Miracle-dz/UniFAR-FTUs huggingface dataset:
Innovation strategy in retail: The unstaffed digital supermarkets at LIFVS

Digitalization captures most aspects of human life. New trends in digitalization have a great impact on the retail chain stores; hence, the implementation of artificial intelligence (AI) has somewhat become necessary to cope up with the changing trend in this sector. This case study analyses the use of artificial intelligence in an unstaffed supermarket called LIFVS located at the rural areas of Sweden. As the number of supermarkets was decreasing in the rural areas over the years due to high maintenance costs, many have opted out of business. In response to the situation, Co-founder Daniel Ludh (hereafter Daniel) implemented a sustainable business model using AI, which serves the purpose of supermarket chains meeting the demands of the consumers in the rural areas while keeping the costs truncated. The case also discusses the various challenges an unstaffed supermarket face and highlights the success of LIFVS, paving the path for the future retailers of the industry.
Dynamics of a Three-Patch Prey-Predator System with the Impact of Dispersal Speed Incorporating Strong Allee Effect on Double Prey

This paper presents the dispersal effect on a prey-predator model with three-patch states incorporating a strong Allee effect on the first two prey populations. The prey species are considered to be mutable to exhibit a balanced dispersal between the patches. The dispersal among patches is directed through lower fit patches to higher fit patches. This paper derives a new approach for dispersal and Allee’s effect with the specific condition on the stability on a three-patch of a three-species prey-predator anticipated system. The persistence of the system is observed because of the dispersal effect on the three-patch prey-predator system. The numerical simulations of analytical findings are presented using hypothetical parameter values to relate real-world prey-predator situations for balancing ecology.
Integration of Ukraine to the Global Value Chains

The article considers the theoretical and methodological approaches to global value chains when measuring international trade. Global trends in the modern development of international trade are analysed, and the main challenges of international trade policy for Ukraine are characterised. It is established that the modern structure of the Ukrainian economy was formed under the influence of external factors. The authors propose that, over time, the influence of the global economy on the dynamics and structure of the Ukrainian economy will continually intensify. The prospects of Ukraine’s integration into global value chains are examined, and the authors found that the structure of Ukrainian exports of goods coincides with the structure of world exports only partially. Ukrainian export of goods is characterised by a low share of highly processed industrial products and a high share of low value‑added products, in particular, basic metals and agriculture and food industry products. The export of domestic high‑tech goods is constantly decreasing compared to developed countries, and its share in the world export of high‑tech goods is insignificant. To ensure the acceleration of GDP growth in Ukraine, it is important not only to increase exports, but also to increase the export of high‑tech goods.
Bilingual Processing Mechanisms of Scientific Metaphors and Conventional Metaphors: Evidence via a Contrastive Event-Related Potentials Study

To study the different mechanisms of understanding figurative language in a speaker’s native language (L1) and their second language (L2), this study investigated how scientific metaphors in Chinese (L1) and English (L2) are electrophysiologically processed via event-related potential experimentation. Compared with the metaphors from daily life or in literary works, scientific metaphors tend to involve both a more complicated context structure and a distinct knowledge-inferencing process. During the N400 time window (300–500 ms), English scientific metaphors elicited more negative N400s than Chinese ones at the parietal region. In the late positive component (LPC) time window (550–800 ms), English scientific metaphors elicited less positive LPCs than Chinese ones at the parietal region, and larger late negativities encompassing smaller areas of the brain. The findings might indicate that for late unbalanced bilingual speakers, L2 scientific metaphor comprehension requires more effort in information retrieval or access to the non-literal route. Altogether, the possible findings are that non-native and non-dominant language processing involves decreased automaticity of cognitive mechanisms, and decreased sensitivity to the levels of conventionality of metaphoric meanings.
Effects of dental implant corrosion- A Review

Implanted prosthetic devices have become an integral modality in restorative dentistry. The cardinal prerequisite of a biomaterial is that the material and the tissue domain of the physique should coexist without having any unwanted effect on each other. Biological risk associated with ionic particles released from metallic implants are critical and very much sought after. Corrosion of dental implant is a pertinent issue as it is intended to function in human body for a life time. This article gives a systematic review on dental implant
Relationship between body weight and elevation in Leadbeater’s possum (Gymnobelideus leadbeateri)

ABSTRACT The body size of mammals is influenced by several evolutionary, morphological, physiological and ecological factors. Studies of body size can provide insight into the processes underlying observed variation in patterns of mammal morphology. We sought to determine if body weight in Leadbeater’s possum (Gymnobelideus leadbeateri) is related to environmental variables and/or sex. Using linear regression modelling, we quantified the influence on body weight of broadscale geographic variables such as latitude and elevation, site-level indicators of forest productivity (forest type, slope, aspect and topographic wetness) and an individual-level variable (sex). We found that body weight was significantly associated with elevation and sex, with individuals being heavier at higher elevations and males (on average) being heavier than females. Monitoring body weight changes over time within particular forest types will be valuable, given the variations in temperature and resource productivity throughout the range of Leadbeater’s possum that are likely to arise from climate change.
CoToRu: Automatic Generation of Network Intrusion Detection Rules from Code

Programmable Logic Controllers (PLCs) are the brains of Industrial Control Systems (ICSes), and thus, are often targeted by attackers. While many intrusion detection systems (IDSes) have been adapted to monitor ICS, they cannot detect malicious network packets from a compromised PLC that con-form to the network protocol. A domain expert needs to manually construct IDS rules to model a PLC’s behavior. That approach is time-consuming and error-prone. Alternatively, machine learning can infer a PLC’s behavior model from network traces, but that model may be inaccurate due to a lack of high-quality training data. This paper presents CoToRu - a toolchain that takes in the PLC’s code to automatically generate a comprehensive set of IDS rules. CoToRu comprises (1) an analyzer that parses PLC code to build a state transition table for modeling the PLC’s behavior, and (2) a generator that instantiates IDS rules for detecting deviations in PLC behavior. The generated rules can be imported into Zeek IDS to detect various attacks. We apply CoToRu to a power grid testbed and show that our generated rules provide superior performance compared to existing IDSes, including those based on statistical analysis, invariant-checking, and machine learning. Our prototype with CoToRu’s generated rules provide sub-millisecond detection latency, even for complex PLC logic.
Identification of a Novel Ceftazidime-Avibactam-Resistant KPC-2 Variant, KPC-123, in Citrobacter koseri Following Ceftazidime-Avibactam Treatment

This study reported the identification of a novel ceftazidime-avibactam-resistant KPC-2 variant, KPC-123, in a Citrobacter koseri isolated from a patient in a Chinese hospital following ceftazidime-avibactam treatment of infection caused by OXA-232-producing Klebsiella pneumoniae. This novel KPC-123 consisting of 302 amino acids differs from KPC-2 by two insertions after positions 179 (ins179_TY) and 270 (ins270_DDKHSEA), respectively. Conjugation and cloning experiments confirmed that KPC-123 was able to confer high-level resistance to ceftazidime and ceftazidime/avibactam (MICs of 128 mg/L and 64/4 mg/L, respectively) and elevated MIC values of cefotaxime, cefepime, and aztreonam (4 mg/L, 2 mg/L, and 4 mg/L, respectively) but retained susceptibility to carbapenems. Whole-genome sequencing and genomic analysis revealed that blaKPC−123 within the “ISKpn27-blaKPC-ISKpn6” structure was located on a 93,814-bp conjugative plasmid that was almost identical to a blaKPC−2-carrying plasmid harbored in a K. pneumoniae isolate from the same sampling site of the patient, suggesting the transfer and in vivo evolution of this blaKPC-carrying plasmid. Hence, active surveillance of ceftazidime/avibactam resistance and the underlying mechanisms, which may facilitate the prevention and control of the dissemination of resistance, is needed.
Knowledge-sharing Strategies of University-Industry Alliances Promoting Green Technology Innovation in Ecosystems: Based on the Utility of Multichannel Funding

Green technology innovation (GTI) requires a large capital investment, while the role of these capital investments in promoting GTI needs to be further confirmed. To improve GTI, university-industry alliances (U-Is) in green innovation ecosystems engage in knowledge-sharing behaviors and form different knowledge-sharing strategies based on changes in cooperation modes. Employing a differential game, this study explores the utility of multichannel funding for innovation revenues in different cooperative modes of U-Is and the impact on revenue distributions. This article considers three game models in five cases: Nash noncooperative game with no multichannel funding, Nash noncooperative game with external funding but no government subsidies, Nash noncooperative game with multichannel funding, Stackelberg game, and cooperative game. Solving the game model and applying the numerical analysis results in certain interesting conclusions. Our research finds that, first, in the cooperative game, the strongest willingness to share knowledge occurs in the university-industry alliance, in which the total revenues of both parties reach the Pareto optimum. Second, multichannel funding can serve as an incentive mechanism for enterprises and universities to improve the knowledge-sharing willingness, the GTI level, and the revenues of the two players, while the utility of the multichannel funding is strongest in the cooperative game. In addition, in the Stackelberg game, enterprises share subsidies with universities, which stimulates their willingness to share knowledge, and both parties’ revenues are better than they are in the three cases of noncooperation. Eventually, the revenue-sharing ratio of the enterprise has a smaller threshold, and the university can share more benefits relative to the absence of the multichannel funding, which helps balance the U-I in the green innovation ecosystem. These conclusions make a substantial contribution to the selection of cooperation modes and the formulation of revenues distribution contracts in university-industry alliances.
Perceived responsibility for mechanical ventilation and weaning decisions in intensive care units in the Kingdom of Saudi Arabia

Background Optimizing patient outcomes and reducing complications require constant monitoring and effective collaboration among critical care professionals. The aim of the present study was to describe the perceptions of physician directors, respiratory therapist managers and nurse managers regarding the key roles, responsibilities and clinical decision-making related to mechanical ventilation and weaning in adult Intensive Care Units (ICUs) in the Kingdom of Saudi Arabia (KSA). Methods A multi-centre, cross-sectional self-administered survey was sent to physician directors, respiratory therapist managers and nurse managers of 39 adult ICUs at governmental tertiary referral hospitals in 13 administrative regions of the KSA. The participants were advised to discuss the survey with the frontline bedside staff to gather feedback from the physicians, respiratory therapists and nurses themselves on key mechanical ventilation and weaning decisions in their units. We performed T-test and non-parametric Mann-Whitney U tests to test the physicians, respiratory therapists, and nurses’ autonomy and influence scores, collaborative or single decisions among the professionals. Moreover, logistic regressions were performed to examine organizational variables associated with collaborative decision-making. Results The response rate was 67% (14/21) from physician directors, 84% (22/26) from respiratory therapist managers and 37% (11/30) from nurse managers. Physician directors and respiratory therapist managers agreed to collaborate significantly in most of the key decisions with limited nurses’ involvement (P<0.01). We also found that physician directors were perceived to have greater autonomy and influence in ventilation and waning decision-making with a mean of 8.29 (SD±1.49), and 8.50 (SD±1.40), respectively. Conclusion The key decision-making was implemented mainly by physicians and respiratory therapists in collaboration. Nurses had limited involvement. Physician directors perceived higher autonomy and influence in ventilatory and weaning decision-making than respiratory therapist managers and nurse managers. A critical care unit’s capacity to deliver effective and safe patient care may be improved by increasing nurses’ participation and acknowledging the role of respiratory therapists in clinical decision-making regarding mechanical ventilation and weaning.
Constraining pseudo‐label in self‐training unsupervised domain adaptation with energy‐based model

Deep learning is usually data starved, and the unsupervised domain adaptation (UDA) is developed to introduce the knowledge in the labeled source domain to the unlabeled target domain. Recently, deep self‐training presents a powerful means for UDA, involving an iterative process of predicting the target domain and then taking the confident predictions as hard pseudo‐labels for retraining. However, the pseudo‐labels are usually unreliable, thus easily leading to deviated solutions with propagated errors. In this paper, we resort to the energy‐based model and constrain the training of the unlabeled target sample with an energy function minimization objective. It can be achieved via a simple additional regularization or an energy‐based loss. This framework allows us to gain the benefits of the energy‐based model, while retaining strong discriminative performance following a plug‐and‐play fashion. The convergence property and its connection with classification expectation minimization are investigated. We deliver extensive experiments on the most popular and large‐scale UDA benchmarks of image classification as well as semantic segmentation to demonstrate its generality and effectiveness.
An Accurate Prediction Algorithm of RUL for Bearings: Time-Frequency Analysis Based on MRCNN

Remaining useful life (RUL) prediction of mechanical components is of high research value in the field of prognostics and health management (PHM). However, RUL prediction problems are completely challenging due to the complicacy of bearings’ operating environment. In this paper, we transform the vibration acceleration signal collected by sensors into a time-frequency domain matrix through continuous wavelet transform (CWT) and then extract the features of the time-frequency domain matrix through the proposed multiscale residual convolutional neural network (MRCNN), which enables the model to extract more local and global features while constructing more accurate health indicators (HI). In order to highlight the degradation trend of mechanical components, the obtained health indicators are smoothed by exponential moving average (EMA). Finally, linear regression is exploited to predict the RUL of the bearing. Performance evaluations based on the public dataset PRONOSTIA demonstrate the effectiveness of our proposed algorithm, which is superior to existing data-driven algorithms in terms of prediction accuracy.
ANALISIS KEPUASAN PENGGUNA SISTEM INFORMASI AKADEMIK DI UNIVERSITAS BHAYANGKARA SURABAYA

Dengan adanya penerapan sistem informasi akademik atau e-learning, universitas atau institusi dapat memenuhi tuntutan kebutuhannya dalam proses belajar mengajar yang fleksibel dengan semakin berkembangnya dunia pendidikan. Begitu pula dengan Sistem Informasi Manajemen (SIM) yang diterapkan di Universitas Bhayangkara Surabaya (Ubhara). Sistem informasi manajemen ini merupakan sistem informasi akademik yang berbasis website yang dapat diakses kapan saja dan dimana saja asalkan tersambung dengan koneksi internet. Tujuan yang ingin dicapai dari penelitian ini adalah untuk mengetahui apakah model end-user computing satisfaction yang telah dimodifikasi oleh Aggelidis & Chatzoglou (2012) dapat diaplikasikan pada Sistem Informasi Manajemen di Universitas Bhayangkara Surabaya dan apa saja faktor dari end-user computing satisfaction yang mempengaruhi kepuasan pengguna. Metode ini merupakan salah satu metode yang digunakan untuk menganalisis kepuasan pengguna sebuah sistem informasi. Penelitian ini menggunakan proportionate stratified random sampling dengan jumlah responden sebanyak 363 responden yang merupakan mahasiswa aktif di Universitas Bhayangkara Surabaya. Berdasarkan hasil analisis diperoleh nilai R-square sebesar 45,4% yang menyatakan bahwa information quality, system quality dan support in sourcing berpengaruh positif dan signifikan terhadap overall satisfaction (kepuasan secara keseluruhan). Kemudian system quality berpengaruh positif dan signifikan terhadap information quality. Lalu support in sourcing berpengaruh positif dan signifikan terhadap system quality.
Short-Term Associations between PM10 and Respiratory Health Effects in Visby, Sweden

The old Swedish city Visby, located on the island Gotland, has, for several years, reported higher PM10 concentrations than any other city in Sweden. In Visby, local limestone is used, both in road paving and as sand used for anti-slip measures, resulting in a clear annual pattern of PM10 with the highest concentrations during winter/spring when studded tires are allowed. This study analyzes the short-term associations between PM10 and daily number of patients with acute respiratory problems (ICD–10 diagnoses: J00–J99) seeking care at the hospital or primary healthcare units in Visby during the period of 2013–2019. The daily mean of PM10 was on average 45 µg m−3 during winter/spring and 18 µg m−3 during summer/autumn. Four outcome categories were analyzed using quasi-Poisson regression models, stratifying for period and adjusting for calendar variables and weather. An increase in respiratory visits was associated with increasing concentrations in PM10 during the summer/autumn period, most prominent among children, where asthma visits increased by 5% (95% CI: 2–9%) per 10 µg m−3 increase in PM10. For the winter/spring period, no significant effects were observed, except for the diagnose group ‘upper airways’ in adults, where respiratory visits increased by 1% (95% CI: 0.1–1.9%) per 10 µg m−3 increase. According to the results, limestone in particles seem to be relatively harmless at the exposure concentrations observed in Visby, and this is in line with the results from a few experimental and occupational studies.
A Randomized Controlled Trial Study of a Multimodal Intervention vs. Cognitive Training to Foster Cognitive and Affective Health in Older Adults

Research over the past few decades has shown the positive influence that cognitive, social, and physical activities have on older adults’ cognitive and affective health. Especially interventions in health-related behaviors, such as cognitive activation, physical activity, social activity, nutrition, mindfulness, and creativity, have shown to be particularly beneficial. Whereas most intervention studies apply unimodal interventions, such as cognitive training (CT), this study investigates the potential to foster cognitive and affective health factors of older adults by means of an autonomy-supportive multimodal intervention (MMI). The intervention integrates everyday life recommendations for six evidence-based areas combined with psychoeducational information. This randomized controlled trial study compares the effects of a MMI and CT on those of a waiting control group (WCG) on cognitive and affective factors, everyday life memory performance, and activity in everyday life. Three groups, including a total of 119 adults aged 65–86 years, attended a 5- or 10-week intervention. Specifically, one group completed a 10-week MMI, the second group completed 5-week of computer-based CT followed by a 5-week MMI, whereas the third group paused before completing the MMI for the last 5 weeks. All participants completed online surveys and cognitive tests at three test points. The findings showed an increase in the number and variability of activities in the everyday lives of all participants. Post hoc analysis on cognitive performance of MMI to CT indicate similar (classic memory and attention) or better (working memory) effects. Furthermore, results on far transfer variables showed interesting trends in favor of the MMI, such as increased well-being and attitude toward the aging brain. Also, the MMI group showed the biggest perceived improvements out of all groups for all self-reported personal variables (memory in everyday life and stress). The results implicate a positive trend toward MMI on cognitive and affective factors of older adults. These tendencies show the potential of a multimodal approach compared to training a specific cognitive function. Moreover, the findings suggest that information about MMI motivates participants to increase activity variability and frequency in everyday life. Finally, the results could also have implications for the primary prevention of neurocognitive deficits and degenerative diseases.