUITS is a Graphic User Interface (GUI) to analyze the hysteresis loop of VSM data. MATLAB is a suitable base for producing this GUI compared to other programming language due to its sophisticated features and clean data visualization. This software is able to visualize hysteresis loop in different ways, mainly through the difference of ascending with descending magnetization value and its 1st derivative. The GUI displays the graph as an interactive plot window which provides detailed observation on each data points, supported by features such as interpolation and smoothing. This article introduces of the functionalities of HYSGUITS and demonstrates its utilization with example use case.

DEVELOPMENT OF THE ELECTRONIC TAFSEER IN INDONESIA: DESCRIPTION OF TALKING PEN MUSHAF GRAND MAQAMAT

Recently, there have been many applications or features for interpreting the Qur'an in electronic media, such as the E-Pen. One application or feature of the interpretation of the Qur'an that is quite good is the Grand Maqamat Mushaf. This Grand Maqamat Mushaf offers many quite sophisticated features in reading and recitation. This article aims to examine and explore the Grand Maqamat Mushaf, its features, presentation of interpretations, and also its advantages and disadvantages. Using descriptive-analytical analysis of McLuhan's theory, the researchers concluded that the Talking Pen Mushaf Grand Maqamat has several features to make it easier to read, understand, and learn the Qur'an features such as translation, interpretation, the reason for the revelation (asbāb an-nuzūl) of the Qur'an, and related hadiṡ. In addition, Pen Mushaf Grand Maqamat is also equipped with readings from various stations, making it easier for Qori to learn to distinguish tones or sounds from several stations. However, it should also be noted that any sophisticated technology has advantages or disadvantages.

Abstract 5053: Artificial intelligence (AI)-based multimodal framework predicts androgen-deprivation therapy (ADT) outcomes in non-metastatic castration resistant prostate cancer (nmCRPC) from SPARTAN

Abstract Objective: AI has demonstrated great promise in learning sophisticated features and relations in data that would otherwise remain hidden to the human eye. Here, we developed a proprietary AI-based multimodal approach to integrate clinical, digitized hematoxylin-eosin (H&amp;E), and radiology bone scan (rBS) data for outcome prediction in ADT-treated nmCRPC patients. Identifying prostate cancer patients who may not benefit from ADT could improve the medical management of this disease beyond current definitive therapy. Methods: Patients in the ADT+placebo arm from SPARTAN clinical trial on nmCRPC with available clinical, H&amp;E, and rBS were used (n=154). These patients were randomly divided into 70% (n=107) discovery and 30% (n=47) hold-out test datasets. Using the discovery set, we developed and trained a multimodal approach that combines survival convolutional neural networks (SCNNs1) and Cox proportional-hazards model (CPH) to learn ADT outcomes for overall survival (OS) and time to PSA progression (TTP) from the integration of imaging data and 11 traditional clinical features (e.g., tumor stage, Gleason score, PSA). The ability of the trained framework in predicting outcomes and risk stratification was evaluated on the hold-out set. Bootstrap analysis with Wilcoxon signed rank test was used to determine the significance of the multimodal framework’s performance improvement compared to clinical CPH. Results: The multimodal framework was predictive of ADT outcomes for OS and TTP in nmCRPC patients. In SPARTAN’s hold-out set, the multimodal framework significantly improved the predictive power of clinical CPH by 14%—16% across both outcomes (Wilcoxon signed rank P&amp;lt;0.0001). In particular, the multimodal framework’s concordance index (c-index) was 0.72 for OS and 0.73 for TTP, while clinical CPH’s c-index was 0.62 for OS, and 0.64 for TTP. Further, multimodal framework significantly stratified high- from low-risk nmCRPC patients for OS and TTP (log-rank P= 0.0049-0.0072), while clinical CPH failed to stratify risk for OS (log-rank P= 0.2891). Conclusion: AI-based framework that learns from the integration of different data types improves outcome prediction in ADT-treated nmCRPC. The multimodal approach demonstrates promise in treatment decision support for the early use of androgen receptor-directed therapy and patient selection for clinical trials with novel treatment combinations. Reference: 1. Mobadersany, Pooya, et al. "Predicting cancer outcomes from histology and genomics using convolutional networks." Proceedings of the National Academy of Sciences 115.13 (2018): E2970-E2979. Conflict of interest statement P.M., J.L., D.G., C.A., S.M., S.B., M.K.Y., K.T., N.H., J.Z., J.G., N.K., and S.S.F.Y., are employees of Janssen Pharmaceutical, LLC. Citation Format: Pooya Mobadersany, Justin Lucas, Darshana Govind, Clemente Aguilar-Bonavides, Sharon McCarthy, Sabine Brookman-May, Margaret K. Yu, Ken Tian, Natalie Hutnick, Jose Zamalloa, Joel Greshock, Najat Khan, Stephen S.F. Yip. Artificial intelligence (AI)-based multimodal framework predicts androgen-deprivation therapy (ADT) outcomes in non-metastatic castration resistant prostate cancer (nmCRPC) from SPARTAN [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5053.

A Study on N-Spherical Histogram for Unbounded Tensor Features in Human Action Recognition

Recent years, Dense Trajectory features has a crucial role in extracting implicit features for action recognition. The method encloses motion and appearance descriptors to specify characteristics of each trajectory. Moreover, combining gradient and optical flow field using tensor product has made a strong positive impact on the result as we introduced in our previous work. In this paper, a breakthrough concept of encoding a high dimensional unbound space using spherical coordinate is introduced and imposed to obtain sophisticated spherical tensor features. The experimental result shows that our propose features outperforms other conventional ones and the combination of all feature channels achieves the highest accuracy rate in our selfrecorded dataset.

Smart-geofencing for system of reporting inadequate regional infrastructure using crossing and winding number

Infrastructure is a &lt;span&gt;major factor in increasing regional productivity and become the center of economic growth. problems arise when infrastructure improvements are only centered on downtown areas. One of the ways to develop regional infrastructure is with media reporting inadequate infrastructure. The use of smartphone technology and geofencing techniques are considered to be helpful in reporting media. Increasingly sophisticated built-in features in smartphones are undeniable proof of the technology’s highly impressive advancement. Mobile applications utilizing location-based services (LBS) assisted by a global positioning systems (GPS) sensor are rapidly becoming popular. One of its most-favorited features is geofencing. The volunteered-geographic-information (VGI)-based reporting system for regional infrastructure damage uses this feature to obtain the data input directly from the users. In this current research, the author uses two methods in geofencing, including crossing number (CN) and winding number (WN). Several previous studies suggest that the combination of these methods might result in faster computation, compared to the raycasting method. By using winding and crossing number methods, 98% of increased accuracy can be obtained, while a prior method could only obtain 96%. An improvement in the system’s speed is also expected.&lt;/span&gt;

Why the digital classroom is a bad model for digital learning

ABSTRACT The pandemic has compelled educational institutes across the world to hastily adopt, adapt and cope with digital learning by developing ‘digital classrooms’, virtual learning environments replete with sophisticated technological features. The eagerness of the intent to ramp up technological infrastructures, however, is not reflected in the rethinking or redesigning of pedagogies for digital learning. Technocratic ambitions often have very little, if anything, to do with pedagogy. Moreover, the digital classroom is ‘global’ by design, and the ethnocentric framework wrought upon by such designs impedes the decolonizing of the curriculum. It depletes local pedagogic cultures: local histories and cultures, political and moral economies. This article argues that imagining the digital classroom a-temporally and a-culturally, typically from a technocratic point of view, signals a tryst with (techo-)orientalism in its new avatar.

Developing a Virtual Nature Laboratory of Faculty Social Science (LAV-FIS) to Assists Field-Based Learning during Pandemic: A Need Analysis Review

The Covid-19 outbreak has affected several aspects of field-based learning. The best way to ensure the survival and safety of everyone involved in field-based learning is to adapt and integrate technology. In response to this problem, numerous innovations in learning technology that lead to the virtualization of the learning environment have begun to emerge. However, the various application products developed have fallen short of the expectations of students. As a result, in order to design the optimal virtual application for use in field-based learning, a needs analysis must be done. This study offers an understanding of student needs for virtual field-based learning applications. An online survey of 140 active university students was carried out to do so. The results showed that students require applications with sophisticated features that resemble the real world for the implementation of online field-based learning. They expect the incorporation of online meeting features, 360 Virtual Reality Panorama, access to learning videos, augmented reality, learning materials, chatbox, and multimedia into the software that will be developed.

Iconografia e interpretazioni: ‘Maria Stuarda’ di Adelaide Ristori

The article examines some iconographic sources (two portraits, some photographs, a caricature) that can be traced back to Friedrich Schiller’s interpretation of Maria Stuarda, a famous pièce de resistance of Adelaide Ristori. The identification of the two paintings that the actress used to build the scenic image of the Scottish queen has allowed us to precisely trace the portrait that inspired her for the figurative conception of the character, moving away from the author’s indications. The comparison between the photographs and the caricature allows us to reconstruct still little studied aspects of the actress’s acting style. In the third scene of the third act of Schiller’s play, Adelaide Ristori was able to blend the Italian theatrical tradition with the noblest and most sophisticated features of her new and celebrated manner. The still photos that remained to us, probably conditioned by a precise will for self-representation exercised by the actress-marquise, faithfully render the dignified and composed aspect of her theatrical gestures and pass down the sublime trait, but they do not document the traces of her more energetic physical actions, which can be traced back to her being born into a family of actors. Such traits of realistic prosaicity are instead confirmed by some testimonies and may be found in the image of her vigorous stage action documented by the caricature.

A deep learning framework with convolutional long short‐term memory for influenza‐like illness trend estimation

AbstractForecasting and modeling real‐world time series problems have been challenging for researchers due to nonlinear and complicated patterns. With the recent developments in deep learning, more accurate and efficient forecasting can be provided by learning the relationships between input and output data. This article has introduced a novel hybrid approach, called the GA‐ConvLSTM‐CNN, based on the genetic algorithm (GA), convolutional long short‐term memory (ConvLSTM) and convolutional neural network (CNN) to capture complex and sophisticated features from historical data. The GA‐ConvLSTM‐CNN approach is constructed with two‐stacked ConvLSTM networks, two‐stacked CNN, and a fully connected layer to alleviate the complexity of the real‐world time series problems with multivariate and multistep. In order to increase the accuracy of the GA‐ConvLSTM‐CNN, the genetic algorithm is adopted to simultaneously optimize the network's hyperparameters. With the aim of making comparisons, the weekly influenza‐like illness (ILI) and the weather data have been handled. The GA‐ConvLSTM‐CNN framework compared different techniques comprising machine learning and deep learning approaches. The results have indicated that the GA‐ConvLSTM‐CNN has the ability to accomplish more effective prediction accuracy and provide remarkable support for ILI outbreaks than the benchmark techniques.

2PN: A Unified Panoptic Segmentation Network with Attention Module

Comprehensive and accurate surveillance of the environment forms the basis of secure Internet of things (IoTs), the threats can be observed, and the AI services of IoT systems can be preserved. Panoptic segmentation is an efficient and popular approach for environmental surveillance based on images captured by smart sensing devices. This approach can jointly detect stuffs and things within an image and feed subsequent tasks like image detection. So far, there are many methods for panoptic segmentation which focus on extracting sophisticated visual features for segmentation. However, these efforts are both heavy on their workload and cannot clearly distinguish essential features useful for surveillance in an open environment. Therefore, this paper proposes a novel deep learning model 2PN for panoptic segmentation. The model includes a 2-way pyramid network and an attention module to learn in a more concentrated and reasonable way which enhances the feature extraction part. It strikes a balance between the computing complexity and the power of model capability. Finally, 2PN (2-way pyramid network) results are reflected on the Cityscapes dataset.

An Efficient Key Management and Multi-Layered Security Framework for SCADA Systems

Supervisory Control and Data Acquisition (SCADA) networks play a vital role in industrial control systems. Industrial organizations perform operations remotely through SCADA systems to accelerate their processes. However, this enhancement in network capabilities comes at the cost of exposing the systems to cyber-attacks. Consequently, effective solutions are required to secure industrial infrastructure as cyber-attacks on SCADA systems can have severe financial and/or safety implications. Moreover, SCADA field devices are equipped with microcontrollers for processing information and have limited computational power and resources. This makes the deployment of sophisticated security features challenging. As a result, effective lightweight cryptography solutions are needed to strengthen the security of industrial plants against cyber threats. In this paper, we have proposed a multi-layered framework by combining both symmetric and asymmetric key cryptographic techniques to ensure high availability, integrity, confidentiality, authentication and scalability. Further, an efficient session key management mechanism is proposed by merging random number generation with a hashed message authentication code. Moreover, for each session, we have introduced three symmetric key cryptography techniques based on the concept of Vernam cipher and a pre-shared session key, namely, random prime number generator, prime counter, and hash chaining. The proposed scheme satisfies the SCADA requirements of real-time request response mechanism by supporting broadcast, multicast, and point to point communication.

Detection of temporality at discourse level on financial news by combining Natural Language Processing and Machine Learning

Finance-related news such as Bloomberg News, CNN Business and Forbes are valuable sources of real data for market screening systems. In news, an expert shares opinions beyond plain technical analyses that include context such as political, sociological and cultural factors. In the same text, the expert often discusses the performance of different assets. Some key statements are mere descriptions of past events while others are predictions. Therefore, understanding the temporality of the key statements in a text is essential to separate context information from valuable predictions. We propose a novel system to detect the temporality of finance-related news at discourse level that combines Natural Language Processing and Machine Learning techniques, and exploits sophisticated features such as syntactic and semantic dependencies. More specifically, we seek to extract the dominant tenses of the main statements, which may be either explicit or implicit. We have tested our system on a labelled dataset of finance-related news annotated by researchers with knowledge in the field. Experimental results reveal a high detection precision compared to an alternative rule-based baseline approach. Ultimately, this research contributes to the state-of-the-art of market screening by identifying predictive knowledge for financial decision making.

An Analysis of Effect GSM Signal Type on Android-Based Vehicle Injector Response Time

The crime of motor vehicle theft is increasing, the automotive industry continues to develop security systems to reduce the number of thefts that occur in Indonesia through the development of sophisticated features. The addition of a global positioning system (GPS) feature on a vehicle is an alternative to supporting a security system by providing vehicle location information when theft occurs. GPS provides location information by providing microcontroller input data. GPS on the vehicle becomes very important because with the GPS module the vehicle can be found by knowing an accurate location. However, the addition of a GPS module is often difficult to find because the distance changes frequently and it takes a long time to find it. The purpose of this paper is to detect vehicle position accurately using GPS U-Blox NEO 6M and to analyze the effect of GSM signal type on the response time of long-distance vehicle injectors based on android. The research method is data analysis to make conclusions, namely the two-way factor quantitative experimental method. The independent variables used are (1) different signal types, and (2) the distance from the signal source to the gasoline engine injector response time. The conclusion obtained is that vehicle position detection using GPS U-Blox NEO 6M is accurate with the results of data analysis shifting 8.3113 meters and the interaction between distance and GSM signal types has no significant effect with the results of data analysis from the three types of Telkomsel GSM signals which are the fastest with a distance of 200 meters from the signal source with a response time of 4.956 seconds and the slowest is XL at a distance of 500 meters from the signal source with a response time of 8.5 seconds.&#x0D; Keywords: Android-Based Vehicle Injector, GSM Signal Type, Vehicle Injector