Irregular Access Research Articles

Banging My Head Against a Brick Wall! Enablers and Barriers to Nurse Practitioners Delivering Opioid Agonist Treatments in Regional/Rural Victoria, Australia.

The Victorian government's Alcohol and other drugs (AOD) workforce policy calls for greater recruitment of alcohol and other drugs nurse practitioners (AODNPs). However, frontline organisations in Victoria report several systemic barriers to their recruitment and retention. Additionally, there is scant Australian literature that examines the experiences of AODNP in opioid agonist treatment (OAT) provision in regional/rural areas. This research aims to address this gap by examining the AODNP workforce issues in delivering pharmacotherapy treatments in regional/rural areas of Victoria. Using an interpretative phenomenological approach, we conducted qualitative, in-depth interviews with 11 OAT prescribing AODNP's working in regional or rural Victoria. We analysed transcripts using reflective thematic analysis and generated three themes: (1) Professional motivations and values, (2) Enablers to professional role and (3) Barriers to professional role. The findings suggest that AODNPs in regional/rural areas are a dedicated workforce who provide holistic and integrated care for OAT clients. AODNPs play an important role in reducing and providing timely and subsidised health care for OAT clients living in regional/rural locations. AODNPs face many systemic and organisational barriers. These include inadequate funding for AODNP positions to prescribe pharmacotherapy treatments, lack of understanding of the AODNP professional role at the executive/management level within organisations and irregular access to supervision, mentoring, training and education. Also, endemic stigma in the health system acts as a barrier in recruiting and supporting AODNP positions that focus on care for OAT clients.

Clusters of Sociodemographic Characteristics and Their Association with Food Insecurity in Mexican University Students.

Food insecurity (FI) expresses irregular access to sufficient, safe, and nutritious food. We analyze the association between clusters of sociodemographic characteristics and FI in university students from Mexico. The Latin American and Caribbean Food Security Scale was applied, and household type, socioeconomic status (SES), and the characteristics of the head of the household, among others, were asked in a cross-sectional study. We performed Two-Step cluster analysis and logistic regression models. We included 466 university students (72.5% women, 47% with FI). We identified three clusters; Cluster-1 (n = 163) included a single-parent (37.4%) or co-resident/roommate (27%) household type, middle SES (63.2%); the head of the household is usually a woman (76.1%), single (71.8%), and with bachelor's degree (41.7%) or less educational level (46.6%). Cluster-2 (n = 144) included the nuclear (89.6%) household type, high SES (76.4%); the head of the household is usually a man (79.2%), in a relationship (99.3%), and with a bachelor's degree (63.2%) or graduate level (33.3%). Cluster-3 (n = 147) is very similar to Cluster-2. The difference is that Cluster-3 includes middle SES (70.1%), and the head of the household's educational level is high school or less (100%). Only belonging to Cluster-1 was positively associated with FI (OR = 1.96, 95%CI: 1.24, 3.09). These results show the interrelationships between multiple sociodemographic characteristics and should contribute to developing interventions that simultaneously address numerous sociodemographic factors to diminish FI in university students.

Factors influencing academic failure rate among first-year Indian medical students who experienced competency-based medical education curriculum during the COVID-19 pandemic.

The primary objective of the study was to assess the factors that influenced the increased failure rate among first-year Indian medical students who appeared for the Maharashtra University of Health Science (MUHS) summative university examination, having experienced the competency-based medical education (CBME) curriculum for the first time amid the coronavirus disease (COVID-19) pandemic. A retrospective cross-sectional online questionnaire-based study was undertaken between July 2021 and November 2021, in which first-year Indian medical students who were enrolled in the Bachelor of Medicine, Bachelor of Surgery (MBBS) program in the year 2019 and appeared for the MUHS summative university examination in February 2021 were invited to participate. Analysis was done using JASP (v0.16.0.0), and the odds ratio for failure was calculated at a significance level of P < 0.05. Responses were obtained from 360 individuals, of whom 74.5% had passed and 25.5% had failed (n = 360). The odds of failure in first-year MBBS summative university examination was higher in students with irregular Internet access, irregular device access, an unsupportive peer environment in college, a National Entrance cum Eligibility Test score below 500, an unsupportive family environment during the lockdown, severe stress, severe depression, testing positive for COVID-19 before the exam, death in the family during the exam, and failing in terms and prelims and students of male gender ( P < 0.01). The total failure rate of 27% in the MUHS examination was mainly attributed to interplay between the student's mental health, lack of devices or Internet, and the changed assessment part of the CBME curriculum. Surprisingly, lecture frequencies, teaching patterns, and study resources did not influence the failure rate. These results can be used to formulate interventions that will help to improve academic performance and mental health students and thus help them adapt to the new curriculum.

NEM-GNN: DAC/ADC-less, Scalable, Reconfigurable, Graph and Sparsity-Aware Near-Memory Accelerator for Graph Neural Networks

Graph neural networks (GNNs) are of great interest in real-life applications such as citation networks and drug discovery owing to GNN’s ability to apply machine learning techniques on graphs. GNNs utilize a two-step approach to classify the nodes in a graph into pre-defined categories. The first step uses a combination kernel to perform data-intensive convolution operations with regular memory access patterns. The second step uses an aggregation kernel that operates on sparse data having irregular access patterns. These mixed data patterns render CPU/GPU-based compute energy-inefficient. Von Neumann based accelerators like AWB-GCN [ 7 ] suffer from increased data movement, as the data-intensive combination requires large data movement to/from memory to perform computations. ReFLIP [ 8 ] performs resistive random access memory based in-memory (PIM) compute to overcome data movement costs. However, ReFLIP suffers from increased area requirement due to dedicated accelerator arrangement, and reduced performance due to limited parallelism and energy due to fundamental issues in ReRAM-based compute. This article presents a scalable (non-exponential storage requirement), DAC/ADC-less PIM-based combination, with (i) early compute termination and (ii) pre-compute by reconfiguring SOC components. Graph and sparsity-aware near-memory aggregation using the proposed compute-as-soon-as-ready (CAR) broadcast approach improves performance and energy further. NEM-GNN achieves ∼80–230x, ∼80–300x, ∼850–1,134x, and ∼7–8x improvement over ReFLIP, in terms of performance, throughput, energy efficiency, and compute density.

A Review of Tobacco User and Non-user

The tobacco epidemic continues to grow due to the increasingly irregular and inadequate access to health care in the population and particularly affects LMICs. The occurrence and mobility of different elements in oral smokeless tobacco products STPs were determined because the effects on human health must take into account their ability. We used data from the 2009-2010 national adult tobacco survey a national landline and cell phone survey of adults aged 18 years and older to estimate current use of any tobacco, Cigarettes, cigars, cigarillos, or small cigars or chewing tobacco, snuff or dip water pipes. We stratified estimates by gender, age, education, income, sexual orientation, and US state. Perceptions of tobacco are a relatively unexplored issue in disadvantaged populations in India and France.

IJCM_140A: A Study On The Medication Compliance Among Adult Registered Patients With Type II Diabetes Mellitus In The Urban Health Training Centre Of MMCRI, Mysuru

Background: Globally, diabetes is one of the four priority noncommunicable diseases targeted for action because of its higher burden and complications. Good Medication adherence is essential to reduce complications of diabetes and its adverse outcomes. Non-adherence to diabetic medications is one of the critical public health challenges. Irregular access to medications, increased number of drugs, presence of side effects and lack of awareness were some of the primary reasons reported for poor adherence. Hence there is a need to assess the magnitude of these reasons so that suitable interventional measures may be adopted to reduce medication non-adherence among people with diabetes. Objective: 1) To estimate the proportion of medication compliance among registered patients with type 2 diabetes mellitus in the urban health training center of MMCRI, Mysuru 2) To assess the influence of determinants on medication compliance among study subjects. Methodology: A community-based cross-sectional study was conducted among registered type 2 Diabetes Mellitus patients under Urban Health Training Centre, Nazarbad from June 2021 to May 2022 using Multistage random sampling. Medication Adherence was assessed using Morisky’s Medication Adherence Scale. Descriptive statistics, Chi-square test were used for the statistical analysis Results: 48.6% of males and 51.3% of females were involved in the study. 58.6% had good medication adherence, 5.9% had medium medication adherence and 35.45% had poor medication adherence.. In this study, we found that as the age increased the patients had poor adherence to diabetic medications. Also, socioeconomic status, gender, educational status were found to be significantly associated with adherence to medication. Conclusion: As the prevalence of Diabetes Mellitus continues to rise in India, it is increasingly important to maintain and improve the patient’s adherence to medication for to prevent complications and to provide a better quality of life.

Smart Log Analyzer for Anomaly Detection in Distributed Systems

Abstract—This research work outlines a novel approach to fortify the stability and security of distributed systems through the implementation of an AI-enabled smart log analyzer. The escalating proliferation of distributed systems has led to an unprecedented surge in the volume of generated log data, which holds crucial insights into system performance, security, and dependability. However, the substantial challenges associated with managing this data—such as its overwhelming volume, diverse nature, and real-time processing requirements—have posed significant hurdles. The proposed AI-enabled smart log analyzer, detailed in this report, harnesses the power of advanced machine learning and natural language processing techniques to address these challenges effectively. The methodology is struc- tured into three fundamental phases, namely, Data Preprocessing, Anomaly Detection, and Clustering. The Data Preprocessing phase encompasses the collection, parsing, filtering, and feature extraction of log data. Anomaly Detection integrates machine learning models to discern various anomalies, encompassing irregular access patterns, log flooding, error messages, suspicious content, and outliers in resource requests. The Clustering phase categorizes log entries into meaningful groups based on attributes such as log level, component, event, error code, and resource usage, facilitating a comprehensive understanding of system behavior. This holistic approach holds the promise of significantly enhancing system stability and security. Index Terms—Anomaly Detection, Clustering, Data Prepro- cessing, Distributed Systems, Log analyzer, System security, System stability

Factors associated with malnutrition in children under 5 years of age in the el baboso community

This study examines the factors associated with malnutrition in children under five years of age in the community of El Baboso, Ecuador. The objective was to identify and relate socioeconomic, cultural, and health service access factors with the prevalence of child malnutrition in this vulnerable population. A mixed-methods research design was used, combining qualitative and quantitative approaches, with a sample of 20 children and 16 parents. The results revealed that 80% of the children were underweight, 75% of the caregivers had no formal education, and 90% of the children did not receive exclusive breastfeeding during the first six months. Additionally, irregular access to health check-ups and a lack of nutritional knowledge were significant factors. It is concluded that implementing educational strategies and improving access to health services is essential to address child malnutrition in this community

Autovesk: Automatic Vectorized Code Generation from Unstructured Static Kernels Using Graph Transformations

Leveraging the SIMD capability of modern CPU architectures is mandatory to take full advantage of their increased performance. To exploit this capability, binary executables must be vectorized, either manually by developers or automatically by a tool. For this reason, the compilation research community has developed several strategies for transforming scalar code into a vectorized implementation. However, most existing automatic vectorization techniques in modern compilers are designed for regular codes, leaving irregular applications with non-contiguous data access patterns at a disadvantage. In this article, we present a new tool, Autovesk, that automatically generates vectorized code from scalar code, specifically targeting irregular data access patterns. We describe how our method transforms a graph of scalar instructions into a vectorized one, using different heuristics to reduce the number or cost of instructions. Finally, we demonstrate the effectiveness of our approach on various computational kernels using Intel AVX-512 and ARM SVE. We compare the speedups of Autovesk vectorized code over GCC, Clang LLVM, and Intel automatic vectorization optimizations. We achieve competitive results on linear kernels and up to 11× speedups on irregular kernels.

Efficient FPGA-Based Sparse Matrix–Vector Multiplication With Data Reuse-Aware Compression

Sparse matrix-vector multiplication (SpMV) on FPGAs has gained much attention. The performance of SpMV is mainly determined by the number of multiplications between non-zero matrix elements and the corresponding vector values per cycle. On the one side, the off-chip memory bandwidth limits the number of non-zero matrix elements transferred from the off-chip DDR to the FPGA chip per cycle. On the other side, the irregular vector access pattern poses challenges to fetch the corresponding vector values. Besides, the read-after-write (RAW) dependency in the accumulation process shall be solved to enable a fully pipelined design. In this work, we propose an efficient FPGA-based sparse matrix-vector multiplication accelerator with data reuse-aware compression. The key observation is that repeated accesses to a vector value can be omitted by reusing the fetched data. Based on the observation, we propose a reordering algorithm to manually exploit the data reuse of fetched vector values. Further, we propose a novel compressed format called data reuse-aware compressed (DRC) to take full advantage of the data reuse and a fast format conversion algorithm to shorten the preprocessing time. Meanwhile, we propose an HLSfriendly accumulator to solve the RAW dependency. Finally, we implement and evaluate our proposed design on the Xilinx Zynq-UltraScale ZCU106 platform with a set of sparse matrices from the SuiteSparse matrix collection. Our proposed design achieves an average 1.18x performance speedup without the DRC format and an average 1.57x performance speedup with the DRC format w.r.t. the state-of-the-art work respectively.

“En la omisión está la acción”: Biopolitización de la migración en tiempos de pandemia en Nuevo León, México

This article explores the intrusion of power into the lives of migrant populations in Mexico during the first year of the COVID-19 pandemic. Drawing from biopolitical theories, a documentary analysis of the state of Nuevo León, and interviews with key actors from civil society, it reveals control mechanisms that pathologize migrants. Contrary to official discourses, the exercise of power is characterized by two dynamics of disposability: an almost exclusive focus on repressive migration policies and a policy of omission that denies migrants in irregular situations access to healthcare services. Whether intentionally implemented or as an unreflective practice by authorities based on supposed health risks, these biopolitical devices align with the needs of the current hegemonic economic system, against which individuals develop strategies of resistance in an attempt to emancipate themselves from the forms of oppression that afflict them.

GraphTune: An Efficient Dependency-Aware Substrate to Alleviate Irregularity in Concurrent Graph Processing

With the increasing need for graph analysis, massive Concurrent iterative Graph Processing (CGP) jobs are usually performed on the common large-scale real-world graph. Although several solutions have been proposed, these CGP jobs are not coordinated with the consideration of the inherent dependencies in graph data driven by graph topology. As a result, they suffer from redundant and fragmented accesses of the same underlying graph dispersed over distributed platform, because the same graph is typically irregularly traversed by these jobs along different paths at the same time. In this work, we develop GraphTune , which can be integrated into existing distributed graph processing systems, such as D-Galois, Gemini, PowerGraph, and Chaos, to efficiently perform CGP jobs and enhance system throughput. The key component of GraphTune is a dependency-aware synchronous execution engine in conjunction with several optimization strategies based on the constructed cross-iteration dependency graph of chunks. Specifically, GraphTune transparently regularizes the processing behavior of the CGP jobs in a novel synchronous way and assigns the chunks of graph data to be handled by them based on the topological order of the dependency graph so as to maximize the performance. In this way, it can transform the irregular accesses of the chunks into more regular ones so that as many CGP jobs as possible can fully share the data accesses to the common graph. Meanwhile, it also efficiently synchronizes the communications launched by different CGP jobs based on the dependency graph to minimize the communication cost. We integrate it into four cutting-edge distributed graph processing systems and a popular out-of-core graph processing system to demonstrate the efficiency of GraphTune. Experimental results show that GraphTune improves the throughput of CGP jobs by 3.1∼6.2, 3.8∼8.5, 3.5∼10.8, 4.3∼12.4, and 3.8∼6.9 times over D-Galois, Gemini, PowerGraph, Chaos, and GraphChi, respectively.

Two Heads Are Better than One: Image-Point Cloud Network for Depth-Based 3D Hand Pose Estimation

Depth images and point clouds are the two most commonly used data representations for depth-based 3D hand pose estimation. Benefiting from the structuring of image data and the inherent inductive biases of the 2D Convolutional Neural Network (CNN), image-based methods are highly efficient and effective. However, treating the depth data as a 2D image inevitably ignores the 3D nature of depth data. Point cloud-based methods can better mine the 3D geometric structure of depth data. However, these methods suffer from the disorder and non-structure of point cloud data, which is computationally inefficient. In this paper, we propose an Image-Point cloud Network (IPNet) for accurate and robust 3D hand pose estimation. IPNet utilizes 2D CNN to extract visual representations in 2D image space and performs iterative correction in 3D point cloud space to exploit the 3D geometry information of depth data. In particular, we propose a sparse anchor-based "aggregation-interaction-propagation'' paradigm to enhance point cloud features and refine the hand pose, which reduces irregular data access. Furthermore, we introduce a 3D hand model to the iterative correction process, which significantly improves the robustness of IPNet to occlusion and depth holes. Experiments show that IPNet outperforms state-of-the-art methods on three challenging hand datasets.

Online Education, International Cooperation and Sustainable Development in Sudan

This article describes three online learning experiences carried out between 2017 and 2021 by Comboni College of Science and Technology, a Sudanese higher education institution in a context characterized by frequent power cuts, irregular and limited access to the internet, lack of preparedness of the teaching staff and very limited digital skills among students. These educational interventions consisted of two international cooperation projects and a learning experience through Moodle during the COVID-19 lockdown from where it was possible to collect data on students' performance and compare them with those obtained under ordinary conditions through a paired sample t-test. The research analyses these three online educational interventions, the context in which they took place and their posterior outcomes sometime later in the framework of the relation between higher education, human capital development and job insertion. The study reveals that in addition to the connectivity problems inherent to the context, some soft skills are supposed to be considered in the selection or training of the learners like time management and acquaintance with the use of an e-mail account. The research also reveals that any international e-learning project in Least Developed Country (LDC) should consider that most students do not own a personal laptop and access to the internet through their Smartphone. Moreover, students of Information and Communication Technologies (ICT) based university programs have a better predisposition than others for online programs. The study highlights the importance of computer literacy courses in the first semester of undergraduate programs for non-ICT related programs and of the presence of a physical tutor to ensure the success of any international educational project, even with students of ICT based university degrees, which may be particularly relevant once the war in progress stops and the field will be open for international cooperation in the educational field.

Distributed large-scale graph processing on FPGAs

Processing large-scale graphs is challenging due to the nature of the computation that causes irregular memory access patterns. Managing such irregular accesses may cause significant performance degradation on both CPUs and GPUs. Thus, recent research trends propose graph processing acceleration with Field-Programmable Gate Arrays (FPGA). FPGAs are programmable hardware devices that can be fully customised to perform specific tasks in a highly parallel and efficient manner. However, FPGAs have a limited amount of on-chip memory that cannot fit the entire graph. Due to the limited device memory size, data needs to be repeatedly transferred to and from the FPGA on-chip memory, which makes data transfer time dominate over the computation time. A possible way to overcome the FPGA accelerators’ resource limitation is to engage a multi-FPGA distributed architecture and use an efficient partitioning scheme. Such a scheme aims to increase data locality and minimise communication between different partitions. This work proposes an FPGA processing engine that overlaps, hides and customises all data transfers so that the FPGA accelerator is fully utilised. This engine is integrated into a framework for using FPGA clusters and is able to use an offline partitioning method to facilitate the distribution of large-scale graphs. The proposed framework uses Hadoop at a higher level to map a graph to the underlying hardware platform. The higher layer of computation is responsible for gathering the blocks of data that have been pre-processed and stored on the host’s file system and distribute to a lower layer of computation made of FPGAs. We show how graph partitioning combined with an FPGA architecture will lead to high performance, even when the graph has Millions of vertices and Billions of edges. In the case of the PageRank algorithm, widely used for ranking the importance of nodes in a graph, compared to state-of-the-art CPU and GPU solutions, our implementation is the fastest, achieving a speedup of 13 compared to 8 and 3 respectively. Moreover, in the case of the large-scale graphs, the GPU solution fails due to memory limitations while the CPU solution achieves a speedup of 12 compared to the 26x achieved by our FPGA solution. Other state-of-the-art FPGA solutions are 28 times slower than our proposed solution. When the size of a graph limits the performance of a single FPGA device, our performance model shows that using multi-FPGAs in a distributed system can further improve the performance by about 12x. This highlights our implementation efficiency for large datasets not fitting in the on-chip memory of a hardware device.

Accelerating Deformable Convolution Networks with Dynamic and Irregular Memory Accesses

Deformable convolution networks (DCNs) proposed to address the image recognition with geometric or photometric variations typically involve deformable convolution that convolves on arbitrary locations of input features. The locations change with different inputs and induce considerable dynamic and irregular memory accesses which cannot be handled by classic neural network accelerators (NNAs). Moreover, bilinear interpolation (BLI) operation that is required to obtain deformed features in DCNs also cannot be deployed on existing NNAs directly. Although a general purposed processor (GPP) seated along with classic NNAs can process the deformable convolution, the processing on GPP can be extremely slow due to the limited parallel computing capability and massive additional data movement. To address the problem, we develop a DCN accelerator on existing NNAs to support both the standard convolution and deformable convolution. Specifically, for the dynamic and irregular accesses in DCNs, we have both the input and output features divided into tiles and build a tile dependency table (TDT) to track the irregular tile dependency at runtime. With the TDT, we further develop an on-chip tile scheduler to handle the dynamic and irregular accesses efficiently. In addition, we propose a novel mapping strategy to enable parallel BLI processing on NNAs and apply layer fusion techniques for more energy-efficient DCN processing. According to our experiments, the proposed accelerator achieves orders of magnitude higher performance and energy efficiency compared to the typical computing architectures including ARM, ARM+TPU, and GPU with 6.6% chip area penalty to a classic NNA.

Permanent Ad-lib Feeders Decrease the Survival of Wintering Great Tits (Parus major)

The optimal body mass hypothesis posits that the body reserves of wintering birds are balanced between the risk of starvation and predation. In this study, we tested whether the body mass of wintering Great Tits (Parus major) was higher under conditions of less predictable food resources. We compared body mass, body mass index, the speed at take-off, and apparent survival of Great Tit adult males wintering in small urban areas either near feeders providing permanent access to food for months or near feeders providing irregular access to food. Body mass and body mass index were greater, while take-off speed and apparent survival were lower, in birds wintering near permanent feeders than birds wintering near irregular feeders. Thus, urban birds, with their predictable access to high energy food, did not follow the fattening strategy predicted by the optimal body mass hypothesis. This study shows that regular excess amounts of high-energy food may affect urban birds’ physiological and behavioral strategies in a non-adaptive way. We recommend irregular feeding of wintering birds and the placing of feeders in places that are safe against attacking predators.

Adaptive Regression Prefetching Algorithm by Using Big Data Application Characteristics

This paper presents an innovative prefetching algorithm for a hybrid main memory structure, which consists of DRAM and phase-change memory. To enhance the efficiency of hybrid memory hardware in serving big data technologies, the proposed design employs an application-adaptive algorithm based on big data execution characteristics. Specifically optimized for graph-processing applications, which exhibit complex and irregular memory access patterns, a dual prefetching scheme is proposed. This scheme comprises a fast-response model with low-cost algorithms for regular memory access patterns and an intelligent model based on an adaptive Gaussian-kernel-based machine-learning prefetch engine. The intelligent model can acquire knowledge from real-time data samples, capturing distinct memory access patterns via an adaptive Gaussian-kernel-based regression algorithm. These methods allow the model to self-adjust its hyperparameters at runtime, facilitating the implementation of locally weighted regression (LWR) for the Gaussian process of irregular access patterns. In addition, we introduced an efficient hybrid main memory architecture that integrates two different kinds of memory technologies, including DRAM and PCM, providing cost and energy efficiency over a DRAM-only memory structure. Based on the simulation-based experimental results, our proposed model achieved performance enhancement of 57% compared to the conventional DRAM model and of approximately 12% compared to existing prefetcher-based models.

Digital information literacy skills and use of electronic resources by humanities graduate students at Kenneth Dike Library, University of Ibadan, Nigeria

PurposeThe purpose of this paper is to assess the level of digital information literacy (DIL) skill and use of electronic resources by humanities graduate students at Kenneth Dike Library, University of Ibadan, Nigeria.Design/methodology/approachThis paper uses the survey research design and 200 graduate students from the 12 departments that made up the Faculty of Arts in the University of Ibadan participated in the study. A self-devised structured questionnaire was used as instrument for data collection. A pilot questionnaire was first sent to a small random sample of the respondents, with feedback used to fine-tune the final questionnaire. Respondents were requested to rate their level of proficiency in the use of digital devices, web-based tasks, information finding, evaluation and utilisation of available e-resources and challenges encountered. Ethical consideration of informed consent, institutional permission, confidentiality and anonymity of participants was strictly followed. Data collected were analysed and result presented using descriptive statistics including frequencies, percentage, mean and inferential statistics such as regression analysis and Pearson’s product moment correlation coefficient were used to test the research question and hypothesis, respectively.FindingsHumanities graduate students at the University of Ibadan possessed high level of DIL skills in respect of digital devices usage, web-based tasks, information finding and evaluation, but low in e-resources utilisation. This study identified inadequate knowledge of e-resources availability, irregular internet access, inadequate training on e-resources utilisation, inadequate staff assistance, lack of continuity in e-resources subscription and paucity of local contents in the e-resources as main challenges encountered by graduate students in the use of e-resources. To ensure that those who can most benefit from e-resources utilisation are not further marginalised, this study recommends that active steps should be taken to increase e-resources awareness, regular internet access, training/support, continuity of e-resources subscription and increased local content so that all may benefit from the opportunities of the information age.Originality/valueThis paper has demonstrated that DIL skills can enhance effective utilisation of e-resources if users have adequate knowledge of e-resources availability, regular internet access, adequate training and assistance on e-resources utilisation, continuity in database subscription and adequate local contents e-resources.

Compressed Geometric Arrays for Point Cloud Processing

The ever-increasing demand for 3D modeling in the emerging immersive applications has made point clouds an essential class of data for 3D image and video processing. Tree based structures are commonly used for representing point clouds where pointers are used to realize the connection between nodes. Tree-based structures significantly suffer from irregular access patterns for large point clouds. Memory access indirection in such structures is disruptive to bandwidth efficiency and performance. In this paper, we propose a point cloud representation format based on compressed geometric arrays (CGA). Then, we examine new methods for point cloud processing based on CGA. The proposed format enables a higher bandwidth efficiency via eliminating memory access indirections (i.e., pointer chasing at the nodes of tree) thereby improving the efficiency of point cloud processing. Our experimental results show that using CGA for point cloud operations achieves 1328x speed up, 1321x better bandwidth utilization, and 54% reduction in the volume of transferred data as compared to the state-of-the-art tree-based format from point cloud library (PCL).