Software Services Research Articles

Analysis of Apple Financial Risk Management

In the dynamic financial environment of the 21st century, Apple Inc. is a model of innovation and market leadership. It is known for its innovative products and groundbreaking business strategies that have solidified its leadership position in consumer electronics, software and online services. In today's fast-paced and diverse financial environment, where technological advancements and regulatory changes are believed to be the norm, corporations like Apple Inc. are facing a growing number of financial threats, explaining the significance of effective financial risk management. This paper aims to investigate the numerous measures Apple Inc. has taken to reduce its financial risks especially market risks such as currency fluctuations, market volatility, and interest rate fluctuations. Particular emphasis is placed on how Apple strategically prevents the mentioned risks through internal controls and a diversified portfolio, highlighting the effectiveness of these measures. The article also analyzes how Apple manages liquidity, complies with regulatory requirements, and uses the latest advanced technology for analyzing and predicting potential financial risks. The results emphasize how crucial Apple's financial risk management is to uphold its financial standing and ensure the company's long-term survival and stability in an uncertain global economy.

Increasing venture capital investment in spinal surgery: an analysis of investments from 2000 to 2023

BACKGROUND CONTEXTVenture capital (VC) plays a vital role in advancing spinal surgery technologies, driven by the growing burden of spinal disorders and high healthcare costs. Despite increasing interest, trends in VC funding for spinal surgery remain understudied. PURPOSEThis study aimed to examine temporal and thematic trends in VC investments in spinal surgery companies from 2000 to 2023. STUDY DESIGN/SETTINGA retrospective cross-sectional study analyzing VC investment trends in privately held US-based spinal surgery companies. METHODSUsing the PitchBook database, we analyzed 1001 VC investments in 227 spinal surgery companies between 2000 and 2023. Investments were categorized into surgical devices, noninvasive devices, biotechnology, surgical software, and clinical services. Total and annual investments were quantified in USD. RESULTSFrom 2000 to 2023, $5.37 billion was invested in spine surgery, with nonsurgical devices receiving the largest share (42.6%), followed by surgical devices (26.3%) and biotechnology (22.2%). Annual investment increased by 1782%, with a decline in 2023 likely due to macroeconomic factors. CONCLUSIONSVC funding in spinal surgery has grown significantly, especially in noninvasive technologies. Further research is needed to assess the long-term impact of these investments and explore alternative financing models.

The Real-Time Detection of Vertical Displacements by Low-Cost GNSS Receivers Using Precise Point Positioning.

Vertical displacements are traditionally measured with precise levelling, which is inherently time consuming. Rapid or even real-time height determination can be achieved by the Global Navigation Satellite System (GNSS). Nevertheless, the accuracy of real-time GNSS positioning is limited, and the deployment of a network of continuously operating GNSS receivers is not cost effective unless low-cost GNSS receivers are considered. In this study, we examined the use of geodetic-grade and low-cost GNSS receivers for static and real-time GNSS levelling, respectively. The results of static GNSS levelling were processed in four different software programs or services. The largest differences for ellipsoidal/normal heights reached 0.054 m/0.055 m, 0.046 m/0.047 m, and 0.058 m/0.058 m for points WRO1, BM_ROOF, and BM_CP, respectively. In addition, the values depended on the software used and the location of the point. However, the multistage experiment was designed to analyze various strategies for GNSS data processing and to define a method for detecting vertical displacement in a time series of receiver coordinates. The developed method combined time differentiation of coordinates estimated for a single GNSS receiver using the Precise Point Positioning (PPP) technique and Butterworth filtering. It demonstrated the capability of real-time detection of six out of eight displacements in the range between 20 and 55 mm at the three-sigma level. The study showed the potential of low-cost GNSS receivers for real-time displacement detection, thereby suggesting their applicability to structural health monitoring, positioning, or early warning systems.

Smart customer service in unmanned retail store enhanced by large language model

In unmanned retail store, providing smart customer service requires two stages: understanding customer needs, and guiding the customer to the product. In this paper, we propose an end-to-end (Customer-to-Shelf) software service framework for unmanned retail. The framework integrates visual recognition technology to detect retail objects, large language models to analyze customer shopping needs and make proper recommendations. First, deep neural network based image recognition models are studied for implementing effective stock keeping units (SKUs) object recognition on the shelf. Second, a novel method is proposed to fine-tune large language models (LLMs) with limited training dataset. Metaheuristic approaches are used to optimize the mask locations in a low dimensional parameter space, resulting a more efficient parameter updating method for limited downstream data. Third, by facilitating an automatic analysis of customer preferences powered by large language models, we present a smart recommender system based on domain-specific knowledge, which completes the Customer-to-Shelf software service framework. Experimental results show that our proposed fine-tuning method, is more efficient than other state-of-the-art training methods for limited downstream domain dataset. Using fine-tuned large models, we can successfully create a seamless shopping experience for customers by understanding personalized needs and providing shopping advice in the unmanned retail store.

A Proposal of Behavior-Based Consumption Profiles for Green Software Design

Despite the increase in the efficiency of energy consumption in information and communication technology, software execution and its constraints are responsible for how energy is consumed in hardware hosts. Consequently, researchers have promoted the development of sustainable software with new development methods and tools to lessen its hardware demands. However, the approaches developed so far lack cohesiveness along the stages of the software development life cycle (SDLC) and exist outside of a holistic method for green software development (GSD). In addition, there is a severe lack of approaches that target the analysis and design stages of the SDLC, leaving software architects and designers unsupported. In this article, we introduce our behavior-based consumption profile (BBCP) external Domain-Specific Language (DSL), aimed at assisting software architects and designers in modeling the behavior of software. The models generated with our external DSL contain multiple sets of properties that characterize features of the software’s behavior. In contrast to other modeling languages, our BBCP emphasizes how time and probability are involved in software execution and its evolution over time, helping its users to gather an expectation of software usage and hardware consumption from the initial stages of software development. To illustrate the feasibility and benefits of our proposal, we conclude with an analysis of the model of a software service created using the BBCP, which is simulated using Insight Maker to obtain an estimation of hardware consumption and later translated to energy consumption.

Applying the analytic hierarchy process for investigating key indicators of responsible innovation in the Taiwan software service industry

This study explores the critical factors of responsible innovation in the Taiwan software service industry. Based on a literature review, four main dimensions have been identified as the key factors, namely, financial capital, capabilities, social capital, and reputational assets. A hierarchical structure is constructed using these factors, and the Analytic Hierarchy Process (AHP) method is used to confirm the importance of factor weights. Questionnaires were distributed to various software companies and respondents from 35 companies. Analysis results indicate that reputational assets are highly valued, followed by social capital. The software server industry prioritizes these two indicators for responsible innovation development. Results highlight the importance of responsible innovation, assisting companies in understanding its significance and formulating strategies to enhance innovation capabilities, corporate governance, and alignment with environment and market trends. This study contributes to the evolving field of innovation management and offers valuable references for software service companies.

Galaxy Helm chart: a standardized method for deploying production Galaxy servers.

The Galaxy application is a popular open-source framework for data intensive sciences, counting thousands of monthly users across more than 100 public servers. To support a growing number of users and a greater variety of use cases, the complexity of a production-grade Galaxy installation has also grown, requiring more administration effort. There is a need for a rapid and reproducible Galaxy deployment method that can be maintained at high-availability with minimal maintenance. We describe the Galaxy Helm chart that codifies all elements of a production-grade Galaxy installation into a single package. Deployable on Kubernetes clusters, the chart encapsulates supporting software services and implements the best-practices model for running Galaxy. It is also the most rapid method available for deploying a scalable, production-grade Galaxy instance on one's own infrastructure. The chart is highly configurable, allowing systems administrators to swap dependent services if desired. Notable uses of the chart include on-demand, fully-automated deployments on AnVIL, providing training infrastructure for the Bioconductor project, and as the AWS-recommended solution for running Galaxy on the Amazon cloud. The source code for Galaxy Helm is available at https://github.com/galaxyproject/galaxy-helm, the corresponding Helm package at https://github.com/CloudVE/helm-charts, and the required Galaxy container image https://github.com/galaxyproject/galaxy-docker-k8s.

About managing the number of simultaneously functioning software robots of different types

Objectives. The study sets out to justify the relevance and investigate approaches for solving the problem of managing the number of simultaneously functioning software robots of various types under conditions of limited computational resources and changes in sets of executable tasks.Methods. A proposed solution is based on models and methods of scenario management, linear programming, inventory management, queuing theory, and machine learning. The described methods are valid for different compositions and preconditions for generating initial data, as well as ensuring the relevance horizons of the obtained solutions.Results. The initial data is obtained via the presented approach for determining the computational resource parameters for operating a single software robot. The resources are determined by analyzing the composition of the software and information services used by an actual software robot. Problem statements and mathematical models are developed for cases involving scenario management and linear programming methods. Methods for real-time management of the number of software robots and their sequential local optimization are proposed based on the abovementioned solution sequences. The developed method for generating statistical data based the results of applying the sequential local optimization method is used to identify deficient and non-deficient computational resources. Some results of working in the multi-functional center of RTU MIREA software robots developed on the Аtоm.RITA platform are outlined.Conclusions. The emerging problem of managing the number of simultaneously operating software robots of various types for cases involving scenario control methods and linear programming is formalized. This problem is relevant in the field of automation of business processes of organizations. The use of mathematical methods for solving this problem opens up opportunities for expanding the functional capabilities of robotic process automation platforms, as well as increasing their economic efficiency to create competitive advantages by optimizing the use of IT infrastructure components.

Secure and efficient data storage with Rivest Shamir Adleman algorithm in cloud environment

Cloud computing rapidly is a prerequisite and releases resources with minimal management effort. The surfacing of the cloud has significantly distorted the general insight into infrastructure, software services, and development models. In contrast to single-key encryption models based on public or private keys (PKs), hybrid encryption systems combine encryption methods using symmetric or asymmetric methods. Various hybrid algorithms fail to meet users’ expectations regarding data security and cannot prevent all security risks. The secure and efficient data storage and retrieval (SEDSR) algorithm was developed for scalable key management between the content owner, cloud user, and service providers in an un-trusted cloud environment. In the implementation, the SEDSR combines the Rivest Shamir Adleman (RSA) algorithm 4096 key length with a primary symmetric key method to provide adequate and compact security with optimal retrieval systems in the cloud. Based on the experimental evaluation, the SEDSR minimizes 1.7 seconds of encryption times (ET) and 1.5 seconds of decryption time (DT) and improves by 34% throughput (TRP) compared to existing parameters.

Prototype, method, and experiment for evaluating usability of smart home user interfaces

Despite the widespread adoption of smart devices and home automation systems, users still face usability challenges. Additionally, evaluating the usability of smart home systems is not straightforward. Our paper focuses on building a Smart Home Prototype, selecting, and evaluating software User Interfaces (UI). We have chosen the smart home heating system as the basis, but the principles are applicable to other applications. To discuss smart home UIs, it is necessary to understand the components, implementation scenarios, configuration, and operation of smart home systems. Therefore, we devote considerable attention to these topics in the paper. Our smart home system utilizes a Raspberry Pi computer, temperature sensors, servo drives, an Android-based smartphone, and other necessary hardware. We installed and configured open-source software and third-party services on the experimental setup. We provided a brief description of each selected software. We used the Home Assistant operating system, its mobile application, voice assistant Alice, and Telegram chatbot. In our study, we described a methodology for evaluating user interfaces in smart homes and conducted an experiment to assess their usability. The experiment results indicated which interface was more user-friendly and identified the drawbacks of each. In conclusion, we discuss UI features that can improve usability and available options for building a smart home based on capabilities.

A rule-based decision model to support technical debt decisions: A multiple case study of web and mobile app startups

ContextSoftware startups are immature software organizations that focus on the development of a single software product or service. This organizational context accumulates a lot of technical debt to cope with constraints such as limited resources and product-market fit uncertainty. While some research has explored technical debt in startups, there is no study that investigates how software startups should make technical debt decisions throughout the startup evolution stages. ObjectiveThe objective of this study is to understand how technical debt decisions are made, and how such decisions should have been made in hindsight. MethodWe conducted a multiple embedded case study to investigate technical debt decisions in five web/mobile app startups. For each case, we interviewed the case founder and developer (a total of 17 participants across cases). In addition, we collected some public documents about the five startups. The data were analyzed using qualitative data analysis techniques. ResultsWe developed a rule-based decision model that summarizes the logic to effectively make technical debt decisions throughout the startup evolution stages. In addition, we evaluated the model by conducting follow-up interviews with three participants. ConclusionThe study provides a decision model that reflects actual practice, and is designed to help software teams in startups when making technical debt decisions throughout the startup evolution stages.

The Effect of Communication, Promotion, Employee Loyalty and Career Development on Employee Performance at PT Manufacture

This study aims to determine the effect of communication, promotion, employee loyalty and career development on the performance of PT Manufacture employees. The quantitative method was chosen as the research method because it is concrete and real. The population used is all employees of PT Manufacture, Injection Molding Factory Department, production staff and operators. Saturated samples are used in sampling. To assist in the data analysis process, the multiple linear regression analysis method was chosen in solving existing problems. Furthermore, Software Statistical Product and Service Solution (SPSS) version 26 was chosen to process and help hide hypotheses. The results of this study state that communication, promotion, employee loyalty and career development affect the performance of PT Manufacture employees partially and simultaneously.

A paradigm shift in appointment Scheduling: Introducing a decentralized integrated Online booking system

Long wait times, coordination, and integration are persistent issues in many health systems, hindering effective management of client waitlists and delaying treatment. The traditional approach of independent scheduling at each center does not allow for a coordinated response to demand fluctuations.An Integrated Online Booking system (IOB) for outpatient services is designed in this study, presenting the first central intake-based appointment system integrated with an optimization approach and a novel decentralized and distributed appointment system. This system considers the independence of centers while better responding to demand and improving overall system performance.The optimization component of the IOB system is expanded in this paper to include multiple sites, patient preferences, priorities, and wait time targets. It becomes a large-scale problem as all requests from a geographical region are combined into one stream. A decomposable algorithm based on the Alternating Direction Method of Multipliers (ADMM) is used for this system. The study employs real large-scale MRI data from all hospitals in Ontario, Canada, to demonstrate the effectiveness of the IOB system, providing insights into its potential impact on healthcare operations. The results indicate that the IOB system can outperform common appointment scheduling approaches in terms of reducing patient wait times, balancing utilization rates, improving referral patterns, and enhancing system efficiency.The proposed IOB system can bring added value to the market by speeding up diagnosis and treatment processes while improving the efficiency of healthcare systems. This platform can be considered as Software as a Service (SaaS), providing a scalable and accessible solution for managing appointments across various sectors, and leveraging blockchain technology to ensure secure, transparent, and tamper-proof appointment records. This study provides insights for decision-makers, highlighting the importance of effective scheduling in managing client waitlists during treatment in the healthcare sector, such as elective surgery, home healthcare, physical and mental therapies, and other service industries like education, public consultations, and government services. The IOB system can be integrated with Artificial Intelligence (AI)-based tools and machine learning methods and combined with other service software to optimize appointment scheduling and improve system performance.

Next Generation EU Product Liability – For Digital and Other Products

Abstract The new Directive on Product Liability is hard to overestimate, as it is nothing less than a complete overhaul of the 1985 Product Liability Directive (PLD), which still lies at the heart of EU tort law. One focus of the reform act is the challenge posed by digital transformation. To accommodate the new category of digital devices, the concept of product is widened so as to include software and related services, and the strict ex ante perspective for the finding of product defect is attenuated to account for continued control of the manufacturer post-marketing. Further to broadening the scope of application, the protective perimeter of the Directive is expanded, namely to include data used for private purposes. The second focus of the reform act, of at least equal importance, are changes to the liability regime for products of any kind, regardless of their digital nature or elements. Thus, e-commerce intermediaries such as platforms are ushered into the scope of the Directive as new defendants, alongside manufacturers, importers, and distributors. With a view to quantum, the € 500 retention for property damage had to go, together with the authority of the Member States to impose a cap on liability under the Directive. Unfortunately, there was no time to revisit the foundations of product liability. Thus, the new Directive holds on to the consumer protection gospel of product liability and excludes commercial property from the scope of protection, even though there is no good reason to discriminate against business interests. As before, national law must and will step in to fill in the gaps in Union law, perpetuating the double-track liability systems that the 1985 Directive helped to create.

The Effect of Establishing a Discharge Satisfaction Questionnaire System.

People's requirements for the quality of medical services have increased. It has become a trend to introduce digital technology into nursing work, which can improve the quality of nursing care, and patient satisfaction at discharge is an important indicator of quality control. In the past, nurses in our unit would distribute paper questionnaires on discharge satisfaction to discharged patients every day and collect and archive the data. They had to work overtime for up to 4.06 hours every month to process 150 questionnaires. This article establishes an electronic discharge satisfaction questionnaire system and puts forward the concept of cloud-based service integration in line with the characteristics of Chinese people. The concept integrates existing software platforms and services and can be reused for other target applications. It can effectively realize the function of rapid management. This convenient model improves the satisfaction of both nurses and patients.

ToxinPred 3.0: An improved method for predicting the toxicity of peptides

Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we utilized a similarity/alignment-based approach employing BLAST to predict toxic peptides, which yielded satisfactory accuracy; however, the method suffered from inadequate coverage. Subsequently, we employed a motif-based approach using MERCI software to uncover specific patterns or motifs that are exclusively observed in toxic peptides. The search for these motifs in peptides allowed us to predict toxic peptides with a high level of specificity with poor sensitivity. To overcome the coverage limitations, we developed alignment-free methods using machine/deep learning techniques to balance sensitivity and specificity of prediction. Deep learning model (ANN - LSTM with fixed sequence length) developed using one-hot encoding achieved a maximum AUROC of 0.93 with MCC of 0.71 on an independent dataset. Machine learning model (extra tree) developed using compositional features of peptides achieved a maximum AUROC of 0.95 with MCC of 0.78. We also developed large language models and achieved maximum AUC of 0.93 using ESM2-t33. Finally, we developed hybrid or ensemble methods combining two or more methods to enhance performance. Our specific hybrid method, which combines a motif-based approach with a machine learning-based model, achieved a maximum AUROC of 0.98 with MCC 0.81 on an independent dataset. In this study, all models were trained and tested on 80 % of data using five-fold cross-validation and evaluated on the remaining 20 % of data called independent dataset. The evaluation of all methods on an independent dataset revealed that the method proposed in this study exhibited better performance than existing methods. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/).

FAKTOR-FAKTOR YANG MEMPENGARUHI KINERJA KEUANGAN PERUSAHAAN PADA PERUSAHAAN MANUFAKTUR DI BEI

This research aims to obtain empirical evidence regarding the effect of leverage, firm size, intellectual capital, and working capital turnover on financial performance in manufacturing companies listed in the Indonesia Stock Exchange during the period of 2018-2020. The sample was selected using purposive sampling method and the valid data used in this study found 69 manufacturing companies. The data processing techniques using multiple regression analysis assisted by SPSS software (Statistical Product and Service Solution) version 21. The results of this study showed that firm size and working capital turnover have no significant effect on financial performance. Meanwhile, leverage has negative and significant effect on financial performance and intellectual capital has a positive and significant effect on financial performance. The implication of this study is for the role of the company management to pay attention on the level of leverage used by the company and the role of intellectual capital in increasing the company’s financial performance.

BioPipeline Creator—a user-friendly Java-based GUI for managing and customizing biological data pipelines

Bioinformatics tools are essential for performing analyses in the omics sciences. Given the numerous experimental opportunities arising from advances in the field of omics and easier access to high-throughput sequencing platforms, these tools play a fundamental role in research projects. Despite the considerable progress made possible by the development of bioinformatics tools, some tools are tailored to specific analytical goals, leading to challenges for non-bioinformaticians who need to integrate the results of these specific tools into a customized pipeline. To solve this problem, we have developed the BioPipeline Creator, a user-friendly Java-based GUI that allows different software tools to be integrated into the repertoire while ensuring easy user interaction via an accessible graphical interface. Consisting of client and server software components, BioPipeline Creator provides an intuitive graphical interface that simplifies the use of various bioinformatics tools for users without advanced computer skills. It can run on less sophisticated devices or workstations, allowing users to keep their operating system without having to switch to another compatible system. The server is responsible for the processing tasks and can perform the analysis in the user's local or remote network structure. Compatible with the most important operating systems, available at https://github.com/allanverasce/bpc.git.

Design and implementation of real-time log analysis based on ELK

With the rapid development of the IT industry, the integrated solution of real-time log analysis has become a member of the new generation of IT operation and maintenance structure. The traditional manual log analysis system is no longer suitable for the rapid development of the operation and maintenance system. The log analysis software commonly used in enterprises includes Elasticsearch, Logstash, and Kibana systems. The advantage is that it is free and open source. "Design and implementation of real-time log analysis based on ELK" was born for system maintenance and developers to understand server software and hardware information through logs, and check the cumbersome operations in the configuration process. Because frequent analysis of the log can understand the load of the server, performance security, so as to take timely measures to correct errors. If you manage dozens or hundreds of servers, there is no centralized log management system to collect and summarize the logs on all servers, which causes great difficulties for operation and maintenance managers. "Design and implementation of real-time log analysis based on ELK" uses Elasticsearch, Logstash, and Kibana to achieve unified collection and classification of server logs, which can solve the defects of traditional log analysis platforms, improve the efficiency of server application positioning problems, and help enterprises solve data center problems running problems of related applications.

A reference architecture for quantum computing as a service

Quantum computers (QCs) aim to disrupt the status-quo of computing – replacing traditional systems and platforms that are driven by digital circuits and modular software – with hardware and software that operate on the principle of quantum mechanics. QCs that rely on quantum mechanics can exploit quantum circuits (i.e., quantum bits for manipulating quantum gates) to achieve ‘quantum computational supremacy’ over traditional, i.e., digital computing systems. Currently, the issues that impede mass-scale adoption of quantum systems are rooted in the fact that building, maintaining, and/or programming QCs is a complex and radically distinct engineering paradigm when compared to the challenges of classical computing and software engineering. Quantum service orientation is seen as a solution that synergises the research on service computing and quantum software engineering (QSE) to allow developers and users to build and utilise quantum software services based on pay-per-shot utility computing model. The pay-per-shot model represents a single execution of instruction on quantum processing unit and it allows vendors (e.g., Amazon Braket) to offer their QC platforms, simulators, and software services to end-users. This research contributes by (i) developing a reference architecture for enabling Quantum Computing as a Service (QCaaS), (ii) implementing microservices with the quantum-classic split pattern as an architectural use-case, and (iii) evaluating the architecture based on practitioners’ feedback. The proposed reference architecture follows a layered software pattern to support the three phases of service lifecycle namely development, deployment, and split of quantum software services. In the QSE context, the research focuses on unifying architectural methods and service-orientation patterns to promote reuse knowledge and best practices to tackle emerging and futuristic challenges of architecting QCaaS.