Software Approach Research Articles

Using checksum to improve the reliability of embedded convolutional neural networks

Convolutional Neural Networks (CNNs) have been used in resource-constrained edge devices, including safety-critical applications. However, recent studies demonstrate that soft errors may result in CNN prediction failures. Many approaches are proposed at the end of layers to detect computation errors. Still, these approaches incur severe overheads or cannot detect errors in memory, e.g., traditional Double Modular Redundancy (DMR) can protect data integrity in memory, but it incurs significant overheads on time and memory. Some existing DNN-specific approaches are lightweight to protect DNNs from transient faults but cannot provide high fault coverage to detect permanent faults in memory. Thus, we propose two low-cost software approaches, channel-based weight checksum and layer-based output checksum, which protect the integrity of weights and layer outputs in memory. We perform a large number of fault injections to evaluate the performance of the proposed approaches and compare them with other approaches. The results show that combining the proposed approaches can provide approximately 100 % fault coverage for weights and layer outputs in memory. The combination increases total time and memory overheads by 4 % and 17 %, respectively, for DNNs that run sequentially. In addition, we observe that the layer-based output checksum can improve the error resilience of register files because most SDCs caused by register file errors are induced by the control flow error and the pointer error. Finally, we present a case study on MobileNet to show that our approaches can be generalized to other DNN libraries that run parallel by dividing the single-layer execution into N parts. When N is 2, our approaches increase total time and memory overheads by 7.4 % and 31 %, respectively.

A Scheme for Detecting the Sinkhole for Secured WSN

Because of the limited computation capability as well as transmissions being broadcasted in a wireless sensor network (WSN) they are supposed to be more susceptible for attacks related to the security. As present wireless sensor networks have low-power constraints as well as increased complexity, thus for nodes’ performance analysis related to the embedded software and network simulation efficient approaches are required. Additionally, as these networks are used to deal with the sensitive information and operated in the adverse unattended environments, thus, security feature must be added in most of these wireless sensor networks. In this paper a novel scheme for detecting various sinkhole nodes for wireless sensor network (WSN). The results of this proposed scheme show the 1.75% fake positive rate and 96% of detection rate. In comparison to the previous schemes, these aspects are considerably better. In addition to these aspects, our scheme also achieves the communication as well as computational efficiencies. As a result of which, this proposed scheme proved to have better results in many applications.

Approximate function memoization

SummaryFunction memoization is an optimization technique that reduces a function call overhead when the same input appears again. A table that stores the previous result is searched and used to skip the repeated computation. This way, it increases the performance of the function call. In this article, we propose a software approach of function memoization to improve computing efficiency by bypassing the execution of the function implemented using approximate computing techniques. Searching overhead is a primary concern in any memoization technique proposed so far. In traditional function memoization, the input arguments are first searched in the look‐up table (LUT) for an exact match, and the corresponding result is extracted for further use. But, in this article, a decision‐making rule is proposed to help us decide whether to search the LUT or go for the actual computation. This decision‐making model is implemented through Bloom filter and Cantor's pairing function. Because Bloom filter sometimes produces false‐positive results, we suggest a simple approximation technique that searches the LUT for an approximate match rather than an exact match. The proposed model also contains a bypass algorithm implemented through C++ code that identifies the trivial computations from the input argument of the candidate function. By this, we can avoid the actual calculation and generate the result directly. Here, trivial computation identifies one or more input arguments that are either 0 or . To analyze the effectiveness of our proposed technique, we conducted several experiments using the benchmarks from the AxBench suite. We found that our result outperforms some of the methods proposed so far in terms of energy consumption and quality of results, particularly in image processing applications.

AutoRelax: HW-SW Co-Optimization for Efficient SpGEMM Operations With Automated Relaxation in Deep Learning

We propose a HW-SW co-optimization technique to perform energy-efficient spGEMM operations for deep learning. First, we present an automated pruning algorithm, named AutoRelax, that allows some level of relaxation to achieve higher compression ratio. As the benefit of the proposed pruning algorithm may be limited by the sparsity level of a given weight matrix, we present additional steps to further improve its efficiency. Along with the software approach, we also present a hardware architecture for processing sparse GEMM operations to maximize the benefit of the proposed pruning algorithm and sparse matrix format. To validate the efficiency of our co-optimization methodology, we evaluated the proposed method on three benchmarks, language modeling, speech recognition and image classification. As a result, our approach improves the on-chip performance on spGEMM operations by 9.50 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 27.57% and achieves 15.35 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 33.28% energy reduction considering DRAM accesses over other sparse accelerators.

Feature Clustering and Ensemble Learning Based Approach for Software Defect Prediction

Objective:: Defects in delivered software products not only have financial implications but also blemish the reputation of the organisation and lead to wastage of time and human resource. This paper aims to detect defects in software modules. Methods:: Our approach sequentially combines SMOTE algorithm to deal with class imbalance problem, K - means clustering algorithm to obtain a set of key features based on inter-class and intra-class coefficient of correlation and ensemble modelling to predict defects in software modules. After cautious examination, an ensemble framework of XGBoost, Decision Tree and Random Forest is used for prediction of software defects owing to numerous merits of ensembling approach. Results:: We have used five open-source datasets from NASA Promise Repository for Software Engineering. The result obtained from our approach has been compared with that of individual algorithms used in ensemble. A confidence interval for the accuracy of our approach with respect to performance evaluation metrics namely Accuracy, Precision, Recall, F1 score and AUC score has also been constructed at a significance level of 0.01. Conclusion:: Results have been depicted pictographically.

Reducing the number of accepted species in Aspergillus series Nigri.

The Aspergillus series Nigri contains biotechnologically and medically important species. They can produce hazardous mycotoxins, which is relevant due to the frequent occurrence of these species on foodstuffs and in the indoor environment. The taxonomy of the series has undergone numerous rearrangements, and currently, there are 14 species accepted in the series, most of which are considered cryptic. Species-level identifications are, however, problematic or impossible for many isolates even when using DNA sequencing or MALDI-TOF mass spectrometry, indicating a possible problem in the definition of species limits or the presence of undescribed species diversity. To re-examine the species boundaries, we collected DNA sequences from three phylogenetic markers (benA, CaM and RPB2) for 276 strains from series Nigri and generated 18 new whole-genome sequences. With the three-gene dataset, we employed phylogenetic methods based on the multispecies coalescence model, including four single-locus methods (GMYC, bGMYC, PTP and bPTP) and one multilocus method (STACEY). From a total of 15 methods and their various settings, 11 supported the recognition of only three species corresponding to the three main phylogenetic lineages: A. niger, A. tubingensis and A. brasiliensis. Similarly, recognition of these three species was supported by the GCPSR approach (Genealogical Concordance Phylogenetic Species Recognition) and analysis in DELINEATE software. We also showed that the phylogeny based on benA, CaM and RPB2 is suboptimal and displays significant differences from a phylogeny constructed using 5 752 single-copy orthologous proteins; therefore, the results of the delimitation methods may be subject to a higher than usual level of uncertainty. To overcome this, we randomly selected 200 genes from these genomes and performed ten independent STACEY analyses, each with 20 genes. All analyses supported the recognition of only one species in the A. niger and A. brasiliensis lineages, while one to four species were inconsistently delimited in the A. tubingensis lineage. After considering all of these results and their practical implications, we propose that the revised series Nigri includes six species: A. brasiliensis, A. eucalypticola, A. luchuensis (syn. A. piperis), A. niger (syn. A. vinaceus and A. welwitschiae), A. tubingensis (syn. A. chiangmaiensis, A. costaricensis, A. neoniger and A. pseudopiperis) and A. vadensis. We also showed that the intraspecific genetic variability in the redefined A. niger and A. tubingensis does not deviate from that commonly found in other aspergilli. We supplemented the study with a list of accepted species, synonyms and unresolved names, some of which may threaten the stability of the current taxonomy. Citation: Bian C, Kusuya Y, Sklenář F, D'hooge E, Yaguchi T, Ban S, Visagie CM, Houbraken J, Takahashi H, Hubka V (2022). Reducing the number of accepted species in Aspergillus series Nigri. Studies in Mycology 102: 95-132. doi: 10.3114/sim.2022.102.03.

NURA

Multi-application execution in Graphics Processing Units (GPUs), a promising way to utilize GPU resources, is still challenging. Some pieces of prior work (e.g. spatial multitasking) have limited opportunity to improve resource utilization, while others, e.g. simultaneous multi-kernel, provide fine-grained resource sharing at the price of unfair execution. This paper proposes a new multi-application paradigm for GPUs, called NURA, that provides high potential to improve resource utilization and ensure fairness and Quality-of-Service(QoS). The key idea is that each streaming multiprocessor (SM) executes the Cooperative Thread Arrays (CTAs) that belong to only one application (similar to spatial multi-tasking) and shares its unused resources with the SMs running other applications demanding more resources. NURA handles resource sharing process mainly using a software approach to provide simplicity, low hardware overhead, and flexibility.We also perform some hardware modifications as an architectural support for our software-based proposal. Our conservative analysis reveals that the hardware area overhead of our proposal is less than 1.07% with respect to the whole GPU die. Our experimental results over various mixes of GPU workloads show that NURA improves throughput by 26% compared to the state-of-the-art spatial multi-tasking, on average, while meeting QoS targets. In terms of fairness, NURA has almost similar results to spatial multitasking, while it outperforms simultaneous multi-kernel by 76%, on average.

Supervised machine learning aided behavior classification in pigeons

Manual behavioral observations have been applied in both environment and laboratory experiments in order to analyze and quantify animal movement and behavior. Although these observations contributed tremendously to ecological and neuroscientific disciplines, there have been challenges and disadvantages following in their footsteps. They are not only time-consuming, labor-intensive, and error-prone but they can also be subjective, which induces further difficulties in reproducing the results. Therefore, there is an ongoing endeavor towards automated behavioral analysis, which has also paved the way for open-source software approaches. Even though these approaches theoretically can be applied to different animal groups, the current applications are mostly focused on mammals, especially rodents. However, extending those applications to other vertebrates, such as birds, is advisable not only for extending species-specific knowledge but also for contributing to the larger evolutionary picture and the role of behavior within. Here we present an open-source software package as a possible initiation of bird behavior classification. It can analyze pose-estimation data generated by established deep-learning-based pose-estimation tools such as DeepLabCut for building supervised machine learning predictive classifiers for pigeon behaviors, which can be broadened to support other bird species as well. We show that by training different machine learning and deep learning architectures using multivariate time series data as input, an F1 score of 0.874 can be achieved for a set of seven distinct behaviors. In addition, an algorithm for further tuning the bias of the predictions towards either precision or recall is introduced, which allows tailoring the classifier to specific needs.

A comparative metabolomic investigation in fruit sections of Citrus medica L. and Citrus maxima L. detecting potential bioactive metabolites using UHPLC-QTOF-IMS

The current study focused on targeted and non-targeted metabolomics of Citrus fruit parts (exocarp, mesocarp, endocarp, and seeds) to gain a comprehensive metabolomic insight. Sections of the Citrus fruit were preliminarily examined for proximate compositions (moisture, ash, fibre, fat, and protein). Whereas ultrasonication-assisted solvent extraction revealed a higher phenolic and flavonoid content at 80% (v/v) ethanolic medium, with the highest amount in the exocarp. Using targeted metabolomics, hesperidin (3307.25 mg/100 g), naringin (4803.73 mg/100 g) were detected in C. medica and C. maxima at greater levels, respectively. Further quantitative analysis revealed the presence of phenolic acids (gallic acid, trans-ferulic acid, p-coumaric acid, trans-cinnamic acid), and polymethoxyflavones (nobiletin, and tangeretin) and detected in the order of exocarp > mesocarp > endocarp > seeds. Using an untargeted metabolomics approach, metabolite discriminations among Citrus fruit sections were illustrated by Venn-diagram, heatmap, PCA, o-PLSDA, correlation matrices, and S-plot. UHPLC-QTOF-IMS revealed 48 metabolites including phenolics, vitamins, and amino acids. Furthermore, the METLIN database leads to the identification of 202 unknown metabolites. The metabolite biosynthesis and corresponding metabolite presence in Citrus fruit sections were confirmed using pathway enrichment and mass fragmentation analysis. Finally, potential biological activities were determined using in silico PASS software approach, and free radical scavenging potential was confirmed using in vitro assays for future preventive and therapeutic applications of the identified metabolites.

Neural Network-Powered Nonlinear Compensation Framework for High-Speed Continuous Variable Quantum Key Distribution

In this paper, we present a neural network-based framework to compensate for the nonlinear distortion of high-speed continuous variable quantum key distribution (CV-QKD) systems. In practice, the nonlinear impairment of imperfect devices could bring interference to the measured quadrature values and thus compromise the parameter estimation procedure and the secure key rate. Specifically, for a high-speed CV-QKD system, the balanced homodyne detector (BHD) and the following analog-to-digital converter are the primary sources of nonlinear impairment. The nonlinear distortion effect will be more pronounced as the pulse repetition rate increases. We propose an autoencoder-based network to learn the inner patterns of distorted data and then compensate the quadrature measurements in real-time at the receiver’s side. The numerical simulation demonstrates that the excess noise induced by nonlinear impairment is reduced to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-3}$</tex-math></inline-formula> level in the shot noise unit. Consequently, such a software approach has the ability to significantly boost the bandwidth efficiency of the BHD and the secure key bit rate.

ANALYSIS OF THE FACTORS THAT INFLUENCE CUSTOMER INTEREST IN USING MOBILE BANKING AT PT. BANK NTB SYARIAH ALAS SUB-BRANCH

Many customers prefer ATMs or come directly to bank offices to do banking transactions. It can lead to crowds considering that the pandemic is not over yet. It is one of the considerations of PT. Bank NTB Syariah launched a mobile banking application as a service facility to facilitate customers' transacting. The use of mobile banking is still relatively new to the public. Currently, the number of mobile banking users of Bank NTB Syariah Alas Sub-Branch has not met the number of targets achieved in several quarters. This study aimed to analyze the factors influencing customer interest in mobile banking. The sample in this study amounted to 100, which are customers who are users of mobile banking users of PT. Bank NTB Syariah Alas Sub-Branch. The statistical tool used in this research is Structural Equation Modeling (SEM) with LISREL 8.80 statistical software approach. The results of this study indicate that the factors that influence customer interest are Perception of Information Technology (X1), Ease of Use (X2), Risk (X3), and Service Features (X4). These factors have a significant simultaneous effect. The coefficient of determination obtained is 84%, which means that customer interest in using mobile banking is influenced by elements of information technology perception, risk, trust, and service features, while the remaining 16% is influenced by other variables not discussed in this study.

Work system barriers to &amp; resilience strategies for COVID-19 PPE use in the emergency department: A qualitative interview study

Background: Emergency departments (EDs) are complex, sociotechnical, high-paced, safety-critical work systems that have been disproportionately affected by the COVID-19 pandemic. Despite training, consistent compliance with recommended PPE use during COVID-19 pandemic has been challenging. Healthcare workers (HCWs) have had adapt to overcome these challenges to ensure their own safety and patient safety. We sought to identify barriers in the work system that impede the recommended COVID-19 PPE use in EDs. Methods: We conducted semistructured, in-depth interviews over ZoomTM from August 2020–May 2021 with 45 HCWs from the ED (ie, physicians, nurses, ancillary support staff, etc) affiliated with a large, tertiary-care, academic medical center. These audio-recorded interviews were transcribed and analyzed using a hybrid (inductive and deductive) qualitative coding approach in NVivo software. The deductive portion was guided by the SEIPS work system model, a well-known human-factors conceptual framework. Results: We identified multiple work-system factors in the ED that impede compliance with the recommended COVID-19 PPE use. In addition, ED HCWs have reported making a variety of adaptations or developing strategies to overcome these barriers. Some of these adaptations were made to the PPE physically (eg, trimming portions of PPE), and others were related to the tasks and/or processes associated with PPE, such as filming their own training video demonstrating PPE donning and doffing techniques, and environment services staff checking a patient’s status with nurses prior to entering the patient’s room when there was no COVID-19 signage on the door. Conclusions: Consistent compliance with COVID-19 PPE use in ED clinical practice is challenging and can be negatively affected by a variety of work system factors. Resilience strategies developed by HCWs can provide critical information with regards to HCW needs and potential directions for innovation. Future efforts should focus on not only changing individual HCW behavior through training but also on improving the PPE and ED work system design.Funding: US CDCDisclosures: The authors gratefully acknowledge the US CDC for funding this work. This material is based upon work supported by the Naval Sea Systems Command (under contract no. N00024-13-D-6400, task order NH076). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Naval Sea Systems Command (NAVSEA) or the US CDC.

PARCSIM: a parallel computing simulator for scalable software optimization

PARCSIM is a parallel software simulator that allows a user to capture, through a graphical interface, matrix algorithm schemes that solve scientific problems. With this tool, the user can analyse the execution times that would be obtained by using different spatio-temporal mapping of computational tasks on available computational units, parallelism parameters and computational libraries. Furthermore, for complex problem models, the self-optimization engine incorporated in this tool analyses the huge tree of possible calculations grouping and mapping strategies in search of the choice that makes the best use of the available hardware resources. This tool also offers polyalgorithmic resolution by making automatically the best decision between different software approaches to solve a given problem on the hardware system available. This work shows the usefulness of this simulator to efficiently solve hierarchical problems constructed from previously modelled subproblems. This task is performed by reusing, in a scalable way, the optimization information of these subproblems to establish the best execution configuration for the composite problem.

A Survey of NFV Network Acceleration from ETSI Perspective

Network function virtualization (NFV) enables network operators to save costs and flexibility by replacing dedicated hardware with software network functions running on commodity servers. There is a high need for network acceleration to achieve performance comparable to hardware, which is vital for the implementation of NFV. The necessity of NFV acceleration stems from the lengthy packet delivery path following virtualization and the unavailability of generic operating system designs to serve network-specific scenarios. Therefore, the software approach alters the operating system’s processing architecture through Kernel Bypass or offload packet processing to hardware. A typical classification scheme divides it into two main categories based on technology with software and hardware. Only these two categories can be utilized to rapidly and easily establish a classification system. However, it is difficult to suggest the specifics and peculiarities of any acceleration approach during real-world operation. For a more comprehensive classification of NFV acceleration, we refer to the ETSI NFV architectural framework in this research. As the framework clearly illustrates, the technical infrastructure layer of NFV and the corresponding management roles provides a comprehensive and intuitive view of the differences between these acceleration technologies, solutions, and initiatives. Additionally, we conducted an analysis to identify opportunities for improvement in existing solutions and propose new research programs. We expect that NFV will increasingly rely on cloud services in the future. Since cloud services do not offer a choice of hardware, our acceleration method will be primarily software-based.

Programming socket-independent network functions with nethuns

Software data planes running on commodity servers are very popular in real deployments. However, to attain top class performance, the software approach requires the adoption of accelerated network I/O frameworks, each of them characterized by its own programming model and API. As a result, network applications are often closely tied to the underlying technology, with obvious issues of portability over different systems. This is especially true in cloud scenarios where different I/O frameworks could be installed depending on the configuration of the physical servers in the infrastructure. The nethuns library proposes a unified programming abstraction to access and manage network operations over different I/O frameworks. The library is freely available to the community under the BSD license and currently supports AF_XDP and netmap for fast packet handling along with the classic AF_PACKET and the pcap library. Network applications based on nethuns need only to be re-compiled to run over a different network API. The experiments prove that the overhead introduced by nethuns is negligible, hence making it a convenient programming platform that eases the coding process while guaranteeing high performance and portability. As proofs of concept, a handy traffic generator as well as the popular Open vSwitch application have been successfully ported and tested over nethuns.

Pharmacokinetic/pharmacodynamic modeling of roxadustat's effect on LDL cholesterol in patients in Japan with dialysis-dependent chronic kidney disease and anemia

AimsThe objective of this analysis was to develop and evaluate a pharmacokinetic/pharmacodynamic (PK/PD) model of the effect of roxadustat on low-density lipoprotein cholesterol (LDL-C) in Japanese patients with anemia of dialysis-dependent chronic kidney disease while considering the impact of covariates on model parameters. MethodsA total of 2330 LDL-C measurements from 275 patients in 3 clinical studies were analyzed using a nonlinear-mixed effects modeling approach in NONMEM software. ResultsThe PK/PD relationship between roxadustat exposure and LDL-C was well described by a kinetic-pharmacodynamic model with a physiological indirect response model as the PD component. Co-administered statin usage, sevelamer usage, type of dialysis (hemodialysis or peritoneal dialysis), and sex were selected as covariates for LDLbaseline. Weight was selected as a covariate for ID50. Imax and ID50 were estimated as 0.661 and 1.51 mg/h, respectively. ConclusionRoxadustat can decrease LDL-C independent of statins and sevelamer. Further study of the ability of roxadustat to lower LDL-C and any potential effects on outcomes is needed.

ANALYSIS OF SPECIFIC PARAMETERS FOR SKIN TUMOR CLASSIFICATION

During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of nonmalignant cutaneous diseases. They grow in melanocytes, the cells responsible for pigmentation. This type of cancer is increasing rapidly and its related mortality rate is increasing more modestly, and inversely proportional to the tumor’s thickness. The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention. In this work, we are interested in extracting all specific attributes which can be used for computer-aided diagnosis of melanoma. In the first step of the proposed work, we applied the Dull Razor [Lee T et al., Dullrazor: A software approach to hair removal from images, Cancer Control Research, British Columbia Cancer Agency, Vancouver, Canada, Vol. 21, No. 6, pp. 533–543, 1997] technique to images to reduce the influence of small structures, hairs, bubbles, light reflection. In the second step, a new fuzzy level set algorithm is proposed in order to facilitate the medical image segmentation task. It is able to directly evolve from the initial segmentation proposed that uses a spatial fuzzy clustering approach. The controlling parameters of the level set evolution are also estimated from the results of the fuzzy clustering step. This step is essential to characterize the shape of the lesion and also to locate the tumor to be analyzed. In this paper, we have also treated the necessity to extract all the specific attributes used to develop a characterization methodology that enables specialists to take the best possible diagnosis. For this purpose, our proposal relies largely on visual observation of the tumor while dealing with some characteristics as color, texture or form. The method used in this paper is called ABCD. It requires calculating four factors: Asymmetry ([Formula: see text], Border ([Formula: see text], Color ([Formula: see text], and Diversity ([Formula: see text]. Finally, these parameters are used to construct a classification module based on artificial neural network for the recognition of malignant melanoma.

Earth and Space Science Informatics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

AbstractThis article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman, et al., 2021b, https://doi.org/10.1029/2021EO153180) in Earth and Space Science Informatics (ESSI) and includes discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Global collaboration, cyberinfrastructure, and data sharing; (Section 3) Machine learning for multiscale modeling; (Section 4) Aerial and satellite remote sensing for advancing Earth system model development by integrating field and ancillary data. ESSI addresses data management practices, computation and analysis, and hardware and software infrastructure. Our role in ICON science therefore involves collaborative work to assess, design, implement, and promote practices and tools that enable effective data management, discovery, integration, and reuse for interdisciplinary work in Earth and space science disciplines. Networks of diverse people with expertise across Earth, space, and data science disciplines are essential for efficient and ethical exchanges of findable, accessible, interoperable, and reusable (FAIR) research products and practices. Our challenge is then to coordinate the development of standards, curation practices, and tools that enable integrating and reusing multiple data types, software, multi‐scale models, and machine learning approaches across disciplines in a way that is as open and/or FAIR as ethically possible. This is a major endeavor that could greatly increase the pace and potential of interdisciplinary scientific discovery.

Past, Present, and Future of Deep Brain Stimulation: Hardware, Software, Imaging, Physiology and Novel Approaches.

Deep brain stimulation (DBS) has advanced treatment options for a variety of neurologic and neuropsychiatric conditions. As the technology for DBS continues to progress, treatment efficacy will continue to improve and disease indications will expand. Hardware advances such as longer-lasting batteries will reduce the frequency of battery replacement and segmented leads will facilitate improvements in the effectiveness of stimulation and have the potential to minimize stimulation side effects. Targeting advances such as specialized imaging sequences and “connectomics” will facilitate improved accuracy for lead positioning and trajectory planning. Software advances such as closed-loop stimulation and remote programming will enable DBS to be a more personalized and accessible technology. The future of DBS continues to be promising and holds the potential to further improve quality of life. In this review we will address the past, present and future of DBS.

A Predictive Approach to Identify Geometrically Hazardous Road Segments and Evaluate the Relative Safety Effects of Design Alternatives

This study has two goals: First, to fill a gap in the use of the predictive approach for evaluating road safety performance in Ethiopia, the most recent analytical methods of the HSM predictive approach in IHSDM software were used to evaluate the safety and operational effects of the existing roadway geometric design. Second, to assure safety and a sustainable transportation system, the relative safety effects of design changes made to hazardous road segments were quantified. Based on the Crash Prediction Module (CPM) evaluation of IHSDM software, the study identified fifteen hazardous road segments on the existing rural two-lane roads of Addis Ababa to Chacha and Addis Ababa to Dillela. The design changes made to the hazardous road segments have resulted in a remarkable reduction in crash rate, especially on the first top five hazardous segments, where incredible improvements have been observed. The total safety benefits acquired by applying engineering mitigations to the fifteen identified hazardous segments are described as follows: 17.18% reduction in total crash frequency (crashes/year), 58.94% reduction in crash rate (crashes/km/year), and 58.86% reduction in travel crash rate (crashes/million veh-km). In general, the study’s findings underlined the effectiveness of performance-based road safety evaluation and design in providing safe, efficient, and economically-feasible roadway infrastructure. The IHSDM reports and graphical outputs assist decision-making in the roadway design process by providing a quantitative evaluation of the safety impact of various design features and identifying roadway segments with safety concerns. Additionally, IHSDM is a tool capable of saving time for Road Safety Audit (RSA) teams. The paper also outlined the need for a computerized crash database recording system in Ethiopia to develop jurisdiction-specific Safety Performance Functions (SPFs).