Software Implementation Research Articles

Impact of connected dispensing technology with advanced analytics in a multicenter health system.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. This study was designed to evaluate the impact of enterprise inventory optimization (EIO) technology and analytics on pharmacy labor, costs, and medication availability within a large integrated delivery network (IDN). This article describes a mixed-methods, postmarket observational study assessing the impact of a solution of disparate technologies including automated dispensing cabinets (ADCs), centralized pharmacy inventory software, and controlled substance vaults connected by an inventory optimization analytics (IOA) tool. Four study modules were implemented over a 10-month period. The intervention consisted of implementation of the IOA software, linking the disparate automated technologies. Transactional data was collected and aggregated with user perception survey data in both the pre- and postintervention periods. Descriptive and comparative statistical testing was used to assess outcomes. A total of 11 facilities with bed counts ranging between 22 and 908 beds were included in this study. At an enterprise level, users were able to complete an average of 2.8 times more periodic automated replenishment (PAR) level changes post intervention, resulting in an estimated enterprise labor avoidance of over 1 full-time equivalent (2,099 labor hours) annually. Despite an enterprise decision to increase ADC inventory on hand from a 3-day supply to a 5-day supply, 5 sites (45%) had a decrease in total inventory, while 9 sites (82%) saw a decrease in ADC inventory costs. Additionally, 7 sites (64%) saw a reduction in the ADC stockout percentage and all 11 sites (100%) saw a decrease in the central pharmacy stockout percentage post intervention. Integration and optimization of connected inventory management technology was observed to have positive impacts on improving labor productivity, reducing ADC inventory carrying costs, and increasing medication availability.

Machine Learning-Based Methods for Code Smell Detection: A Survey

Code smells are early warning signs of potential issues in software quality. Various techniques are used in code smell detection, including the Bayesian approach, rule-based automatic antipattern detection, antipattern identification utilizing B-splines, Support Vector Machine direct, SMURF (Support Vector Machines for design smell detection using relevant feedback), and immune-based detection strategy. Machine learning (ML) has taken a great stride in this area. This study includes relevant studies applying ML algorithms from 2005 to 2024 in a comprehensive manner for the survey to provide insight regarding code smell, ML algorithms frequently applied, and software metrics. Forty-two pertinent studies allow us to assess the efficacy of ML algorithms on selected datasets. After evaluating various studies based on open-source and project datasets, this study evaluated additional threats and obstacles to code smell detection, such as the lack of standardized code smell definitions, the difficulty of feature selection, and the challenges of handling large-scale datasets. The current studies only considered a few factors in identifying code smells, while in this study, several potential contributing factors to code smells are included. Several ML algorithms are examined, and various approaches, datasets, dataset languages, and software metrics are presented. This study provides the potential of ML algorithms to produce better results and fills a gap in the body of knowledge by providing class-wise distributions of the ML algorithms. Support Vector Machine, J48, Naive Bayes, and Random Forest models are the most common for detecting code smells. Researchers can find this study helpful in better anticipating and taking care of software development design and implementation issues. The findings from this study, which highlight the practical implications of ML algorithms in software quality improvement, will help software engineers fix problems during software design and development to ensure software quality.

Domain wall and magnetic tunnel junction hybrid for on-chip learning in UNet architecture

We present a spintronic device based hardware implementation of UNet for segmentation tasks. Our approach involves designing hardware for convolution, deconvolution, rectified activation function (ReLU), and max pooling layers of the UNet architecture. We designed the convolution and deconvolution layers of the network using the synaptic behavior of the domain wall MTJ. We also construct the ReLU and max pooling functions of the network utilizing the spin hall driven orthogonal current injected MTJ. To incorporate the diverse physics of spin-transport, magnetization dynamics, and CMOS elements in our UNet design, we employ a hybrid simulation setup that couples micromagnetic simulation, non-equilibrium Green’s function, and SPICE simulation along with network implementation. We evaluate our UNet design on the CamVid dataset and achieve segmentation accuracies of 83.71% on test data, on par with the software implementation with 821 mJ of energy consumption for on-chip training over 150 epochs. We further demonstrate nearly one order of magnitude (10×) improvement in the energy requirement of the network using unstable ferromagnet (Δ = 4.58) over the stable ferromagnet (Δ = 45) based ReLU and max pooling functions while maintaining similar accuracy. The hybrid architecture comprising domain wall MTJ and unstable FM-based MTJ leads to an on-chip energy consumption of 85.79 mJ during training, with a testing energy cost of 1.55 µJ.

R2C-GAN: Restore-to-Classify Generative Adversarial Networks for blind X-ray restoration and COVID-19 classification

Restoration of poor-quality medical images with a blended set of artifacts plays a vital role in a reliable diagnosis. As a pioneer study in blind X-ray restoration, we propose a joint model for generic image restoration and classification: Restore-to-Classify Generative Adversarial Networks (R2C-GANs). This is the first generic restoration approach forming an Image-to-Image translation task from poor-quality having noisy, blurry, or over/under-exposed images to high-quality image domain where forward and inverse transformations are learned using unpaired training samples. Simultaneously, the joint classification preserves the diagnostic-related label during restoration. Each R2C-GAN is equipped with operational layers/neurons in a compact architecture. The proposed joint model successfully restores images while achieving state-of-the-art Coronavirus Disease 2019 (COVID-19) classification with above 90% in F1-Score. In qualitative analysis, the restoration performance is confirmed by medical doctors where 68% of the restored images are selected against the original images. We share the software implementation at https://github.com/meteahishali/R2C-GAN.

Hyperparameter optimization of two-branch neural networks in multi-target prediction

As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past decade. However, software implementations like Auto-WEKA and Auto-sklearn typically focus on classical machine learning (ML) tasks such as classification and regression. Our work can be seen as the first attempt at offering a single AutoML framework for most problem settings that fall under the umbrella of multi-target prediction, which includes popular ML settings such as multi-label classification, multivariate regression, multi-task learning, dyadic prediction, matrix completion, and zero-shot learning. Automated problem selection and model configuration are achieved by extending DeepMTP, a general deep learning framework for MTP problem settings, with popular hyperparameter optimization (HPO) methods. Our extensive benchmarking across different datasets and MTP problem settings identifies cases where specific HPO methods outperform others.

Implementation of Hardware, Software and Workstation Information Security Measures in Federal University Libraries of Nigeria

Implementation of Hardware, Software and Workstation Information Security Measures in Federal University Libraries of Nigeria

Multi-criteria Model for Production Plan Management of a Machine-building Enterprise

The concept of developing an intelligent system to manage the planning of a manufacturing enterprise, and a description of the functionality and modules of the information system are given. In general, operational planning is a complex task of optimizing the construction of a production plan, which includes several criteria and faces various limitations. Multi-criteria is associated with the need for order fulfillment priority in accordance with the law, and the non-linearity of the task is due to technological limitations of production. One of the criteria is to maximize the fulfillment of contractual obligations to supply products to customers. Another criterion is to maximize profits from the sale of the entire range of manufactured products. The limitations in this task are the storage capacity, availability of raw materials and components, the time of operations, the workload of equipment and the priority of orders. Two approaches to solve the optimization problem of managing the production plan are considered, and block diagrams of the implementation of the proposed algorithms are provided. In the first approach, the initial problem is reduced to a combination of linear programming problems, for which the simplex method or the Gomori method are used in practice. A more effective approach to solving this extreme problem is the use of a genetic algorithm and its modifications. The scientific novelty of the conducted research lies in the systematic application of mathematical and computer modeling methods to build an optimal operational production plan. The experience of practical implementation of software systems confirms that intelligent production plan management systems are able to increase the efficiency of enterprise business proce sses, allowing organizations to plan production and allocate resources in market conditions optimally.

STUDY ON AUTOMATION PROGRAMMING TO MONITOR AND CONTROL A SCREW ELECTRO-COMPRESSOR

The article describes the programming architecture of a programmable logic controller (PLC) and an operator panel that is part of an automation cabinet of a screw electro-compressor. Behind the operation of a screw electro-compressor, there is a programming logic presented in the article that is implemented according to the requirements of each project. The article presents all the states of a screw compressor that are implemented in the PLC by a programmer in the form of software sequences. The article depicts the logic diagrams used to facilitate the software implementation process. Following the software implementation in the PLC of the logic diagrams, the article has a second software that contains a series of screens for the operator panel. The operator panel is a touch-screen type and communicates with the PLC through a defined communication protocol displaying the parameter values in real time on the programmed screens. The software developed for the operator panel by a programmer has the role of facilitating a human operator to monitor parameters and transmit commands to the screw electro-compressor. The article ends by presenting the conclusions that argue the efficiency and safety in operation through the presented software concept of a screw electro-compressor.

Development and research of filter models communication and control systems configured for the required set of parameters

Problem statement. A digital filter refers to a hardware or software implementation of a mathematical algorithm that converts a digital signal in a certain way. The classification of digital filters is usually based on the functional characteristics of digital filtering algorithms. Digital filters may have parameters that cannot be implemented in analog filters, do not require periodic monitoring and calibration, and one filter can process several input channels or signals, while the accuracy is limited only by the bit depth used. The implementation of digital signal processing units using programmable logic integrated circuits (FPGAs) will significantly simplify hardware costs and make the most effective use of these digital filter capabilities. Goal. Conducting research in the field of digital signal processing in order to obtain fundamentally new software and hardware architectures, developing an RTL model of a complex functional block (SF block) of a digital signal processing system, its verification both at the logical and physical level. Results. In the course of the scientific research, a software and hardware model of the filter was developed to adjust the parameters during operation, taking into account their architectural advantages and disadvantages. In order to verify and confirm the adequacy of the methodology developed in the course of the study, an SF model was created-a digital signal processing unit. This model has been implemented and physically verified on the basis of a debugging board using FPGAs. Practical significance. The technique presented in this paper will allow the development of software and hardware devices for digital signal processing, with the possibility of their configuration at the software level for the required set of processing parameters, which in turn will significantly save resources spent on development.

Formal design, verification and implementation of robotic controller software via RoboChart and RoboTool

Current practice in simulation and implementation of robot controllers is usually undertaken with guidance from high-level design diagrams and pseudocode. Thus, no rigorous connection between the design and the development of a robot controller is established. This paper presents a framework for designing robotic controllers with support for automatic generation of executable code and automatic property checking. A state-machine based notation, RoboChart, and a tool (RoboTool) that implements the automatic generation of code and mathematical models from the designed controllers are presented. We demonstrate the application of RoboChart and its related tool through a case study of a robot performing an exploration task. The automatically generated code is platform independent and is used in both simulation and two different physical robotic platforms. Properties are formally checked against the mathematical models generated by RoboTool, and further validated in the actual simulations and physical experiments. The tool not only provides engineers with a way of designing robotic controllers formally but also paves the way for correct implementation of robotic systems.

Design and implementation of embedded hardware in furniture control based on DSP

The continuous progress of embedded technology and Internet of Things technology has accelerated the arrival of the era of home intelligence. By designing a smart home control system based on embedded DSP technology, it can detect temperature, humidity, flammable gas leakage, smoke, fire, and other indicators in the home environment, and carry out intelligent control of the detected security risks. The core of the system is the smart home management terminal, which is responsible for the network interconnection inside and outside the home. Terminal hardware includes a high-speed DSP chip, various interface modules, and so on. The software adopts the C/OS-II operating system to realize multi-task processing. This paper briefly summarizes DSP technology and embedded technology, and proposes the composition of intelligent home control system based on embedded DSP technology, as well as the hardware and software design and implementation of the system. Through the smart home system, users can control home conditions and obtain public service information anytime and anywhere, which is highly practical.

Experience in the development and use of simulators for professional training of specialists in the electric power industry

Issues of development of simulators for training and testing of electrical engineers and students of electrical engineering specialties were considered. They are based on modern knowledge in the field of electricity. The main characteristics of simulators and requirements for their computer support are given. The developments implement a new method of practical training and testing of electrical engineers and can be used at all stages of electrical engineer training. The considered simulators are based on a combination of practical and theoretical knowledge of the electric power industry. The purpose of the developments is to activate and increase the efficiency of the educational process due to the introduction of new learning technologies. The technical capabilities of the software implement both individual and team work of students and electric power personnel. It was noted that in recent years, two professional simulators, implemented in the form of two laboratory workshops, have been developed and implemented in the educational process at the department of electrical energy transmission of National technical university “Kharkiv polytechnic institute”. The first helps students study the main provisions of the Normative Documentation in the power industry, the second – the rules and sequence of operational switching in electrical networks. In addition, experts determine the high efficiency of training systems based on precedent-analytical models. The exercise program according to the plan-do-check-act cycle is considered. Its basis is the well-known concept of continuous quality improvement “Plan-Do-Check-Act” (Deming's cycle). For the electric power industry, a plan-do-check-act cycle approach can consist of cyclical planning of activities, implementation of planned actions, verification of results obtained and correction of non-conformance to expectations. The precedent-scenario method of constructing a training system based on the software platform of the training system based on the plan-do-check-act scenario is presented in detail.

C2B: A Semantic Source Code Retrieval Model using CodeT5 and Bi-LSTM

To enhance the software implementation process, developers frequently leverage preexisting code snippets by exploring an extensive codebase. Existing code search tools often rely on keyword- or syntactic-based methods and struggle to fully grasp the semantics and intent behind code snippets. In this paper, we propose a novel hybrid C2B model that combines CodeT5 and bidirectional long short-term memory (Bi-LSTM) for source code search and recommendation. Our proposed C2B hybrid model leverages CodeT5’s domain-specific pretraining and Bi-LSTM’s contextual understanding to improve code representation and capture sequential dependencies. As a proof-of-concept application, we implemented the proposed C2B hybrid model as a deep neural code search tool and empirically evaluated the model on the large-scale dataset of CodeSearchNet. The experimental findings showcase that our methodology proficiently retrieves pertinent code snippets and surpasses the performance of prior state-of-the-art techniques.

Clinical Applications of Myocardial Work in Echocardiography: A Comprehensive Review.

Left ventricular (LV) global longitudinal strain (GLS) has recently garnered attention as a reliable and objective method for evaluating LV systolic function. One of the key advantages of GLS is its ability to detect subtle abnormalities even when the ejection fraction (EF) appears to be preserved. However, it is important to note that GLS, much like LVEF, is significantly influenced by load conditions. In recent years, researchers and clinicians have been exploring noninvasive myocardial work (MW) quantification as an innovative tool for assessing myocardial function. This method integrates measurements of strain and LV pressure, providing a comprehensive evaluation of the heart's performance. Notably, MW offers an advantage over GLS and LVEF because it provides a load-independent assessment of myocardial performance. The implementation of commercial echocardiographic software that facilitates the noninvasive calculation of MW has significantly broadened the scope of its application. This advanced technology is now being utilized in multiple clinical settings, including ischemic heart disease, valvular diseases, cardiomyopathies, cardio-oncology, and hypertension. One of the fundamental aspects of MW is its correlation with myocardial oxygen consumption, which allows for the assessment of work efficiency. Understanding this relationship is crucial for diagnosing and managing various cardiac conditions. The aim of this review is to provide an overview of the noninvasive assessment of myocardial by echocardiography, from basic principles and methodology to current clinical applications.

Software architecture and implementation based on multi-core digital signal processors

Abstract Embedded detection systems are a critical research topic in intelligent monitoring equipment. In response to the current challenges in balancing real-time performance, accuracy, and stability in embedded hardware detection systems, this paper proposes a software architecture system based on multi-core digital signal processing (DSP), incorporating the image detection Canny algorithm into this system. Initially, the paper discusses the selection criteria for multi-core DSPs and then introduces a multi-core self-check function that can effectively ensure system stability. It also proposes a multi-core communication handshake protocol that significantly enhances the system’s real-time performance. Furthermore, an improved multi-core master-slave architecture is presented, which includes the addition of a timer to the conventional architecture, providing a temporal guarantee for system operation. Finally, the Canny algorithm is selected and applied to this software architecture, with parallel acceleration processing applied to parts of the algorithm. Operational tests have demonstrated that the system maintains stable internal data flow, with its detection accuracy and speed meeting the set requirements.

Teleworking and its relationship with workplace technostress post COVID-19

The overwhelming use of information and communications technologies (ICT) that is being implemented in organizations makes it necessary to analyze the effects of teleworking on employees. The objective of this review was to analyze the scientific literature published on the relationship between teleworking and work-related technostress in the post-COVID-19 context. The method used had a qualitative approach, carrying out a systematic review that respected the PRISMA guidelines. 20 empirical investigations found in Scopus and Google Scholar were analyzed, published between 2019 and 2023. The results categorically indicate that there is a close relationship between teleworking and work-related technostress. The factors are social isolation, complex software, conflict between work and the personal environment, the absence of interpersonal relationships, and technological uncertainty. Therefore, it is suggested that organizations promote work autonomy and effective self-leadership, in addition to considering measures such as training in the use of digital platforms, the implementation of high-quality software that allows better interaction, and the organization of periodic face-to-face activities.

Pemanfaatan Metode TOPSIS dalam Membangun Sistem Pendukung Keputusan untuk Menentukan Jenis Penyakit Tanaman Gandum; Studi Kasus di Ladang Gandum Alahan Panjang, Sumatera Barat

Wheat (Triticum aestivum L.) is an annual plant that can grow in highland areas. There are many types of diseases that attack wheat plants, making it difficult for farmers to determine the type of disease. Therefore, it is necessary to build a Decision Support System (DSS) that can determine the type of disease that attacks wheat plants. This research aimed to design and develop a Decision Support System (DSS) application to identify the types of diseases affecting wheat by observing the physical characteristics of the plants. These disease characteristics (criteria) were then inputed into the system, which used an algorithm based on the Technique for Order of Preference by Similarity to a Ideal Solution (TOPSIS) method. The DSS method used was TOPSIS, which considered four criteria: Flower, Stem, Seed, and Leaf. The application development method for the DSS used the Waterfall model, which consisted of the following stages: requirements, definition, system and software design, implementation, and unit testing. A DSS application using the TOPSIS method can determine the type of wheat disease based on physical characteristics as criteria that are inputed into the application. This application can help wheat farmers identify the diseases affecting their plants. The use of the TOPSIS method algorithm in developing the SPK application is an innovation that can help Alahan Panjang wheat farmers in detecting diseases types affecting their crops based on visible physical characteristics. Automatically, the system provides recommendations on the type of disease affecting the wheat plants.

Constraints that Hinders Secure Software Implementation and Development Processes

Abstract: Internet of a things have change the entire world in the 21st century, software applications and system are now becoming part of human endeavour. The widely reliance software system is now an issue of concern, because almost everything aspect of human existences dwelled on it. Considering such intimacy and it used in storing of sensitive information and data in almost domain used by human, the need to come up with strategies that will overcome factors affecting implementation of secure software development process has become paramount important. However, most research stress building a secured software but with limited emphasis on the challenges that lead to poor implementation of secure software development process. The paper aims at evaluating the constraints that hinders secure software implementation and development process. Forty-five studies were reviewed using the Systematic literature review and concluded thirteen (13) factors affects successful implementation of secure software development practice.

Optimization of technological parameters of the methane tri-reforming process based on the phenomenon of conservatively perturbed equilibrium

course under the condition of the phenomenon of conservative perturbed equilibrium (CPE) are considered. It is shown that the onset of the CPE point - an extremum of the over-equilibrium concentration - occurs at a certain point in time. Therefore, one of the tasks of optimal control is to calculate the time of occurrence of this point and determine the effect of temperature and pressure on its position. The modeling of the TRM process is performed for an ideal displacement reactor. The DWSIM software product was used to model the TRM process. The kinetic equations were programmed in the form of scripts. The position of the over-equilibrium concentration point was also investigated depending on the initial composition of the mixture. According to the modeling results, it was concluded that the position of the CPE point for product H₂ is constant, and depends only on pressure and temperature and does not depend on the initial composition of the mixture. This allows us to conclude that the control can ensure that the product yield with the maximum concentration is achieved. The problem of optimizing the initial composition of the mixture for the methane tri-reforming process using CPE is formulated: the objective function and constraints are defined, and the solution method is chosen. The optimization problem is to maximize the target product of methane tri-reforming, H2. The solution to the problem is the composition of the mixture (concentrations of CH4, H2O, CO2). As for the constraints, the pressure and temperature are limited by the physicochemical properties of the process and the design features of the reactor, the elemental balance must be maintained, the key component at the input must have an equilibrium concentration at a given pressure and temperature, and the concentrations of other components must be deviated from their equilibrium values. A software implementation of the optimization problem based on the Optimization Toolbox Matlab was performed. The research results indicate an increase in the efficiency of the process (increase in the output of the target component) and the possibility of implementing optimal solutions in real time, which opens up prospects for implementation in existing control systems and development of decision-making systems.

Advanced Innovations in Electronic Control Units: Enhancing Performance and Reliability with AI

This paper proposes a methodology for the design of electronic control unit (ECU) hardware units with increased performance and reliability. Today's vehicles are equipped with dozens of ECUs that significantly influence the system's efficiency, reliability, performance, and safety. With the increased complexity of control algorithms and the environmental constraints that automotive systems operate, the robustness and efficiency of the ECUs are of utmost importance. In this work, an approach is proposed based on combining hardware redundancy, commercial field programmable gate arrays (FPGAs), and artificial intelligence strategies to provide increased redundancy checks and robust control. An additional redundancy is added to the hardware architecture of the ECU to include a parallel hardware unit. The two controlling units operate in parallel. The output of each of them is compared, allowing redundancy checks in the computation of the output variable (oV) of the system. The mathematical model of the ECU depicts the governing equations of the ECU in the form of differential equations, which results in a corresponding state-space configuration. These mathematical models are encoded into the field programmable gate array (FPGA) and processed in hardware, leading to an equivalent software-based implementation. To analyze the performance of both models within the ECU, an artificial neural network (ANN)-based strategy is proposed. The ANN depicts the governing equations of the ECU in the form of differential equations encoded in the form of sigmoidal functions. To analyze the reliability of the control action in the ECU, the temperature of the system is increased, which leads to a random variation of the system parameters. The variability of the ECU parameters leads to a deviation in the computation of the oV and the corresponding control action. The robustness of the control is determined in such conditions. A control law is determined to guarantee proper control action under variations in the governing equations of the system. This control law is represented by a simple algebraic equation, which can then be cast in various control strategies, such as look-up tables or fuzzy logic controllers. Several cases are simulated to assess the performance of the proposed control law for the hardware redundancy scheme, for the ANN-based equivalent software implementation approach, and for the additional fuzzy logic controller. The simulation results are analyzed concerning the requested oV and give insight into the performance and reliability of the proposed dedicated ECU design.