Hardware Support Research Articles

영유아기 자녀를 둔 아버지의 온라인(on-line) 양육참여 경험의 의미와 개선요구

Objectives This study aimed to explore the meaning and the demand of online involvement in parenting for fathers raising young children. Methods To this end, a qualitative study centered on depth-interview with 8 fathers raising young children was conducted to specifically analyze the actual experiences of fathers in online involvement in parenting. Results First, it was analyzed that fathers' on-line involvement in parenting is meaningful as an effective channel for getting closer to their children through online and offline mixed play, personalized education through online-based customized information acquisition, further activating child-rearing-related communication, and achieving child-rearing goals and solving problems. Second, it was found that fathers required reliable standards for online parenting and educational information, family-friendly online content and platform development and dissemination, parental education support for content selection, and hardware support at the national and regional level to improve on-line involvement in parenting. Conclusions The main context of online involvement in parenting for fathers raising young children was the expansion of play between fathers and children through online and offline connection, and in this process, it was found that social and emotional meaning was formed to form a closer relationship with each other. In addition, it was found that fathers' online-based involvement in parenting enabled information tailored to their children's personal characteristics, enabling individualized education for children, and further activating communication with spouses and families and children.

From GPU to CPU (and Beyond): Extending Hardware Support in GPUSPH Through a SYCL‐Inspired Interface

ABSTRACTWhile most software is originally designed for serial or parallel execution on CPU, and porting to GPU comes later in its development, GPUSPH was designed from the ground up to run on GPUs using CUDA. Making it accessible to a wider audience by introducing support for other computational hardware, and in particular CPUs, poses challenges that are complementary to the ones normally faced when porting CPU code to GPU. We present the approach we have adopted to support CPUs as computational devices in GPUSPH with minimal code changes and low developer effort. Detailed benchmarks illustrating the performance of the implementation and its scalability across multiple cores in both single‐socket and NUMA configurations show good strong and weak scaling.

Intelligent Hardware and Software Support and Increasing the Efficiency of Welding Processes

Intelligent Hardware and Software Support and Increasing the Efficiency of Welding Processes

Evaluation of computer aided design (CAD) systems and their usage efficiency in Türkiye furniture industry

Computer-aided design (CAD), briefly refers to the utilization of computers to ensure quality and efficiency in design. CAD systems developed in the 1960s in the United States and their consequent application in all developed countries was discussed, focusing on making these systems more effective in the industry and evaluating their implementation conditions in the Turkish furniture sector. Accordingly, the objective of this current research is to summarize the effectiveness of CAD systems in the 2000s while evaluating current applications and their future impacts. In the 2000s, CAD systems required significant labor, hardware, and software support. In the current globalized world, CAD systems have become even more user-friendly with the support of artificial intelligence (AI), alongside more effective applications. The research employed face-to-face survey methods, focusing on large-scale furniture enterprises in the Marmara region. The results were evaluated using SPSS software and reliability tests. In the 2000s, approximately 25% of Turkish furniture enterprises expected CAD systems to shorten the design process, improve product quality, and enhance demand and order fulfilment times. Evaluations of today's enterprises indicate that these expectations remain. Many furniture enterprises in Türkiye utilize integrated software with CAD systems in handling orders. Approximately 46% of Turkish furniture enterprises report efficient use of these programs, while the remaining 54% cite a lack of trained personnel as the reason for inefficient use. CAD applications have contributed to the development of Turkish furniture enterprises. With CAD applications, the order process has decreased by 57 %, and production has increased by 34%. This research aims to provide insights into the current state and future potential of CAD systems in the sector by examining the developments and transformations over the past two decades.

Potamoi: Accelerating Neural Rendering via a Unified Streaming Architecture

Neural Radiance Field (NeRF) has emerged as a promising alternative for photorealistic rendering. Despite recent algorithmic advancements, achieving real-time performance on today’s resource-constrained devices remains challenging. In this paper, we identify the primary bottlenecks in current NeRF algorithms and introduce a unified algorithm-architecture co-design, Potamoi , designed to accommodate various NeRF algorithms. Specifically, we introduce a runtime system featuring a plug-and-play algorithm, SpaRW , which significantly reduces the per-frame computational workload and alleviates compute inefficiencies. Furthermore, our unified streaming pipeline coupled with customized hardware support effectively tames both SRAM and DRAM inefficiencies by minimizing repetitive DRAM access and completely eliminating SRAM bank conflicts. When evaluated against a baseline utilizing a dedicated DNN accelerator, our framework demonstrates a speed-up and energy reduction of 53.1 × and 67.7 ×, respectively, all while maintaining high visual quality with less than a 1.0 dB reduction in peak signal-to-noise ratio.

FOREIGN ECONOMIC ACTIVITY IN THE SYSTEM OF ACCOUNTING AND ANALYTICAL MANAGEMENT SUPPORT

The article decomposes the foreign economic activity of enterprise through the prism of accounting and analytical support of management. It has been determined that current trends in the development of the domestic and world economy, specificity and peculiarities of activities of individual business entities in the field of foreign economic relations, the need for further development of methodology and methods of accounting and analysis of foreign economic activity, taking into account the objectively arising needs for the development of information support for management, determine the relevance of decomposition of foreign economic activity in the system of accounting and analytical support for management. It is substantiated that the basis for the decomposition of foreign economic activity in the system of accounting and analytical support of management is the concept of the type of activity. The organisation of accounting of foreign economic activity at a particular enterprise is largely determined by the type of foreign economic activity inherent in this enterprise. In the process of production and economic activity, enterprises, as a rule, face several types of foreign economic operations simultaneously. The accounting and analytical aspects of management of foreign economic activity of enterprise are systematised. A model of decomposition of the foreign economic activity of enterprise has been developed. It is proposed that the model of decomposition of foreign economic activity of enterprise should include types of foreign economic activity and accounting and analytical aspects, grouped into blocks: key aspects; characteristics of types of foreign economic activity, organisational and legal conditions, purpose, directions and results of functioning, accounting approaches and basic analytical criteria, performance indicators, structural construction and functioning, etc. The developed model provides for the reflection and determination of performance results by types, subtypes, segments and other classification features of foreign economic activity in order to obtain analytical, detailed, transparent and relevant information based on financial, management and strategic accounting data. Formation of an integrated system of accounting and analysis of foreign economic activity is based on consideration of regulatory, contractual, structural, accounting, analytical, control and software and hardware support of foreign economic activity.

EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy

In the post-Moore's Law era, relying solely on hardware advancements for automatic performance gains is no longer feasible without increased energy consumption, due to the end of Dennard scaling. Consequently, computing accounts for an increasing amount of global energy usage, contradicting the objective of sustainable computing. The lack of hardware support and the absence of a standardized, software-centric method for the precise tracing of energy provenance exacerbates the issue. Aiming to overcome this challenge, we argue that fine-grained software energy attribution is attainable, even with limited hardware support. To support our position, we present a thread-level, NUMA-aware energy attribution method for CPU and DRAM in multi-tenant environments. The evaluation of our prototype implementation, EnergAt, demonstrates the validity, effectiveness, and robustness of our theoretical model, even in the presence of the noisy-neighbor effect. We envisage a sustainable cloud environment and emphasize the importance of collective efforts to improve software energy efficiency.

SN-FSAT system for single sensor ultrasonic guided wave beam steering and damage detection

In wireless communications as well as in structural health monitoring (SHM) beam steering presents interesting advantages. In the former it plays an important role for data directivity, which is also a perk for energy efficiency and data security; for the latter, directional inspection through guided waves leads also to energy efficiency, arbitrary dedicated space scanning, multi-function operation through multiple beams and even to application of a base-line free inspection concept. Normally, to enable this simultaneous beam control and data communication in arbitrary spatial directions (2D) over a structure, a large array of transducers is required, arranged to a similarly complex hardware and software support system. In this work, we present a system based on a dedicated sensor node (SN) and frequency steerable acoustic transducer (FSAT) combination. The SN-FSAT system is charged of the adequate power and signal management for acquisition, transmission, processing and data evaluation, required to perform spatial scanning and damage detection, as well as direction focused wireless communication tasks. These were tested over an isotropic aluminum plate-like structure by means of a double symmetric beam, via differential actuation signals, or in a high directive beam in a single direction by means of a quadrature driving signals. The complete guided wave-based setup consists of three SN-FSAT pairs. As damage a pseudo fault of a small added mass with damping tacky tape was used. The results show that damage located in different positions around the FSATs can be detected without a baseline reference and multiple directional communication paths between the SN-FSAT network can be established.

Systematization of hardware schemes of vehicle operation monitoring systems

Information monitoring is the most important stage of monitoring the technical condition of a modern car in operating conditions. Remote control of vehicle operation can be rationally formed on the basis of the systematization of the hardware schemes of the monitoring systems. In the article, a structural analysis and synthesis of possible hardware schemes of vehicle monitoring systems in interaction with the infrastructural environment is performed on the basis of the study of the technical system «Vehicle – Infrastructure». The article systematizes monitoring systems according to the types of hardware used to obtain information about the vehicle and infrastructure environment at various stages of performance of their inherent functions in operational conditions. The article shows that it is advisable to consider the means of obtaining information about the condition of operators, vehicles and objects of transportation (cargo or passengers) as separate functional elements of the monitoring information system. In order to systematize possible schemes of hardware support of monitoring systems, 5 functional elements are selected as a set of specialized hardware. For each of the functional elements of the monitoring information system, the main morphological features that characterize the type of information, methods of obtaining, processing, transmitting, analyzing and using it to solve specific tasks of vehicle operation are highlighted. A list of specific options for technical support of selected morphological features is determined, the combination of which determines the structure and functionality of the formed information monitoring system to solve the tasks in the specified operating conditions. The article shows the process of researching the formation of diagrams of the structure of the hardware of information systems for monitoring the operation of vehicles with different layouts in the form of a morphological formula. The obtained diagram of the structure of the hardware support of information systems for monitoring the operation of the vehicle can be considered as an acting way to ensure effective monitoring of the vehicle in operating conditions

A lightweight data-driven spiking neuronal network model of Drosophila olfactory nervous system with dedicated hardware support.

Data-driven spiking neuronal network (SNN) models enable in-silico analysis of the nervous system at the cellular and synaptic level. Therefore, they are a key tool for elucidating the information processing principles of the brain. While extensive research has focused on developing data-driven SNN models for mammalian brains, their complexity poses challenges in achieving precision. Network topology often relies on statistical inference, and the functions of specific brain regions and supporting neuronal activities remain unclear. Additionally, these models demand huge computing facilities and their simulation speed is considerably slower than real-time. Here, we propose a lightweight data-driven SNN model that strikes a balance between simplicity and reproducibility. The model is built using a qualitative modeling approach that can reproduce key dynamics of neuronal activity. We target the Drosophila olfactory nervous system, extracting its network topology from connectome data. The model was successfully implemented on a small entry-level field-programmable gate array and simulated the activity of a network in real-time. In addition, the model reproduced olfactory associative learning, the primary function of the olfactory system, and characteristic spiking activities of different neuron types. In sum, this paper propose a method for building data-driven SNN models from biological data. Our approach reproduces the function and neuronal activities of the nervous system and is lightweight, acceleratable with dedicated hardware, making it scalable to large-scale networks. Therefore, our approach is expected to play an important role in elucidating the brain's information processing at the cellular and synaptic level through an analysis-by-construction approach. In addition, it may be applicable to edge artificial intelligence systems in the future.

Digital Infrastructure Construction and Improvement of Non-Farm Employment Quality of Rural Labor Force—From the Perspective of Informal Employment

The pivotal role of digital infrastructure as hardware support for fostering economic efficiency in the digital economy is widely acknowledged. However, it begs the question, can the development of digital infrastructure also advance social equity, particularly concerning horizontal equity, as exemplified by the quality of non-farm employment among rural laborers, which serves as a barometer for the fairness and inclusivity of the social opportunity landscape? This article delves into the ramifications of digital infrastructure development on the quality of non-farm employment for rural laborers. Initially, it conducts a theoretical exploration of the impact and mechanisms of digital infrastructure construction on non-farm employment quality within rural labor sectors, drawing upon the Todaro model framework and existing scholarly discourse. Subsequently, by integrating data on digital infrastructure construction at the prefecture-level city level with four periods of the China Family Panel Studies (CFPS) data spanning 2014 to 2020, employing various endogenous treatment methods including two-way fixed effects, sensitivity analysis, and instrumental variable techniques, it empirically tests and analyzes the internal mechanisms. The findings reveal that digital infrastructure construction plays a beneficial role in enhancing the quality of non-farm employment for rural laborers, encompassing both subjective perceptions and objective circumstances of non-farm work. Notably, it is observed that digital infrastructure construction significantly fosters improvements in the quality of informal employment among rural laborers, with notable disparities across gender and skill levels. This discovery exerts a positive influence on advancing the sustainable development of the labor market. Specifically, female rural laborers necessitate higher skill proficiency and educational attainment to attain commensurate benefits as their male counterparts. Moreover, caution is warranted regarding the potential for digital infrastructure construction to exacerbate existing power differentials and widen socioeconomic disparities through the perpetuation of the digital divide.

Easily porting material point methods codes to GPU

AbstractThe material point method (MPM) is computationally costly and highly parallelisable. With the plateauing of Moore’s law and recent advances in parallel computing, scientists without formal programming training might face challenges in developing fast scientific codes for their research. Parallel programming is intrinsically different to serial programming and may seem daunting to certain scientists, in particular for GPUs. However, recent developments in GPU application programming interfaces (APIs) have made it easier than ever to port codes to GPU. This paper explains how we ported our modular C++ MPM code to GPU without using low-level hardware APIs like CUDA or OpenCL. We aimed to develop a code that has abstracted parallelism and is therefore hardware agnostic. We first present an investigation of a variety of GPU APIs, comparing ease of use, hardware support and performance in an MPM context. Then, the porting process of to the Kokkos ecosystem is detailed, discussing key design patterns and challenges. Finally, our parallel C++ code running on GPU is shown to be up to 85 times faster than on CPU. Since Kokkos also supports Python and Fortran, the principles presented therein can also be applied to codes written in these languages.

Unleashing the potential of spiking neural networks for epileptic seizure detection: A comprehensive review

Epilepsy is a prevalent neurological condition characterized by repeated seizures resulting from structural or functional changes in the brain. Standard treatment approaches include anti-epileptic medications and surgical interventions. However, for patients with drug-resistant epilepsy, long-term EEG recordings are essential to monitor medication efficacy and make necessary adjustments to minimize seizure frequency. Continuous monitoring of such patients requires compact and comfortable wearable devices. This review explores the potential of Spiking Neural Networks (SNNs) for low-power signal processing in neuromorphic systems, specifically addressing the demand for compact and wearable devices. SNNs offer rapid computation, minimal power consumption, and superior data representation compared to traditional artificial neural networks (ANNs). With biological plausibility and event-driven hardware support, SNNs are well-suited for modeling neural activity in epilepsy. The review delves into existing literature, investigating the potential of SNNs in seizure detection. It covers various aspects, including the neuronal system, neuromorphic computing, SNN architecture, spiking neuron models, spike encoding schemes, and training methodologies. Representative studies utilizing spike-based machine learning for seizure detection are examined. Furthermore, the review addresses gaps, challenges, strengths, weaknesses, and trade-offs in SNN implementations. By spotlighting the capabilities of SNNs, this review aims to stimulate further research and innovative approaches, contributing to the advancement of epilepsy diagnosis and management.

AIfES: A Next-Generation Edge AI Framework.

Edge Artificial Intelligence (AI) relies on the integration of Machine Learning (ML) into even the smallest embedded devices, thus enabling local intelligence in real-world applications, e.g. for image or speech processing. Traditional Edge AI frameworks lack important aspects required to keep up with recent and upcoming ML innovations. These aspects include low flexibility concerning the target hardware and limited support for custom hardware accelerator integration. Artificial Intelligence for Embedded Systems Framework (AIfES) has the goal to overcome these challenges faced by traditional edge AI frameworks. In this paper, we give a detailed overview of the architecture of AIfES and the applied design principles. Finally, we compare AIfES with TensorFlow Lite for Microcontrollers (TFLM) on an ARM Cortex-M4-based System-on-Chip (SoC) using fully connected neural networks (FCNNs) and convolutional neural networks (CNNs). AIfES outperforms TFLM in both execution time and memory consumption for the FCNNs. Additionally, using AIfES reduces memory consumption by up to 54% when using CNNs. Furthermore, we show the performance of AIfES during the training of FCNN as well as CNN and demonstrate the feasibility of training a CNN on a resource-constrained device with a memory usage of slightly more than 100 kB of RAM.

Language Translator Using Deep Learning

Abstract: Digital translation (MT) is a process of translating text from one language to another using software by including both computer and language information. Initially, the MT system acquires text translation into the source language by simply associating the meaning of the words in the source language with the target language with the help of grammar. However, such methods did not produce good results due to their failure to capture the various sentence structures in the language. This process of translation is time-consuming and requires skilled craftsmen in both languages. Subsequently, integrated translation methods such as mathematical translation (SMT) and neural translation (NMT) technology have been introduced to address the challenges of legal-based approaches. MT has already shown promising results in bilingual translation. In contrast to SMT, which requires sub-components trained separately in translation, NMT uses one large neural network for training. This paper outlines how to train a repetitive neural network for rearrangement for a source to identify. The default encoder helps to reconstruct the vectors of the target language. Therefore, powerful hardware (GPU) support is required. The GPU improves system performance by reducing training time.

Research on the Model of Public Physical Teaching in Universities in the Context of Modern Information Technology

With the advent of the information and network era, new opportunities have emerged for the development of educational and teaching work. In the context of public physical teaching in universities, the rational application of modern information technology based on actual development situations plays a significant role. It can enrich teaching methods, improve teaching efficiency, meet students' diverse learning experiences, and ensure the quality and efficiency of teaching. However, due to objective factors, there are still some problems in the actual application process, such as the limited ability of some teachers to use information technology and the lack of investment in teaching resources, which directly limit the advantages of information technology and even affect the final teaching effectiveness, leading to counterproductive effects. Therefore, in the future development, it is necessary to strengthen the information technology training for teachers, improve their application ability and information literacy, so that they can better integrate modern information technology with physical teaching. When conditions permit, increase the investment in information technology, improve infrastructure construction, and provide necessary hardware support for public physical teaching in universities.

MapReduce Algorithm on a Serverless Platform

Using the MapReduce (MR) programming technique, large-scale data processing tasks can be divided into tasks that are easier to manage and independent. The serverless implementation's performance is better than the non-serverless MR model when the model parameters are altered in the experiment. Optimizing the use of the MR model on the serverless platform can be achieved by taking into account the relationship between implementation efficiency and platform settings. Additionally, it can act as a source of inspiration for future serverless hardware support configurations. Also, the serverless platform demonstrates how it improves the effectiveness of resource utilization in machine learning training. The intended result can then be achieved by combining the results of these concurrently operating jobs on server clusters. This work adapts MR, a popular big data processing framework, to the serverless platform, emphasizing realization simulation principles and services, and then uses the results in a word count experiment. The experiment uses a word count of about eleven thousand words to evaluate how well MR is implemented on Alibaba Cloud. It is validated for execution time on the platform with varying Central Processing Unit (CPU) core counts, memory configurations, and worker counts. By testing several platform configurations, it is found that the memory configuration has very little impact on the model's execution time, while the size of the CPU core has a considerable impact on reaction time relative to the number of workers.

Scaling neural simulations in STACS

As modern neuroscience tools acquire more details about the brain, the need to move towards biological-scale neural simulations continues to grow. However, effective simulations at scale remain a challenge. Beyond just the tooling required to enable parallel execution, there is also the unique structure of the synaptic interconnectivity, which is globally sparse but has relatively high connection density and non-local interactions per neuron. There are also various practicalities to consider in high performance computing applications, such as the need for serializing neural networks to support potentially long-running simulations that require checkpoint-restart. Although acceleration on neuromorphic hardware is also a possibility, development in this space can be difficult as hardware support tends to vary between platforms and software support for larger scale models also tends to be limited.In this paper, we focus our attention on Simulation Tool for Asynchronous Cortical Streams (STACS), a spiking neural network simulator that leverages the Charm++ parallel programming framework, with the goal of supporting biological-scale simulations as well as interoperability between platforms. Central to these goals is the implementation of scalable data structures suitable for efficiently distributing a network across parallel partitions. Here, we discuss a straightforward extension of a parallel data format with a history of use in graph partitioners, which also serves as a portable intermediate representation for different neuromorphic backends.We perform scaling studies on the Summit supercomputer, examining the capabilities of STACS in terms of network build and storage, partitioning, and execution. We highlight how a suitably partitioned, spatially dependent synaptic structure introduces a communication workload well-suited to the multicast communication supported by Charm++. We evaluate the strong and weak scaling behavior for networks on the order of millions of neurons and billions of synapses, and show that STACS achieves competitive levels of parallel efficiency.

Assessing the Effectiveness of a Multicomponent Intervention on Hand Hygiene and Well-Being in Primary Health Care Centers and Schools Lacking Functional Water Supply in Protracted Conflict Settings: Protocol for a Cluster Randomized Controlled Trial.

Hand hygiene is crucial in health care centers and schools to avoid disease transmission. Currently, little is known about hand hygiene in such facilities in protracted conflict settings. This protocol aims to assess the effectiveness of a multicomponent hand hygiene intervention on handwashing behavior, underlying behavioral factors, and the well-being of health care workers and students. Moreover, we report our methodology and statistical analysis plan transparently. This is a cluster randomized controlled trial with 2 parallel arms taking place in 4 countries for 1 year. In Burkina Faso and Mali, we worked in 24 primary health care centers per country, whereas in Nigeria and Palestine, we focused on 26 primary schools per country. Facilities were eligible if they were not connected to a functioning water source but were deemed accessible to the implementation partners. Moreover, health care centers were eligible if they had a maternity ward and ≥5 employees, and schools if they had ≤7000 students studying in grades 5 to 7. We used covariate-constrained randomization to assign intervention facilities that received a hardware, management and monitoring support, and behavior change. Control facilities will receive the same or improved intervention after endline data collection. To evaluate the intervention, at baseline and endline, we used a self-reported survey, structured handwashing observations, and hand-rinse samples. At follow-up, hand-rinse samples were dropped. Starting from the intervention implementation, we collected longitudinal data on hygiene-related health conditions and absenteeism. We also collected qualitative data with focus group discussions and interviews. Data were analyzed descriptively and with random effect regression models with the random effect at a cluster level. The primary outcome for health centers is the handwashing rate, defined as the number of times health care workers performed good handwashing practice with soap or alcohol-based handrub at one of the World Health Organization 5 moments for hand hygiene, divided by the number of moments for hand hygiene that presented themselves during the patient interaction within an hour of observation. For schools, the primary outcome is the number of students who washed their hands before eating. The baseline data collection across all countries lasted from February to June 2023. We collected data from 135 and 174 health care workers in Burkina Faso and Mali, respectively. In Nigeria, we collected data from 1300 students and in Palestine from 1127 students. The endline data collection began in February 2024. This is one of the first studies investigating hand hygiene in primary health care centers and schools in protracted conflict settings. With our strong study design, we expect to support local policy makers and humanitarian organizations in developing sustainable agendas for hygiene promotion. ClinicalTrials.gov NCT05946980 (Burkina Faso and Mali); https://www.clinicaltrials.gov/study/NCT05946980 and NCT05964478 (Nigeria and Palestine); https://www.clinicaltrials.gov/study/NCT05964478. DERR1-10.2196/52959.

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning

Faced the vast amount of information, choosing the appropriate materials is a prerequisite for effective self-directed learning. The recommendation algorithm is a kind of intelligent technology that can accurately locate the required information which the users care about most. However, many recommendation techniques experience can not be trained adequately in scenarios with small sample data and extremely sparse ratings. Moreover, DLRAs (Deep learning based Recommendation Algorithms) require high hardware support. The probabilistic graph (PG) can effectively represent the implicit complex relations among nodes, but it still has the problem of sparse data sensitivity. Therefore, we propose a Matrix-Factorization-based Probabilistic Graph Model for Recommendation Algorithm (MF-PGMRA): By matrix-factorizing the sparse rating matrix, the users and items are mapped to the user/item spaces, respectively; We employ the inner product to data-enhance and overcome the problems of sparse data and cold start; Then, we build Probabilistic Graph to construct the “user-item” latent spaces and estimate the probability distribution based on expectation maximization (EM), so as to predict the ratings; Finally, we built a library management system with the recommendation module to highlight the benefits of MF-PGMRA for students’ subject learning. According to a questionnaire, we confirmed that the students are satisfied with the system from four aspects of speed, accuracy, usability and convenience, which can confirm that the library management system based on MF-PGMRA can efficiently and accurately recommend suitable materials for students from the huge amount of learning materials to improve students’ self-directed learning efficiency.