Hasil untuk "Management information systems"

Allen H. Huang, R. Lehavy, Amy Y. Zang et al.

Allen H. Huang,a Reuven Lehavy,b Amy Y. Zang,a Rong Zhengc aDepartment of Accounting, Hong Kong University of Science and Technology, Kowloon, Hong Kong; bRoss School of Business, University of Michigan, Ann Arbor, Michigan 48109; cDepartment of Information Systems, Business Statistics, and Operations Management, Hong Kong University of Science and Technology, Kowloon, Hong Kong Contact: allen.huang@ust.hk (AHH); rlehavy@umich.edu, http://orcid.org/0000-0003-3875-8848 (RL); amy.zang@ust.hk (AYZ); rzheng@ust.hk (RZ)

S. Dara

Earlier models of integrated pest management (IPM) focused on ecological aspects of pest management. With the recent developments in agricultural technology, modern communication tools, changing consumer trends, increased awareness for sustainably produced food systems, and globalization of trade and travel, there seems to be a need to revisit the IPM paradigm as appropriate for modern times. A new model, built on earlier models based on ecological and economic aspects, is expanded and reconfigured to include management, business, and sustainability aspects and emphasize the importance of research and outreach. The management aspect contains four components of IPM that address the pest management options, the knowledge and resources to develop management strategies, the management of information and making timely decisions, and the dissemination or sharing of information. With the business aspect that includes the producer, consumer, and seller, and the sustainability aspect that covers economic viability, environmental safety, and social acceptability, the new model presents the human, environmental, social, and economic factors that influence the food production.

Valérie A. Bedia-Tanoh, Abibatou Konaté-Touré, Orphée M. A. Kangah-Kouakou et al.

Abstract Background Accurate malaria diagnosis and reporting are critical for effective case management and surveillance. In sub-Saharan Africa, rapid diagnostic tests (RDTs) are widely used to support clinical decision-making. However, limited data exist on the accuracy of recorded RDT results reported to health information systems. This study assessed the accuracy of recording of malaria RDT results documented in health facility registers in Côte d’Ivoire, as part of a multi-country evaluation. Methods A mixed-methods, observational study was conducted across 16 primary health care facilities in two regions of Côte d’Ivoire between August and December 2023. For each patient tested for malaria, a digital image of the RDT cassette was captured and linked to the corresponding register entry. An independent panel of trained reviewers interpreted the RDT images, which served as the reference standard. Agreement between panel and register results was assessed using percent agreement and Cohen’s kappa (κ). The positive predictive value (PPV) and negative predictive value (NPV) were calculated for the register result. Meta-regression was used to identify facility, healthcare worker (HCW), and patient characteristics associated with agreement. Results Of 11,161 matched RDT images and register entries, 57.9% were interpreted as positive by the external panel. Overall agreement between panel and register results was strong (κ = 0.83, 95% CI 0.77, 0.88), with a PPV of 90.7% and NPV of 94.8%. However, negative or invalid results were more frequently incorrectly recorded as positive (5.9%) than the reverse (2.0%). Recording errors occurred more often among patients noted in the register with a diagnosis of malaria or antimalarial prescription, suggesting potential systematic bias. District, patient volume, HCW cadre, and education level of the health worker were associated with agreement. Notably, HCWs who frequently performed or recorded RDTs had lower agreement levels. Most HCWs believed that a negative RDT could miss malaria and that treatment could still be warranted. Conclusion The results from the study demonstrate that RDT results are recorded fairly accurately in Côte d’Ivoire. However, the disproportionate misclassification of negative results as positive may distort malaria surveillance data and test positivity rates. Strategies such as regular comparison of RDT cassettes with register entries, enhanced HCW training, and reinforcement of adherence to diagnostic guidelines may improve data quality and support evidence-based decision-making.

Zhenkai Qin, Zhenkai Qin, Dongze Wu et al.

IntroductionThis paper introduces an intelligent question-answering system designed to deliver personalized medical information to diabetic patients. By integrating large language models with knowledge graphs, the system aims to provide more accurate and contextually relevant medical guidance, addressing the limitations of traditional healthcare systems in handling complex medical queries.MethodsThe system combines a Neo4j-based knowledge graph with the Baichuan2-13B and Qwen2.5-7B models. To enhance performance, Low-Rank Adaptation (LoRA) and prompt-based learning techniques are applied. These methods improve the system's semantic understanding and ability to generate high-quality responses. The system's performance is evaluated using entity recognition and intent classification tasks.ResultsThe system achieves 85.91% precision in entity recognition and 88.55% precision in intent classification. The integration of a structured knowledge graph significantly improves the system's accuracy and clinical relevance, enhancing its ability to provide personalized medical responses for diabetes management.DiscussionThis study demonstrates the effectiveness of integrating large language models with structured knowledge graphs to improve medical question-answering systems. The proposed approach offers a promising framework for advancing diabetes management and other healthcare applications, providing a solid foundation for future personalized healthcare interventions.

Le Liu, Yu Kawano, Ming Cao

For systems whose states implicate sensitive information, their privacy is of great concern. While notions like differential privacy have been successfully introduced to dynamical systems, it is still unclear how a system's privacy can be properly protected when facing the challenging yet frequently-encountered scenario where an adversary possesses prior knowledge, e.g., the steady state, of the system. This paper presents a new systematic approach to protect the privacy of a discrete-time linear time-invariant system against adversaries knowledgeable of the system's prior information. We employ a tailored \emph{pointwise maximal leakage (PML) privacy} criterion. PML characterizes the worst-case privacy performance, which is sharply different from that of the better-known mutual-information privacy. We derive necessary and sufficient conditions for PML privacy and construct tractable design procedures. Furthermore, our analysis leads to insight into how PML privacy, differential privacy, and mutual-information privacy are related. We then revisit Kalman filters from the perspective of PML privacy and derive a lower bound on the steady-state estimation-error covariance in terms of the PML parameters. Finally, the derived results are illustrated in a case study of privacy protection for distributed sensing in smart buildings.

Alexis Llewellyn, Thai Han Phung, Marta O Soares et al.

Background Magnetic resonance imaging localises cancer in the prostate, allowing for a targeted biopsy with or without transrectal ultrasound-guided systematic biopsy. Targeted biopsy methods include cognitive fusion, where prostate lesions suspicious on magnetic resonance imaging are targeted visually during live ultrasound, and software fusion, where computer software overlays the magnetic resonance imaging image onto the ultrasound in real time. The effectiveness and cost-effectiveness of software fusion technologies compared with cognitive fusion biopsy are uncertain. Objectives To assess the clinical and cost-effectiveness of software fusion biopsy technologies in people with suspected localised and locally advanced prostate cancer. A systematic review was conducted to evaluate the diagnostic accuracy, clinical efficacy and practical implementation of nine software fusion devices compared to cognitive fusion biopsies, and with each other, in people with suspected prostate cancer. Comprehensive searches including MEDLINE, and Embase were conducted up to August 2022 to identify studies which compared software fusion and cognitive fusion biopsies in people with suspected prostate cancer. Risk of bias was assessed with quality assessment of diagnostic accuracy studies-comparative tool. A network meta-analysis comparing software and cognitive fusion with or without concomitant systematic biopsy, and systematic biopsy alone was conducted. Additional outcomes, including safety and usability, were synthesised narratively. A de novo decision model was developed to estimate the cost-effectiveness of targeted software fusion biopsy relative to cognitive fusion biopsy with or without concomitant systematic biopsy for prostate cancer identification in biopsy-naive people. Scenario analyses were undertaken to explore the robustness of the results to variation in the model data sources and alternative assumptions. Results Twenty-three studies (3773 patients with software fusion, 2154 cognitive fusion) were included, of which 13 informed the main meta-analyses. Evidence was available for seven of the nine fusion devices specified in the protocol and at high risk of bias. The meta-analyses show that patients undergoing software fusion biopsy may have: (1) a lower probability of being classified as not having cancer, (2) similar probability of being classified as having non-clinically significant cancer (International Society of Urological Pathology grade 1) and (3) higher probability of being classified at higher International Society of Urological Pathology grades, particularly International Society of Urological Pathology 2. Similar results were obtained when comparing between same biopsy methods where both were combined with systematic biopsy. Evidence was insufficient to conclude whether any individual devices were superior to cognitive fusion, or whether some software fusion technologies were superior to others. Uncertainty in the relative diagnostic accuracy of software fusion versus cognitive fusion reduce the strength of any statements on its cost-effectiveness. The economic analysis suggests incremental cost-effectiveness ratios for software fusion biopsy versus cognitive fusion are within the bounds of cost-effectiveness (£1826 and £5623 per additional quality-adjusted life-year with or with concomitant systematic biopsy, respectively), but this finding needs cautious interpretation. Limitations There was insufficient evidence to explore the impact of effect modifiers. Conclusions Software fusion biopsies may be associated with increased cancer detection in relation to cognitive fusion biopsies, but the evidence is at high risk of bias. Sufficiently powered, high-quality studies are required. Cost-effectiveness results should be interpreted with caution given the limitations of the diagnostic accuracy evidence. Study registration This trial is registered as PROSPERO CRD42022329259. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: 135477) and is published in full in Health Technology Assessment; Vol. 28, No. 61. See the NIHR Funding and Awards website for further information. Plain language summary Men with an magnetic resonance imaging scan that shows possible prostate cancer (PCa) are offered prostate biopsies, where samples of the prostate tissue are collected with a needle, to confirm the presence and severity of cancer. Different biopsy methods exist. In a cognitive fusion biopsy, clinicians will target abnormal looking parts of the prostate by looking at the magnetic resonance imaging scan alongside ‘live’ ultrasound images. During a software fusion (SF) biopsy, a computer software is used to overlay the magnetic resonance imaging scan onto the ultrasound image. This study evaluated whether SF is better at detecting cancer compared with cognitive fusion biopsy, and whether it represents value for money for the National Health Service. We did a comprehensive review of the literature. We combined and re-analysed the evidence, and assessed its quality. We investigated whether SF biopsies are sufficient value for money. Compared with cognitive fusion, patients receiving a SF biopsy may have: (1) a lower probability of having a ‘no cancer’ result, (2) similar probability of having a benign, non-clinically significant (CS) cancer result and (3) higher probability of detecting CS cancer. However, it is uncertain to what extent SF is more accurate than cognitive fusion, because of concerns about the quality of the evidence. We found no evidence that any SF devices were superior to others. Using additional, random biopsies alongside software or cognitive fusion would increase the detection of PCa. We also looked for evidence on the value for money of the SF biopsies to detect PCa and found no relevant studies. We weighed the costs and the benefits of SF biopsy compared to cognitive fusion to determine whether it could be a good use of National Health Service money. The poor quality of information makes the value of the technologies largely unknown. Scientific summary Background Prostate cancer (PCa) is the most commonly diagnosed cancer in men in the UK. In the NHS people with suspected PCa are offered multiparametric magnetic resonance imaging (mpMRI). People with suspected PCa, according to MRI, are offered a biopsy procedure to confirm the presence and severity of cancer. Traditionally patients were offered a systematic transrectal, ultrasound-guided prostate biopsy (or systematic biopsy). Since the introduction of mpMRI, specific areas of abnormal tissue can be targeted, by combining (or fusing) the results of mpMRI and ultrasound imaging. Several methods for fusing MRI and ultrasound images exist, including cognitive fusion (CF), in which a region of interest is identified prior to biopsy and the biopsy operator estimates where it might be on an ultrasound image, and software fusion (SF), where regions of interest on magnetic resonace images are identified and contoured before biopsy and overlayed with the prostate contours on ultrasound images during the biopsy. Systematic biopsy may be used in addition to targeted biopsy. A number of SF technologies are available. However, the effectiveness and cost-effectiveness of SF compared with CF is uncertain. Objectives This study aimed to assess the clinical and cost-effectiveness of SF biopsy systems in people with suspected localised and locally advanced PCa. Methods Systematic review A systematic review of the diagnostic accuracy, clinical effectiveness, safety and practical implementation of nine SF systems compared with CF and with each other, in people suspected PCa according to MRI was conducted. Comprehensive bibliographic searches, including MEDLINE and EMBASE and supplementary sources, were conducted up to 2 August 2022 for published and unpublished literature. Studies of people with suspected PCa who have had a MRI scan that indicates a significant lesion [Likert or prostate imaging – reporting and data system (PI-RADS) score of 3 or more], including biopsy-naive and repeat biopsy patients with a previous negative prostate biopsy, and comparing SF with CF or with another SF device, were included. The following SF technologies were included: ARTEMIS (InnoMedicus ARTEMIS), BioJet (Healthcare Supply Solutions Ltd), BiopSee (Medcom), bkFusion (BK Medical UK Ltd and MIM Software Inc.), Fusion Bx 2.0 (Focal Healthcare), FusionVu (Exact Imaging), iSR’obot Mona LisaTM (Biobot iSR’obot), KOELIS Trinity (KOELIS and Kebomed) and UroNav Fusion Biopsy System (Phillips). Previous versions were also eligible. In-bore (or in-gantry) biopsies were excluded. Prospective, randomised and non-randomised comparative studies were included, and retrospective evidence where no prospective evidence could be found for an eligible SF device. To provide sufficient evidence for a network meta-analysis (NMA), within-patient comparisons or randomised controlled trials (RCTs) between SF and systematic biopsy, and between CF and systematic biopsy, were also eligible to inform indirect comparisons of diagnostic accuracy. Two researchers independently screened the titles and abstracts of all reports identified by the bibliographic searches and of all full-text papers subsequently obtained. Data extraction and quality assessment were conducted by at least one researcher and checked by a second. Risk of bias of diagnostic accuracy studies was assessed using quality assessment of diagnostic accuracy studies-comparative (QUADAS-C). For diagnostic accuracy outcomes, studies reporting sufficient data were included in network meta-analyses comparing SF and CF with or without concomitant systematic biopsy, and systematic biopsy alone, where odds of being categorised in each of different cancer grades were allowed to vary by biopsy type. Results were reported as odds ratios with 95% credible intervals (CrIs). Additional diagnostic accuracy results that could not be pooled in a meta-analysis and clinical effectiveness, safety and implementation outcomes were synthesised narratively. Economic analysis Cost-effectiveness evidence comparing SF biopsy systems with CF for targeted prostate biopsy in men with suspected PCa was identified by the previously mentioned searches, with evidence narratively summarised and tabulated. Studies were appraised for their quality, generalisability and appropriateness to inform the decision problem as defined by the National Institute for Health and Care Excellence Diagnostics Assessment Report (NICE DAR) scope. A targeted search was conducted to identify evidence to support the development of a de novo decision model. The searches aimed to identify cost-effectiveness evidence of diagnostic strategies at the point of biopsy to support the model conceptualisation. Evidence was reviewed to (1) identify value components of the biopsy approaches, (2) characterise alternative mechanisms of evidence linkage from disease prevalence, diagnostic accuracy, choice of treatment to final outcomes, and (3) identify any UK-relevant sources of evidence. A de novo decision analytic model was developed to estimate the cost-effectiveness of SF compared to CF. The model evaluated two strategies for two alternative comparisons: (1) targeted SF biopsy versus targeted cognitive biopsy and (2) combined (targeted and systematic) SF biopsy versus combined cognitive biopsy. The four strategies could not be incrementally compared due to the mechanism of evidence generation for the diagnostic accuracy, which relied on separate evidence networks. The de novo model consisted of two components: (1) a decision tree, which captured biopsy adverse events (AEs), repeated biopsies and classified individuals according to their biopsy results and underlying true disease status, and (2) long-term model to link classification to clinical management decisions and this to longer-term costs and consequences (e.g. disease progression and PCa mortality) so that differences in costs, life-year gains and quality-adjusted life-years (QALYs) were quantified over a lifetime horizon. The model required the development of (1) an extension to the evidence synthesis to allow quantifying the extension of test misclassification in the diagnostic model with SF biopsy and CF biopsy, and (2) an inference model to derive unobservable transition probabilities for the long-term model. Results The systematic review of clinical evidence included a total of 3733 patients who received SF and 2154 individuals with CF from 23 studies. Evidence was included for all devices specified in the protocol, except for Fusion Bx 2.0 and FusionVu. Overall, the evidence for all devices was at high risk of bias. Overall, biopsy-naive patients were under-represented. Fourteen studies were included in the meta-analyses. Diagnostic accuracy Across all analyses results must be interpreted with caution due to the high risk of bias in the evidence base and wide uncertainty over the results. The meta-analyses show that patients undergoing SF biopsy may have: (1) a lower probability of being classified as not having cancer, (2) similar probability of being classified as having non-clinically significant cancer [International Society of Urological Pathology (ISUP) grade 1], and (3) higher probability of being classified at higher ISUP grades, particularly ISUP 2. Similar results were obtained where both biopsy methods were combined with systematic biopsy. Additional meta-analyses of cancer detection rates suggest that, compared with CF biopsy, SF may identify more PCa (any grade) (OR 1.30; 95% CrI 1.06, 1.61). Adding systematic biopsy to cognitive or SF may increase the detection of all PCa and of clinically significant (CS) cancer, and from this evidence there is no suggestion that SF with concomitant systematic biopsy is superior to CF with systematic biopsy. Meta-analyses of cancer detection rates, by individual device, showed that compared with CF biopsy, BioJet and Urostation are associated with a higher detection of PCa overall. There was no evidence that any of the SF devices increased detection of CS cancer (except for BioJet, although this is based on one low-quality study), and overall, the evidence was insufficient to conclude whether any individual devices were superior to CF, or whether some SF technologies are more accurate than others. Clinical effectiveness There is no evidence that biopsy positivity rates and safety outcomes differ significantly between SF and CF, or between SF devices. There was some evidence that systems with rigid registration (BioJet or UroNav) are easier and faster to use than elastic registration (KOELIS Trinity), although this is informed by a single, small study and is not conclusive. Cost-effectiveness One full cost-effectiveness study of SF compared targeted SF to targeted CF. However, the findings of the study were not considered generalisable to the decision problem under assessment. Sixteen studies were identified of which nine were selected to inform the conceptualisation and parameterisation of the de novo decision model. The base-case cost-effectiveness analysis suggests for the targeted biopsy and the combined biopsy comparisons, that SF strategy is on average costlier and yields greater QALYs than the CF strategy, resulting in a probabilistic incremental cost-effectiveness ratio (ICER) of £6197 and £2199 per additional QALY for each comparison, respectively. These ICERs are below the lower bound of the cost-effectiveness threshold range recommended by NICE, suggesting that SF may be cost-effective compared to CFs in both the targeted and the combined comparisons. However, these results should be interpreted cautiously given the uncertainties in the relative diagnostic accuracy evidence which informs the model. The probabilistic analysis suggests a higher probability of cost-effectiveness for SF versus CF at the range of cost-effectiveness thresholds recommended by NICE (0.64 and 0.68 at £20,000 and £30,000 per additional QALY for targeted SF biopsy). Discussion This assessment includes a broad, comprehensive literature search for software and CF technologies and has been conducted following recognised guidelines to ensure high quality. The review identified evidence on the diagnostic accuracy of nine SF technologies, and is the first systematic review to formally compare the relative accuracy of SF and CF, with and without systematic biopsy, as well as different SF devices, using both direct and indirect evidence in a NMA. Unlike recent systematic review evidence, our review found that SF increased detection of clinically insignificant cancer compared with CF. Our review has a number of limitations. The evidence included in the systematic review is at high risk of bias overall. There was variation in patient and study characteristics. Biopsy-naive patients, who form the large majority of patients eligible for targeted biopsy, were under-represented, although there was insufficient evidence to evaluate whether the relative accuracy of software and CF differed between biopsy-naive and repeat biopsy patients. There was insufficient evidence to explore the impact of a number of other potential effect modifiers, including lesion location, operator experience, biopsy routes and anaesthesia methods. There were few studies per comparison, not all studies reported outcomes by all cancer grades, and most estimates from the meta-analyses were imprecise, particularly at higher cancer grades where data were most sparse. The network meta-analyses relied on the assumption that CF was equivalent across different centres, which is uncertain. No evidence was found for most of this assessment’s prespecified outcomes: biopsy sample suitability/quality, number of repeat biopsies performed, procedure completion rates, software failure rate, time to diagnosis, length of hospital stay, time taken for MR image preparation, subsequent PCa management, re-biopsy rate, hospitalisation, overall survival, progression-free survival (PFS), patient- and carer- reported outcomes [including tolerability and health-related quality of life (HRQoL)], barriers and facilitators to implementations. The cost-effectiveness results are driven by the modelled differences in diagnostic accuracy between software and CF, particularly the increased correct detection of Cambridge Prognostic Group 1 (CPG 1) (resulting in net losses for SF) and CPG 2 (resulting in net gains for SF). The External Assessment Group (EAG)’s NMA and its extension underpinned the economic model, so its limitations apply to the cost-effectiveness estimates. The magnitude of value realised for SF, compared with CF, depends on the balance between different degrees of misclassification and correct classification with the two technologies and on the prevalence of disease at each cancer grade. The value of SF is thus driven by comparative diagnostic accuracy (compared to ‘gold standard’) derived where evidence is particularly sparse (cancer grades above 2), and by prevalence, which is also affected by evidence sparsity. Therefore, the estimates of cost-effectiveness are affected by unquantified uncertainty and should be interpreted with caution. Conclusions Compared to CF biopsy, patients undergoing SF biopsy may show a lower probability of being classified as not having cancer, similar probability of being classified as having non-CS cancer, and a higher probability of being classified at higher ISUPs, particularly ISUP 2. Both SF and CF biopsy can miss CS cancer lesions, and the addition of standard-systematic biopsy increases the detection of all PCa and CS cancer for both fusion methods. There is insufficient evidence to conclude on the relative accuracy and clinical effectiveness of different software devices. Cost-effectiveness estimates comparing software to CF were generally favourable to SF, except where the technologies were assumed to have the same diagnostic accuracy. The drivers of economic value of SF, comparative diagnostic accuracy and prevalence, are affected by unquantified uncertainty. Judgements on the economic value of SF require integration of the uncertainties over the clinical evidence with the overall cost-effectiveness. Recommendations for further research High-quality, sufficiently powered RCT evidence comparing SF biopsy with CF biopsy is required to address limitations from the existing evidence. Improved reporting of diagnostic accuracy outcomes would enable future syntheses to make use of a larger body of evidence. Study registration This trial is registered as PROSPERO CRD42022329259. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: 135477) and is published in full in Health Technology Assessment; Vol. 28, No. 61. See the NIHR Funding and Awards website for further information.

Ugbold Maidar, Minyoung Ra, Donghee Yoo

Within the evolving field of sentiment analysis, the integration of topic modeling and association rule mining presents a promising yet underexplored method. This approach currently lacks an organized framework for maximizing insights that aid in drawing robust conclusions concerning customer sentiments. Therefore, this study addresses the need and rationale for having comprehensive sentiment analysis systems by integrating topic modeling and association rule mining to analyze online customer reviews of earphones sold on Amazon. It employs Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic), a technique that generates coherent topics by effectively capturing contextual information, and Frequent Pattern Growth (FPGrowth), an efficient association rule mining algorithm used for discovering patterns and relationships in a dataset without candidate generation. This analysis of reviews on ten earphone products identified key customer concerns as follows: sound quality, noise cancellation, durability, and battery life. The results indicate an overall positive sentiment towards sound quality and battery life, mixed reviews on noise cancellation, and significant dissatisfaction with product durability. Using integrated topic modeling and association rule mining offers deeper insights into customer preferences and highlights specific areas for product improvement and guiding targeted marketing strategies. Moreover, we focused on algorithm selection to improve the model’s performance and efficiency, ensuring effective compatibility with our sentiment analysis framework. This study demonstrates how combining advanced data mining techniques and structuring insights from written customer feedback enhances the depth and clarity of sentiment analysis, furthering its applicability in e-commerce research.

WANG Xiangyu, CHEN Wuhui, GUO Xiaolong et al.

With the development and evolution of the information revolution, promoting the integration of a new generation of digital technology with traditional power generation system, and promoting the digital construction of power generation systems is an important way to support energy transformation and digital grid construction. Based on the digital business needs of power generation system, this paper summarized the business needs of data in various scenarios such as the full life cycle management, intelligent operation and maintenance, and intelligent operation. The architecture of power generation system was expounded from the aspects of network structure and digital technology architecture. The key technologies and applications in the process of digitalization of power generation system were sorted out. Finally, the problems that need to be solved in the process of digitalization of power generation system were discussed.

M. D. Magomedov, E. Yu. Alekseycheva

The article considers regional development trends in the context of economic activity and price processes, shows data on material well-being of families in Russian regions, and presents the ratings of the Russian regions by dynamics of wages and family welfare. The dynamics of sectoral production (agro-industrial complex, logging and wood processing, extractive industry, oil and oil products transshipment, chemical industry, motor transport, marine transportation, furniture production, construction materials, housing construction, machine building, shipbuilding, tourism, and education) in the Russian regions has been shown, and diffrentiation of regions by industries development revealed. The dynamics of consumer demand has been presented and main factors affcting consumer activity from the cost of living crisis and escapism manifested in diffrent forms revealed. A signifiant trend is also a decrease in concentrating attention. Main trends in online commerce development in Russia have been shown as it has been growing in 2022 even against the background of general decline in consumer demand. These trends include orientation of most players to sustainable development and ethics: increasing market share and capitalization, as well as reducing concentration of activities on profi. Global and local factors of consumer goods sales development in the Internet have been identifid.

Sheila R. Ngwaku, Janine Pascoe, Wiehan A. Pelser et al.

Several methodologies have been developed to manage diesel in open-cast mining due to its high demand and increasing diesel prices. Although the use of diesel-powered equipment in underground mines has increased over the years, effective management thereof has not received the same attention. With the advent of Industry 4.0, data can be utilised more effectively by modern businesses to identify and solve problems in a structured manner. In this study, an underground mine was used as a case study to determine whether a Data, Information, Knowledge, Wisdom (DIKW) method for diesel management could be coupled with the Six Sigma Define, Measure, Analyse, Improve, Control (DMAIC) tool to make more informed decisions and gain new insights to help reduce diesel wastage underground. The new integrated methodology identified diesel spillages and highlighted the biggest contributors to these underground spillages. The Six Sigma DMAIC domain utilised root cause analysis to determine the reason for recent systems failures, followed by the identification of practical solutions to eliminate up to 200 ML (megalitres) of diesel spillage. With this information, the case study mine stands to save over USD 175,000 per annum.

Kusuma Ayu Laksitowening, Made Diva Prasetya, Dawam Dwi Jatmiko Suwawi et al.

Students can have various characteristics and learning patterns. By understanding the characteristics and learning pattern of individual students, teachers can provide individualized learning strategies based on students' needs. Students' learning patterns may experience changes depending on their conditions during the learning process. If the learning pattern analysis is only run once, then the progress and changes in student learning patterns throughout the learning process cannot be recognized. On the other hand, periodical analysis is expected to describe the dynamics of student learning patterns from time to time. This research is intended for capturing students' dynamic learning pattern using Hierarchical Clustering. We clustered the learning patterns based on Learning Management Systems (LMS) activity logs. The activity log data were partitioned into several periodical datasets. The results of the periodic clustering indicated that students’ learning patterns varied from one another and changed from time to time. Most students experienced change in learning patterns throughout the semester. The analysis also indicated that learning pattern also has the potential to be improved and maintained.

Safia Meftah, Elhabib Hadjadj, Mohamad Biomy et al.

In this work, by using the iterative method, we discuss the existence and uniqueness of solutions for multiterm fractional boundary value problems. Next, we examine some existence and uniqueness returns for semilinear fractional differential inclusions and equations for multiterm problems by using some notions and properties on set-valued maps and give some examples to explain our main results. We explore and use in this paper the fundamental properties of set-valued maps, which are needed for the study of differential inclusions. It began only in the mid-1900s, when mathematicians realized that their uses go far beyond a mere generalization of single-valued maps.

Hamid Ravaghi, Merette Khalil, Jehan Al-Badri et al.

BackgroundCOVID-19 highlighted the critical role that hospitals play throughout the prolonged response and continuous recovery stages of the pandemic. Yet, there is limited evidence related to hospitals in the recovery stage, particularly capturing the perspectives of hospital managers and frontliners in resource-restrained and humanitarian settings.ObjectiveThis paper aims to capture the perspectives of hospital managers and frontliners across the Eastern Mediterranean Region on (1) the role of hospitals in recovering from COVID-19, (2) Hospitals' expectations from public health institutions to enable recovery from COVID-19, (3) the Evaluation of hospital resilience before and through COVID-19, and (4) lessons to strengthen hospital resilience throughout the COVID-19 recovery.MethodsA multi-methods approach, triangulating a scoping review with qualitative findings from 64 semi-structured key-informant interviews and survey responses (n = 252), was used to gain a deeper context-specific understanding. Purposeful sampling with maximum diversity supported by snowballing was used and continued until reaching data saturation. Thematic analysis was conducted using MAXQDA and simple descriptive analysis using Microsoft Excel.FindingsIn recovering from COVID-19, hospital managers noted hospitals' role in health education, risk reduction, and services continuity and expected human resource management, financial and material resource mobilization, better leadership and coordination, and technical support through the provision of updated clinical evidence-based information from their public health institutions. Qualitative findings also indicated that hospital managers attributed considerable changes in hospitals' resilience capacities to the pandemic and suggested that strengthening hospitals' resilience required resilient staff, sustainable finance, and adaptive leadership and management.ConclusionHospitals are the backbone of health systems and a main point of contact for communities during emergencies; strengthening their resilience throughout the various stages of recovery is critical. Hospitals cannot be resilient in silos but rather require an integrated-whole-of-society-approach, inclusive of communities and other health systems actors.

Toni Taipalus

Vector database management systems have emerged as an important component in modern data management, driven by the growing importance for the need to computationally describe rich data such as texts, images and video in various domains such as recommender systems, similarity search, and chatbots. These data descriptions are captured as numerical vectors that are computationally inexpensive to store and compare. However, the unique characteristics of vectorized data, including high dimensionality and sparsity, demand specialized solutions for efficient storage, retrieval, and processing. This narrative literature review provides an accessible introduction to the fundamental concepts, use-cases, and current challenges associated with vector database management systems, offering an overview for researchers and practitioners seeking to facilitate effective vector data management.

Hyunki Seong, Chanyoung Chung, David Hyunchul Shim

In this letter, we propose a model parameter identification method via a hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient explore-exploit strategy to identify the parameters of dynamic models in a data-driven optimization manner. We utilize our method for model parameter identification of the AV-21, a full-scaled autonomous race vehicle. We then incorporate the optimized parameters for the design of model-based planning and control systems of our platform. In experiments, MI-HPO exhibits more than 13 times faster convergence than traditional parameter identification methods. Furthermore, the parametric models learned via MI-HPO demonstrate good fitness to the given datasets and show generalization ability in unseen dynamic scenarios. We further conduct extensive field tests to validate our model-based system, demonstrating stable obstacle avoidance and high-speed driving up to 217 km/h at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The source code for our work and videos of the tests are available at https://github.com/hynkis/MI-HPO.

Noman Bashir, Yasra Chandio, David Irwin et al.

Environmentally-powered computer systems operate on renewable energy harvested from their environment, such as solar or wind, and stored in batteries. While harvesting environmental energy has long been necessary for small-scale embedded systems without access to external power sources, it is also increasingly important in designing sustainable larger-scale systems for edge applications. For sustained operations, such systems must consider not only the electrical energy but also the thermal energy available in the environment in their design and operation. Unfortunately, prior work generally ignores the impact of thermal effects, and instead implicitly assumes ideal temperatures. To address the problem, we develop a thermodynamic model that captures the interplay of electrical and thermal energy in environmentally-powered computer systems. The model captures the effect of environmental conditions, the system's physical properties, and workload scheduling on performance. In evaluating our model, we distill the thermal effects that impact these systems using a small-scale prototype and a programmable incubator. We then leverage our model to show how considering these thermal effects in designing and operating environmentally-powered computer systems of varying scales can improve their energy-efficiency, performance, and availability.

Zhenglong Li, Hejun Huang, Vincent Tam

Reinforcement learning (RL) based investment strategies have been widely adopted in portfolio management (PM) in recent years. Nevertheless, most RL-based approaches may often emphasize on pursuing returns while ignoring the risks of the underlying trading strategies that may potentially lead to great losses especially under high market volatility. Therefore, a risk-manageable PM investment framework integrating both RL and barrier functions (BF) is proposed to carefully balance the needs for high returns and acceptable risk exposure in PM applications. Up to our understanding, this work represents the first attempt to combine BF and RL for financial applications. While the involved RL approach may aggressively search for more profitable trading strategies, the BF-based risk controller will continuously monitor the market states to dynamically adjust the investment portfolio as a controllable measure for avoiding potential losses particularly in downtrend markets. Additionally, two adaptive mechanisms are provided to dynamically adjust the impact of risk controllers such that the proposed framework can be flexibly adapted to uptrend and downtrend markets. The empirical results of our proposed framework clearly reveal such advantages against most well-known RL-based approaches on real-world data sets. More importantly, our proposed framework shed lights on many possible directions for future investigation.

Yifeng Chu, Maxim Raginsky

This paper presents a general methodology for deriving information-theoretic generalization bounds for learning algorithms. The main technical tool is a probabilistic decorrelation lemma based on a change of measure and a relaxation of Young's inequality in $L_{ψ_p}$ Orlicz spaces. Using the decorrelation lemma in combination with other techniques, such as symmetrization, couplings, and chaining in the space of probability measures, we obtain new upper bounds on the generalization error, both in expectation and in high probability, and recover as special cases many of the existing generalization bounds, including the ones based on mutual information, conditional mutual information, stochastic chaining, and PAC-Bayes inequalities. In addition, the Fernique-Talagrand upper bound on the expected supremum of a subgaussian process emerges as a special case.

A. Lederer, V. Sethi

S. Hamilton, N. L. Chervany