Navid Mahdavi, Animesh Dutta, Syeda Humaira Tasnim
et al.
The integration of solar greenhouses into smart energy systems (SESs) remains largely unexplored, despite their potential to enhance energy sharing and hydrogen production. This review investigates the role of solar greenhouses as active energy contributors within SESs, emphasizing their biomass waste gasification for hydrogen production and their integration into district heating and cooling (DHC) networks. A structured classification of machine learning (ML) and deep learning (DL) techniques applied in forecasting and optimizing these processes is provided. Additionally, the evolution of DHC systems is analyzed, with a focus on fifth-generation DHC (5GDHC) networks, which facilitate bidirectional energy exchange at near-ambient temperatures. The review highlights that existing studies have predominantly addressed SES advancements and ML-driven energy management without considering the contributions of solar greenhouses. A novel framework is proposed, illustrating their role as prosumers capable of exchanging electricity, hydrogen, and thermal energy within SESs. Key findings reveal that integrating solar greenhouses with SESs can enhance energy efficiency, reduce carbon emissions, and improve system resilience. Furthermore, ML-driven predictive control strategies, particularly model predictive control (MPC), are identified as essential for optimizing real-time energy flows and biomass gasification processes. This study provides a foundation for future research on the technical, economic, and environmental feasibility of integrating greenhouses into SESs. The insights presented offer a pathway toward more sustainable, AI-driven energy-sharing networks, supporting policymakers and industry stakeholders in the transition toward low-carbon energy solutions.
Marek Makowski, Janusz Granat, Andrii Shekhovtsov
et al.
pyMCMA is the Python implementation of a novel method for autonomous computation of the Pareto-front representation composed of efficient solutions distributed uniformly in terms of the distances between neighbor Pareto solutions. pyMCMA supports scientific, i.e., objective, model analysis by providing preference-free Pareto front representation.The update provides new functionalities and enhancements. The former include clustering of the Pareto-front solutions. The enhancements include internal software improvements, optional customization of some parameters, as well as a new functionalities that might be used by advanced users.
İsmet Kaan Çekiş, Buğra Ayrancı, Fezayim Numan Salman
et al.
IP spoofing is a critical component in many cyberattacks, enabling attackers to evade detection and conceal their identities. This study rigorously compares eight deep learning models—LSTM, GRU, CNN, MLP, DNN, RNN, ResNet1D, and xLSTM—for their efficacy in detecting IP spoofing attacks. Overfitting was mitigated through techniques such as dropout, early stopping, and normalization. Models were trained using binary cross-entropy loss and the Adam optimizer. Performance was assessed via accuracy, precision, recall, F1 score, and inference time, with each model executed a total of 15 times to account for stochastic variability. Results indicate a powerful performance across all models, with LSTM and GRU demonstrating superior detection efficacy. After ONNX conversion, the MLP and DNN models retained their performance while achieving significant reductions in inference time, miniaturized model sizes, and platform independence. These advancements facilitated the effective utilization of the developed systems in real-time network security applications. The comprehensive performance metrics presented are crucial for selecting optimal IP spoofing detection strategies tailored to diverse application requirements, serving as a valuable reference for network anomaly monitoring and targeted attack detection.
We show how to realize a general quantum circuit involving gates between arbitrary pairs of qubits by means of geometrically local quantum operations and efficient classical computation. We prove that circuit-level local stochastic noise modeling an imperfect implementation of our derived schemes is equivalent to local stochastic noise in the original circuit. Our constructions incur a constant-factor increase in the quantum circuit depth and a polynomial overhead in the number of qubits. To execute an arbitrary quantum circuit on n qubits, we give a three-dimensional quantum fault-tolerance architecture involving O(n^{3/2}log^{3}n) qubits and a quasi-two-dimensional architecture using O(n^{2}log^{3}n) qubits. Applied to recent fault-tolerance constructions, this gives a fault-tolerance-threshold theorem for universal quantum computations with local operations, a polynomial qubit overhead, and a quasipolylogarithmic depth overhead. More generally, our transformation dispenses with the need for considering the locality of operations when designing schemes for fault-tolerant quantum information processing.
Faheed A.F. Alrslani, Manal Abdullah Alohali, Mohammed Aljebreen
et al.
Abstract Cyberattacks have given rise to several phenomena and have raised concerns among users and power system operators. When they are built to bypass state estimation bad data recognition methods executed in the conventional grid system control room, False Data Injection Attacks (FDIA) pose a significant security threat to the operation of power systems. Therefore, real-time monitoring becomes inevitable with the quick implementation of renewables within the grid operator. The state estimation algorithm plays a major role in defining the grid’s operating scenarios. FDIA creates a significant risk to these estimation strategies by adding malicious information to the measurement obtained. Real-time recognition of these attack classes improves grid resiliency and ensures a secure grid operation. This study introduces a novel Attribute Reduction with a Deep Learning-based False Data Injection Attack Recognition (ARDL-FDIAR) technique. The primary goal of the ARDL-FDIAR technique is to improve security via the FDIA detection process. The ARDL-FDIAR technique uses Z-score normalization to scale the input data. The attribute reduction process gets invoked using the modified Lemrus optimization algorithm (MLOA) to choose optimal feature sets. Moreover, the FDIA detection process is performed by modelling an improved deep belief network (IDBN) model. Furthermore, the performance of the IDBN model is improved by the Cetacean Optimization Algorithm (COA)-based hyperparameter tuning process. A series of experiments were performed to ensure the enhancement of the ARDL-FDIAR technique. The results indicated the enhanced security performance of the ARDL-FDIAR technique compared to other DL approaches.
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.
Objective: Clinical research has shown that local injections for carpal tunnel syndrome reduce the symptoms of patients and enhance their quality of life considerably. However, there are several therapy options, and the optimal choice of regimen remains uncertain. Therefore, we comprehensively evaluated the variations in clinical efficacy and safety of several medications for treating carpal tunnel syndrome.Methods: Computer searches of Embase, PubMed, Cochrane Library, and Web of Science databases were used to collect articles of randomized controlled trials on local injections for treating carpal tunnel syndrome from database creation till 10 June 2023. Two researchers independently screened the literature, extracted information, evaluated the risk of bias in the included studies, and performed network Meta-analysis using Stata 17.0 software. Drug efficacy was assessed using symptom severity/function and pain intensity. Surface under the cumulative ranking curve (SUCRA) ranking was used to determine the advantage of each therapy.Results: We included 26 randomized controlled trials with 1896 wrists involving 12 interventions, such as local injections of corticosteroids, platelet-rich plasma, 5% dextrose, progesterone, and hyaluronidase. The results of the network meta-analysis showed the following: (i) symptom severity: at the 3-month follow-up, D5W combined with splinting (SUCRA = 95%) ranked first, and hyaluronidase (SUCRA = 89.6%) at 6 months; (ii) functional severity: either at the 3-month follow-up (SUCRA = 89.5%) or 6 months (SUCRA = 83.6%), iii) pain intensity: 5% dextrose in water combined with splinting was the most effective at the 3-month (SUCRA = 85%) and 6-month (SUCRA = 87.6%) follow-up.Conclusion: Considering the combination of symptoms/function and pain intensity, combining 5% dextrose in water with splinting is probably the treatment of choice for patients with carpal tunnel syndrome. It is more effective than glucocorticoids and no adverse effects have been observed.Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022370525.
Мета статті – дослідити роль цифрових навичок у сучасних бізнес-моделях та їх вплив на управління компанією.
Методи дослідження. Методологію становлять принципи наукового дослідження. Використано загальнонаукові методи пізнання, проаналізовано глобальні практики оцінки цифрових навичок на основі використання офіційних документів і методологій, вивчено кейси цифрової трансформації та використання цифрових навичок у бізнес-моделях глобальних компаній.
Наукова новизна полягає у сформульованих перевагах цифрових навичок, які мають визначальний вплив на трансформацію бізнес-моделей у цифровому середовищі, а також на структуру та тенденції ринку праці, визначаючи базові компетенції робочої сили та формуючи спектр вимог роботодавців. Цифрова трансформація не тільки змінила світ праці, створивши нові робочі ролі та змінивши характер праці як такої, а й розвинула готовність компаній до протистояння сучасним глобальним і регіональним викликам. Найкращі практики оцінювання цифрових навичок охоплюють використання комплексної системи цифрових компетенцій, проведення регулярних оцінок навичок співробітників і надання цільових програм навчання та розвитку для усунення прогалин у навичках.
Висновки. Сформульовано, що управління цифровими навичками є постійним процесом, який передбачає визначення навичок і компетенцій, необхідних для кожної ролі в організації, оцінку поточних навичок і надання можливостей навчання та розвитку. Швидкий технологічний прогрес, потреба перенавчання та підвищення кваліфікації, проблема пошуку талантів, цифрова безпека, організаційний опір змінам, відсутність стандартизації, складність інтеграції з наявними процесами тощо становлять виклики для сучасних компаній. Вивчено кейси компанії «Amazon», «Walmart», «General Electrics», «IBM». Вони змогли використати цифрові навички для збору й аналізу даних, покращення співпраці та спілкування команд, а також оптимізації своїх операцій, що призвело до таких переваг, як підвищення ефективності, рівня задоволеності клієнтів, зниження витрат.
Bibliography. Library science. Information resources, Computer software
As the technology is emerging rapidly, the health industry has shifted its standard towards providing the best and minimally invasive, novel treatment options to the patients to choose from three-dimensional (3D) printing technology. After its introduction in medicine and health care, 3D printing technologies are advanced manufacturing technologies based on computer-aided design (CAD) digital objects to create customized 3D objects automatically with the help of software. 3D printing technology is valuable to clinicians as well as patients as it is time-saving and helps the clinician to view the precise anatomy and fabricate patient-specific models, surgical guides, stents, prostheses, and drug delivery systems. Because of its advantages, it is widely used in various branches of dentistry and its application in Pediatric dentistry has also taken a broad path. 3D printing will play a larger role in dentistry in the future. The integration of scanning, visualization, CAD, milling, and 3D printing technology, together with the profession’s intrinsic curiosity and innovation, makes this an exciting time to be in dentistry. Hence, the aim of this review article is to provide knowledge and awareness about 3D printing and its application in pediatric dentistry.
This paper presents a new IndRNN-based deep attention model, termed DA-IndRNN, for skeleton-based action recognition to effectively model the fact that different joints are usually of different degrees of importance to different action categories. The model consists of (a) a deep IndRNN as the main classification network to overcome the limitation of a shallow RNN network in order to obtain deeper and longer features, and (b) a deep attention network with multiple fully connected layers to estimate reliable attention weights. To train the DA-IndRNN, a new triplet loss function is proposed to guide the learning of the attention among different action categories. Specifically, this triplet loss enforces intra-class attention distances to be smaller than inter-class attention distances and at the same time to allow multiple attention weight patterns to exist for the same class. The proposed DA-IndRNN can be trained end-to-end. Experiments on the widely used datasets, including the NTU RGB + D dataset and UOW Large-Scale Combined (LSC) Dataset, have demonstrated that the proposed method can achieve better and stable performance than the state-of-the-art attention models.
WANG Lijuan, ZHANG Lin, YIN Ming, HAO Zhifeng, CAI Ruichu, WEN Wen
The consistency of multiview data is important for multiview clustering.To achieve multiview data with better consistency, this paper proposes a new multiview clustering algorithm, OMTSC.The OMTSC algorithm simultaneously learns the cluster assignment matrix and feature embedding of each view.Each cluster assignment matrix can be decomposed into shared orthogonal basis-cluster coding matrices.An orthogonal basis matrix can capture and store consistent multiview data and form latent cluster centers.A weighted multiview cluster coding matrix can balance the quality differences of different views effectively.Meanwhile, bipartite graph co-clustering is introduced to realize knowledge transfer, which involves clustering coding, feature embedding, and the orthogonal basis.This improves the multiview data consistency and diversity learning, as well as allows the OMTSC algorithm to leverage the diversity of feature embedding for maximizing multiview consistency and learning the optimal latent cluster centers, thus further improving the performance of multiview clustering.In addition, feature embedding based on group sparse constraints is robust to noise in view data.Experimental results on WikipediaArticles, COIL20, and ORL datasets show that the OMTSC algorithm is superior to SC-Best, Co-Reg, and advanced multiview clustering algorithms, and that it yields the highest score in all three evaluation indexes, i.e., the ACC, NMI, and ARI on COIL20 and ORL datasets, the NMI evaluation index for the OMTSC algorithm exceeds 0.9.
Pasquale Piombino, Vincenzo Abbate, Lorenzo Sani
et al.
Over 70% of patients suffering from dentofacial deformities mention esthetics as the biggest issue pushing them to look for orthodontic and orthognathic treatment. At present, several pieces of software for computer-aided surgery have been released on the market. This surgical planning software allows surgeons to manipulate digital representations of hard and soft tissue profile tracings and subsequently morph the pretreatment image to produce a treatment simulation. The aims of this study were to investigate and find the difference between two of the most used pieces of digital software in pre-surgical planning for patients affected by dentofacial deformities by using the following parameters: usability, validity, timing, accessibility, efficacy, and predictability of the pre-surgical planning. Analyzing the results obtained from our study, it is correct to define both software tools useful and valid in digital surgical planning for the treatment of patients with dentofacial deformities. Each software has negligible differences in performance that do not in any way affect the success of surgical planning. The IPS software represents a valid alternative to the most popular and tested Dolphin Imaging software, and we are even inclined to evaluate it as better in terms of accuracy, effectiveness, and reliability.
As one of the core technologies of electric vehicles (EVs), the state of charge (<i>SOC</i>) estimation algorithm of lithium-ion batteries is directly related to the performance of the battery management system (BMS). Before EVs are put into the market, the <i>SOC</i> estimation algorithm must be tested and verified to ensure the reliability of the BMS and the safe operation of EVs. Therefore, this paper establishes a lithium-ion batteries’ <i>SOC</i> estimation algorithm verification platform for the comprehensive performance evaluation and verification of the new <i>SOC</i> estimation algorithm. In addition, there are two schemes, including real-time <i>SOC</i> estimation verification and offline <i>SOC</i> estimation verification can be selected, which improve the reliability and efficiency of verification. Firstly, the design idea of the verification platform (the research and development purpose, functional requirements, and the overall design scheme) is introduced in detail. Secondly, the modular design idea is used to design the hardware structure of the verification platform, which mainly includes the BMS host module, BMS slave module, battery charger module, and electronic load module. Finally, the software system, including the communication architecture, the <i>SOC</i> reference standard and evaluation indexes of the algorithm, and the upper computer function and implementation is designed to realize the functions of the verification platform.
Volodymyr Sokol, Vitalii Krykun, Mariia Bilova
et al.
The demand for the creation of information systems that simplifies and accelerates work has greatly increased in the context of the rapid
informatization of society and all its branches. It provokes the emergence of more and more companies involved in the development of software
products and information systems in general. In order to ensure the systematization, processing and use of this knowledge, knowledge management
systems are used. One of the main tasks of IT companies is continuous training of personnel. This requires export of the content from the company's
knowledge management system to the learning management system. The main goal of the research is to choose an algorithm that allows solving the
problem of marking up the text of articles close to those used in knowledge management systems of IT companies. To achieve this goal, it is necessary
to compare various topic segmentation methods on a dataset with a computer science texts. Inspec is one such dataset used for keyword extraction and
in this research it has been adapted to the structure of the datasets used for the topic segmentation problem. The TextTiling and TextSeg methods were
used for comparison on some well-known data science metrics and specific metrics that relate to the topic segmentation problem. A new generalized
metric was also introduced to compare the results for the topic segmentation problem. All software implementations of the algorithms were written in
Python programming language and represent a set of interrelated functions. Results were obtained showing the advantages of the Text Seg method in
comparison with TextTiling when compared using classical data science metrics and special metrics developed for the topic segmentation task. From
all the metrics, including the introduced one it can be concluded that the TextSeg algorithm performs better than the TextTiling algorithm on the
adapted Inspec test data set.
Anna Rutkowska, Sebastian Rutkowski, Joanna Szczepańska-Gieracha
The popularity of immersion, understood as absolute engrossment in a virtual world, has been growing year by year, due to new hi-tech sound, image and data-processing technologies. Man, because of human nature, is attracted to immersion as a way of experiencing new environments, which are often very different from those offered by the real world. Thanks to immersion in a virtual world, one can step into any desired computer-generated reality. This technology has found its use in the process of motor rehabilitation, likewise, psychological therapy. Total immersion in a virtual world creates the possibility for guided rehabilitation, utilising the appeal of am imaginary environment. Patients become more engaged and motivated to take part in the laborious and painstakingly long process leading to the recovery of their motor functions. Cooperation between physiotherapists and psychologists with engineers has resulted in the creation of new software solutions, and improved equipment, which can be tailored to meet the needs of patients with various mental problems or physical disfunctions and disabilities.