Miracle-dz/UniFAR-FTUs huggingface dataset:
THE ROLE OF INNOVATION IN REGIONAL COMPETITIVENESS

{BSTRACT The paper reports on an eleven-region, European Union-funded research :roject on Regional Innovation Systems (RIS). The RIS concept is relatively new, though :creasingly being used by academics and policy-makers to seek to fashion a new approach '. : regional policy in which innovation capabilities of regional firms and organisations are :::rphasised. The key research findings are that regions warranting the RIS designation are '- lnd in Europe, but that by no means all regions have functional, systemic innovation. '. 1 rst firms'R&D activities in Europe are remarkably regionally and nationally focused. -- . :ect effects of globalisation mainly affect large firms. Regions are also important for , -:ply-chain relations and inter-firm co-operation. Innovation is nowadays seen as a core :"-. iness activity for many firms because their customers demand higher quality products :. - j services at lower cost. Despite this, much of the enterprise support infrastructure for :l::ll and medium-sized firms is not reaching them satisfactorily and is generally in need of :. :rhaul.
Design of Incu Analyzer for IoT-Based Baby Incubator Calibration

An incubator analyzer, serving as a calibration tool, is utilized to measure diverse parameters such as temperature, mattress temperature, humidity, airflow, and noise in infant incubators. The present study focuses on the development of the Design Incubator Analyzer for IoT (Mattress Temperature and Humidity) with LCD and ThingSpeak display, specifically designed for calibrating baby incubators. The primary objective is to design and develop an Incubator Analyzer as a calibration device for assessing various parameters in infant incubators, encompassing temperature, mattress temperature, humidity, airflow, and noise. The design of this calibration device incorporates a Thermocouple Type-K sensor for baby incubator mattress temperature parameters, a DHT22 sensor for humidity parameters, and an ESP32 microcontroller. The ESP32 processes data from the Thermocouple Type-K and DHT22 sensors to generate values for mattress temperature (TM) and humidity (RH), which are then displayed on LCD and ThingSpeak displays. The device underwent rigorous testing against an established measuring device, the INCU II. In the study, the TM parameter or mattress temperature exhibited the smallest error of -0.0140% at 35°C and the largest error of 0.0584% at 36°C. Concerning the humidity parameter, the largest error was 0.0570% at 32°C, while the smallest error was 0.0207% at 35°C. Overall, the Incubator Analyzer Design for IoT-Based Baby Incubator Calibration device, or IoT-based Incubator Analyzer, demonstrates potential usability following the planning and execution phases, including a thorough review of existing literature. To enhance user experience during the calibration process, an IoT system was developed for data transmission over Wi-Fi, presenting results on the ThingSpeak platform in real-time
Determination of Activity Concentration and Radiological Parameters of Natural Radionuclides for Soil Samples from Kogi State University Staff Nursery and Primary School, Kogi State

The activity concentrations of naturally occurring radionuclides 226Ra, 232Th and 40K, in soil samples collected from the Kogi State University, Anyigba staff Nursery and Primary School were determined by Gamma spectroscopy method. The average activity concentrations of 226Ra, 232Th and 40K for SSN soil samples were found to be 78.67 ± 40.51, 11.50 ± 9.83, 4.15 ± 3.27 Bqkg-1, respectively; and SSP soil samples have average activity concentration values of 81.06 ± 53.28, 16.79 ± 18.00, 6.08 ± 2.73 Bqkg-1, respectively. The results obtained for the corresponding radionuclides are lower than the worldwide average values of 35, 30 and 400 Bqkg-1, respectively. The average absorbed dose rate in air (D) and the annual effective dose rates equivalent (AEDE) due to 226Ra, 232Th and 40K were observed to be 7.30 nGyh-1 and 8.95 μSvy-1, respectively for SSN, and 10.26 nGyh-1 and 12.58 μSvy-1, respectively for SSP. These values are all below the world average value of 60 nGyh-1 and 80 μSvy-1, respectively; the average radium equivalent (Raeq) activity, internal and external hazard indices estimated for SSN were 11.10 Bqkg-1, 0.04 and 0.03; and 15.29 Bqkg-1, 0.07 and 0.04, respectively for SSP. These values are also less than the world average values of 370 Bqkg-1 and unity, respectively. These results indicate that there are no associated radiological hazards.
HISTOPATHOLOGIC PATTERNS, CYTOHISTOLOGIC CORRELATION AND ASSOCIATED FACTORS OF THYROID LESIONS AMONG PATIENTS WITH

ii Abstract Introduction : Thyroid diseases are major health problems, that are manifested by alteration in hormone secretion, enlargement of the thyroid gland (goiter), or both. Identifying and characterizing lesions of the thyroid gland have pivotal role since most of the lesions require medical or surgical management. Fine needle aspiration cytology (FNAC) is an important initial screening tool and histopathologic examination is important in the definitive diagnosis of thyroid disease. Objective : To assess histopathologic patterns, cytohistologic correlation of thyroid lesions and associated factors among patients with thyroid biopsy. Methods : A Five-year retrospective cross-sectional study was conducted from August 1 to September 30 2021. A review of 581 eligible biopsies, including 286 cases in which Fine needle aspiration was done prior to surgery, submitted from thyroidectomy specimens at Jimma medical center, pathology department from 14th September 2015 to 10th of September 2020. Data was cleaned, coded and entered into Epidata v3.1 and exported to SPSS version 26 for analysis. Descriptive and analytic studies were done. Cross tabulation, chi square test and logistic regression with multivariate analysis were done to look for associations between the study variables. A p-value<0.05 was used as a cut-off point for identifying predictors for histopathologic patterns. The findings were presented using text, tables, diagrams and charts. Result : Majority of thyroid lesions,432 (74.4%), were found to be non-neoplastic and 149 (25.6%) were neoplastic. Colloid goiter was found in 417 (71.8%) cases. Adenoma, carcinoma and thyroiditis accounted for 54 (9.3%), 95 (16.3%) and 12 (2.1%) cases respectively. Among 286 FNAC results cyto-histologic correlation was achieved in 233 (86.6%) cases. Sensitivity and specificity of FNAC were 71% and 92.7% respectively with diagnostic accuracy of 86.6%. Conclusion: Nodular colloid goiter is by far the most common thyroid lesion and Papillary thyroid
Avaliação de cadeias de suprimentos sustentáveis: o caso dos operadores logísticos brasileiros

This paper highlights contributions that organizations can make decisions when choosing business partners viewing a set of sustainable variables and the possible behavior that the supply chain can be conditioned. For this, an exploratory research was conducted, contemplating a literature review and a questionnaire sent to eleven Brazilian logistics operators. The results show that the surveyed companies have certifications that address environmental issues, however, one realizes that they are actions that most prioritize the issue of regulation, because in addition to being certified which, if not taken result in a penalty, there is no management integrated flows of these actions with others involved in the process by the gap of periodic inspection.
Human-Centered Computing : Toward a Human Revolution

C omputing is leading a revolution—everything we do is changing at a pace never experienced before in human history. Whether we talk about the pervasive, ubiquitous, mobile, grid, or even the social computing revolution, we can be sure that computing is impacting the way we interact with each other, the way we design and build our homes and cities, the way we learn, the way we communicate, the way we play, the way we work. Simply put, computing technologies are increasingly affecting and transforming almost every aspect of our daily lives. Unfortunately, the changes are not always positive, and much of the technology we use is clunky, unfriendly, unnatural, culturally biased, and difficult to use. As a result, several aspects of daily life are becoming increasingly complex and demanding. We have access to huge amounts of information, much of which is irrelevant to our own local sociocultural context and needs or is inaccessible because it is not available in our native language, we cannot fully utilize the existing tools to find it, or such tools are inadequate or nonexistent. Thanks to computing technologies, our options for communicating with others have increased, but that does not necessarily mean that our communications have Human-centered computing studies the
Climate change e¡ects on bene¢cial plant^microorganism interactions

It is well known that beneﬁcial plant-associated microorganisms may stimulate plant growth and enhance resistance to disease and abiotic stresses. The effects of climate change factors such as elevated CO 2 , drought and warming on beneﬁcial plant–microorganism interactions are increasingly being explored. This now makes it possible to test whether some general patterns occur and whether different groups of plant-associated microorganisms respond differently or in the same way to climate change. Here, we review the results of 135 studies investigating the effects of climate change factors on beneﬁcial microorganisms and their interaction with host plants. The majority of studies showed that elevated CO 2 had a positive inﬂuence on the abundance of arbuscular and ectomycorrhizal fungi, whereas the effects on plant growth-promoting bacteria and endophytic fungi were more variable. In most cases, plant-associated microorganisms had a beneﬁcial effect on plants under elevated CO 2 . The effects of increased temperature on beneﬁcial plant-associated microorganisms were more variable, positive and neutral, and negative effects were equally common and varied considerably with the study system and the temperature range investigated. Moreover, numerous studies indicated that plant growth-promoting microorganisms (both bacteria and fungi) positively affected plants subjected to drought stress. Overall, this review shows that plant-associated microorganisms are an important factor inﬂuencing the response of plants to climate change.
Reform of College English Vocabulary Teaching with the” Vocabulary Handout” as the Core from the Perspective of the Levels of Processing Theory

Under the theoretical framework of Levels of Processing, this study designs and implements a university English vocabulary teaching model with the “Vocabulary Handout” at its core. This innovative vocabulary teaching model is driven by two testing sessions, selects vocabulary based on students’ recent learning needs, and applies the Levels of Processing Theory to conduct shallow processing, deep processing, and elaborative processing on target words. The scientific design and implementation of university English vocabulary instruction have positively influenced the teaching and learning of vocabulary in the university context.
Anti-JNK2 peptide–siRNA nanostructures improve plaque Anti-JNK2 peptide–siRNA nanostructures improve plaque endothelium and reduce thrombotic risk in atherosclerotic mice endothelium and reduce thrombotic risk in atherosclerotic mice

Background: A direct and independent role of inflammation in atherothrombosis was recently highlighted by the Canakinumab Antiinflammatory Thrombosis Outcome Study (CANTOS) trial, showing the benefit of inhibiting signaling molecules, eg, interleukins. Accordingly, we sought to devise a flexible platform for preventing the inflammatory drivers at their source to preserve plaque endothelium and mitigate procoagulant risk. Methods: p5RHH-siRNA nanoparticles were formulated through self-assembly processes. The therapeutic efficacy of p5RHH-JNK2 siRNA nanoparticles was evaluated both in vitro and in vivo. Results: Because JNK2 is critical to macrophage uptake of oxidized lipids through scavenger receptors that engender expression of myriad inflammatory molecules, we designed an RNA-silencing approach based on peptide–siRNA nanoparticles (p5RHH-siRNA) that localize to atherosclerotic plaques exhibiting disrupted endothelial barriers to achieve control of JNK2 expression by macrophages. After seven doses of p5RHH-JNK2 siRNA nanoparticles over 3.5 weeks in ApoE - / - mice on a Western diet, both JNK2 mRNA and protein levels were significantly decreased by 26% ( P = 0.044) and 42% ( P = 0.042), respectively. Plaque-macrophage populations were markedly depleted and NF κ B and STAT3-signaling pathways inhibited by 47% ( P , 0.001) and 46% ( P = 0.004), respectively. Endothelial barrier integrity was restored (2.6-fold reduced permeability to circulating 200 nm nanoparticles in vivo, P = 0.003) and thrombotic risk attenuated (200% increased clotting times to carotid artery injury, P = 0.02), despite blood-cholesterol levels persistently exceeding 1,000 mg/dL. No adaptive or innate immunoresponses toward the nanoparticles were observed, and blood tests after the completion of treatment confirmed the largely nontoxic nature of this approach. Conclusion: The ability to formulate these nanostructures rapidly and easily interchange or multiplex their oligonucleotide content represents a promising approach for controlling deleterious signaling events locally in advanced atherosclerosis.
Age‐dependent migration strategy in honey buzzards Pernis apivorus tracked by satellite

Six adult and three juvenile honey buzzards Pernis apivorus were radio-tracked by satellite during autumn migration from southwestern Sweden. All adults crossed the Mediterranean Sea at the Strait of Gibraltar and continued across the Sahara desert to winter in West Africa, from Sierra Leone to Cameroon. Analysing three main steps of the migration, (1) from the breeding site to the southern Mediterranean region, (2) across the Sahara and (3) from the southern Sahara to the wintering sites, the adults changed direction significantly between these steps, and migrated along a distinct large-scale detour. In contrast, the juveniles travelled in more southerly directions, crossed the Mediterranean Sea at various places, but still ended up in the same wintering areas as the adults. Average speeds maintained on travelling days were similar for the two age groups, about 170 km/day in Europe, 270 km/day across Sahara and 125 km/day in Africa south of Sahara. However, as the adults used fewer stopover days en route, they maintained higher mean overall speeds and completed migration in a shorter time (42 days) than the juveniles (64 days). Although the juveniles set out on more direct courses towards the wintering grounds, they did not cover significantly shorter distances than the adults, as they tended to show a larger directional scatter between shorter flight segments. The results corroborate previous suggestions that adult and juvenile honey buzzards follow different routes during autumn migration, and that the birds change migration strategy during their lifetime. While juveniles may use individual vector orientation, social influences and learning may be of great importance for the detour migration of adults. The remarkable and distinct age-dependent shift in migratory route and orientation of the honey buzzard provides a challenging evolutionary problem.
Enhancing K-means Clustering Results with Gradient Boosting: A Post-Processing Approach

—As the volume and complexity of data continue to grow exponentially, finding efficient and accurate clustering algorithms has become crucial for many applications. K-means clustering is a widely used unsupervised machine learning technique for data analysis and pattern recognition. Despite its popularity, k-means suffers from certain limitations, such as sensitivity to initial conditions, difficulty in determining the optimal number of clusters, and the potential for misclassification. This research paper proposes an enhanced approach for improving the accuracy and performance of the k-means clustering algorithm by incorporating post-processing techniques using a gradient boosting algorithm. The proposed method comprises training the gradient boosting model on the labeled training set, i.e., the samples with correct cluster assignments obtained from the k-means algorithm, to predict the correct cluster assignments for the misclassified samples in the testing set. This results in refined cluster assignments for the testing set. The k-means algorithm is only used initially to cluster the data and obtain initial cluster assignments. The effectiveness of the proposed approach is validated through experiments on several benchmark datasets, and the results show a significant improvement in clustering accuracy and robustness compared to the standard k-means algorithm. The proposed approach has the potential to enhance the performance of k-means in various real-world applications and domains.
tinyRadar for Gesture Recognition: A Low-power System for Edge Computing

—Hand gesture recognition (HGR) plays a pivotal role in improving human-machine interaction across domains like smart homes/vehicles and wearable devices. While vision-based HGR systems encounter challenges with lighting, complex backgrounds, and occlusion, radar-based systems overcome these limitations by harnessing electromagnetic principles. This paper presents tinyRadar, a real-time, low-power, single-chip radar solution for HGR. By leveraging miniaturized mmWave radar hardware, tinyRadar offers a compact and cost-effective HGR solution. The Texas Instruments IWRL6432 radar is utilized, achieving a total power consumption of less than 80mW and a memory footprint of ∼ 11 KB for the quantized inference model and < 256 KB for the entire system. The solution utilizes quantized depthwise separable convolutions and integrates a hardware accelerator and Cortex®-M4 microcontroller for real-time inference. With its small form factor and low power requirements, tinyRadar facilitates on-edge implementation, delivering 95% real-time inference accuracy for six gestures. This paper contributes to developing wearable gadgets and IoT devices that seamlessly incorporate HGR technology.
MFSC: Matching by Few-Shot Classiﬁcation

The ability to accurately and efﬁciently match between sets of items has always been fundamental in computer vision pipelines and applications with a wide variety of realizations that involve ﬁnding correspondences between sets of local features, small patches or entire images collections. In recent years, the emergence of deep learning has facilitated signiﬁcant improvements of matching based applications. This progress was achieved through advancing improved data embedding and description, and less focus was put on the matching process itself. Speciﬁcally, relying on simple pairwise or triplet distance-based metric learning, ignoring the set-to-set nature of the problem. We suggest a holistic approach to matching, by observing its natural connection to few-shot classiﬁcation (FSC), a largely growing research area that deals with learning using limited amounts of data. We argue that certain popular FSC paradigms, such as meta-learning and transductive learning, are particularly suitable for tackling the speciﬁc challenges that arise in matching problems. Our approach, MFSC, builds upon state-of-the-art features and FSC algorithms, signiﬁcantly improving the quality of matching. Moreover, we show how to construct a meta-learning scheme based on our approach, which allows end-to-end training of the entire matching process. We validate our method on the tasks of patch-correspondence, image-alignment and person re-identiﬁcation.
Cloud Computing Security Challenges

: Cloud computing might change Internet consumption in the ever-changing technological environment. This study carefully explores Cloud computing security issues, from data privacy to service delivery model dynamics. The problem is creating SLAs that are precise enough to cover critical features and simple to comprehend. This is much more complicated when working with IaaS, PaaS, and SaaS cloud services. The diversity of cloud providers makes it important to carefully consider SLA detail, according to the report. In a broad market with several cloud services, interoperability is crucial. According to the "Hazy Cloud" phenomenon, vendor locking occurs because each cloud provider has a distinct methodology that hinders smooth interaction. The study proposes balancing Cloud computing's transformational potential with watchful security measures to shape IT solutions. It highlights the security dangers of Cloud computing and stresses the need for a deliberate and educated approach to reap its advantages.
L EARNING THEN L EVERAGING S TRUCTURES H ELP WITH C OMPLEX , C OMPOSITIONAL , C AUSAL S EQUEN - TIAL T ASKS IN I NVERSE R EINFORCEMENT L EARNING

Inverse Reinforcement Learning (IRL) has experienced substantial advancements in recent years, with commendable newer approaches yielding crucial applications in diverse areas such as robotics, cognition, and healthcare. This paper underscores the limitations of foundational IRL methods when learning an agent’s reward function from expert trajectories that have underlying causal reward structure. We posit that imbuing IRL models with causal structural motifs capturing underlying relationships between actions and outcomes or, the reward logic can enable and enhance their performance. Based on this hypothesis, we propose SMIRL – an IRL approach that initially learns the task’s structure as a finite-state-automaton (FSA) and subsequently leverages this structural motif to solve the IRL problem. We demonstrate SMIRL’s capabilities in both discrete (grid world) and high-dimensional continuous domain environments across four logic based tasks. The SMIRL approach proves adept at learning tasks characterized by causal reward functions, a known limitation of foundational IRL approaches.Our model also outperforms the baselines in sample efficiency on tasks. We further show promising test results in a modified continuous domain on tasks with compositional reward functions
Exogenous market changes analysis using arti�cial options volatility

This paper focuses on market changes due to exogenous eﬀects. The standard implied volatility is shown to be insuﬃcient for a proper detection and analysis of this type of risk. This is mainly because such changes are usually dominated by endogenous eﬀects coming from a speciﬁc trading mechanism or a natural market dynamics. A unique methodological approach based on artiﬁcial options that always have a constant (over time) maturity is proposed and explicitly deﬁned. The key principle is to use interpolated volatilities that can eﬀectively eliminate instabilities due to the natural market dynamics while the changes caused by the exogenous causes stay preserved. Formal statistical tests for distinguishing signiﬁcant eﬀects are proposed under diﬀerent theoretical and practical scenarios. Statistical theory, computational and algorithmic details, and comprehensive empirical comparisons together with a real data illustration are all presented in the paper.
The independence of central banks: a reductio ad absurdum

This paper testifies to the fact that the proclaimed independence of central banks, as conceived by its founders, is nothing more than a chimera. We demonstrate that the hypothesis ‘inflation is a purely monetary phenomenon’ does not substantiate the case for independence. Further, the portrayal of the conservative central banker, the imaginary principal-agent contract, the alleged financial autonomy, along with the ban on budgetary financing, amount to flawed logic in arguing for the independence of the central bank. We also highlight that the idea of independence is not convincing due to the absence of well-defined outlines in its operational toolbox and the system of rules it relies upon.