Miracle-dz/UniFAR-FTUs huggingface dataset:
The power/knowledge of consultants and project management office in enterprise system implementation: a case study of a Saudi Arabian university

This thesis explores the power/knowledge of consultants and the project management office (PMO) in the implementation of an Enterprise Resource Planning (ERP) system and its subsequent development. The research followed a qualitative approach involving an interpretive in-depth case study. The study was conducted at a Saudi Arabian university and explored the power of consultants and the PMO in the implementation of enterprise systems in order to better understand the interaction of power and knowledge in the implementation and development process. A theoretical framework was developed by applying Clegg’s (1989) Circuits of Power, Absorptive Capacity, Agency theory, Structuration theory, and Neo-institutional theory. This theoretical framework was used to guide data collection and analysis. A total of 34 interviews with senior management, consultants, technical staff, project managers, and end users were conducted. Data were analysed following Creswell’s (2013) approach, and the principles of interpretive research in information systems (IS) proposed by Klein and Myers (1999) were applied. This study found that PMO and associated consultants have become powerful in ERP implementation and can mobilize power/knowledge by speaking for both the technology and the organization. This study contributes to research addressing ERP by investigating the power/knowledge of highly influential actors (consultants and PMOs) in the adoption and implementation of ERP. It also contributes to the literature on ERP and power, which has not extensively explored the context of developing nations such as Saudi Arabia.
Securing IP Cores for DSP Applications Using Structural Obfuscation and Chromosomal DNA Impression

This paper presents a novel hybrid methodology with structural obfuscation and encrypted chromosomal DNA impression to secure intellectual property (IP) cores of digital signal processing (DSP) applications. The proposed work offers security against altering register transfer level (RTL) description using multilevel structural obfuscation as well as security against IP piracy using secret chromosomal DNA impression. In this approach, an invisible DNA impression is covertly implanted into the structurally obfuscated DSP design using robust encoding and encryption using multi-iteration Feistel cipher. Our work is more robust than recent facial biometric and steganography-based hardware IP security techniques, in terms of stronger proof of digital evidence as well as tamper tolerance ability. The results report following qualitative and quantitative analysis of the proposed structural obfuscation with encrypted chromosomal DNA impression based framework: (a) very low probability of coincidence (Pc) (indicating strength of digital evidence) for different DSP IP cores in the range of 7.59E-5 to 1.2E-1; (b) stronger tamper tolerance for different DSP IP cores in the range of 5.62E +14 to 3.40E +38; (c) negligible design cost overhead of 0.00% for different DSP IP cores; (d) strength of obfuscation in terms of number of gates obfuscated.
The effect of modified atmosphere storage on the quality of fresh dill(Anethum graveolens L.)

Because of its high perishability, fresh dill (Anethum graveolens L.) cannot be stored for more than a few days, even at low temperature. The present study was therefore undertaken to determine whether modified atmospheres based on changes in CO2 and O2 concentrations would benefit dill storage. Dill cultivar ‘Ducat’ was cultivated between October 2009 and January 2010. Plants were harvested at the fresh market stage (before flowering) and randomly selected leaves were weighed, placed in airtight plastic bags and stored for 10 days at 5°C. The atmospheres within the bags (O2-CO2-N2) were initially as follows: (1) Control – 20-0-80 (air), (2) 20-10-70, (3) 10-0-90, and (4) 10-10-80. The changes in O2-CO2 concentrations were monitored during storage; fresh weight, chlorophyll, vitamin C and total phenolics concentrations were measured before and after storage. The O2 concentration within the bags during storage fell to 12.5% (control) and 17.9% (treatment 2) indicating a reduction of respiration due to the inclusion of 10% CO2 within the initial atmosphere (20% O2). At an initial concentration of 10% O2, the O2 level decreased to 5.5 and 5.8% in the absence or presence of 10% CO2 (treatments 3 and 4), respectively. In each case, the decrease in O2 was accompanied by a corresponding increase in CO2 concentration. Weight loss during storage ranged from 2.6-5.3% and the total phenolics levels decreased in all treatments except treatment 3 (10-0-90). Vitamin C and chlorophyll concentrations decreased during storage. Vitamin C loss was similar in all treatments, but chlorophyll loss was significantly higher in the treatments with 10% CO2. In conclusion, although modified atmospheres containing 10% CO2 reduce respiratory activity they are of questionable value for dill because the decrease in chlorophyll concentration causes a decrease in quality.
Carceral algorithms and the history of control: An analysis of the Pennsylvania additive classification tool

Scholars have focused on algorithms used during sentencing, bail, and parole, but little work explores what we term “carceral algorithms” that are used during incarceration. This paper is focused on the Pennsylvania Additive Classification Tool (PACT) used to classify prisoners’ custody levels while they are incarcerated. Algorithms that are used during incarceration warrant deeper attention by scholars because they have the power to enact the lived reality of the prisoner. The algorithm in this case determines the likelihood a person would endure additional disciplinary actions, can complete required programming, and gain experiences that, among other things, are distilled into variables feeding into the parole algorithm. Given such power, examining algorithms used on people currently incarcerated offers a unique analytic view to think about the dialectic relationship between data and algorithms. Our examination of the PACT is two-fold and complementary. First, our qualitative overview of the historical context surrounding PACT reveals that it is designed to prioritize incapacitation and control over rehabilitation. While it closely informs prisoner rehabilitation plans and parole considerations, it is rooted in population management for prison securitization. Second, on analyzing data for 146,793 incarcerated people in PA, along with associated metadata related to the PACT, we find it is replete with racial bias as well as errors, omissions, and inaccuracies. Our findings to date further caution against data-driven criminal justice reforms that rely on pre-existing data infrastructures and expansive, uncritical, data-collection routines.
Cognitive inhibition explains children’s production of medial wh-phrases

ABSTRACT Non-adult-like linguistic behavior in children is sometimes taken as evidence for endogenous factors that drive selection of grammatical features from the child’s hypothesis space of possible grammars. Analyses of English-acquiring children’s productions of medial wh-phrases exemplify this trend in particular. We provide an alternative account of these productions as performance errors arising from underdeveloped cognitive inhibition. We offer experimental evidence in favor of our failure of inhibition account. The results argue against treating these errors as reflecting incomplete or non-target acquisition of grammatical features. Instead, the results support a theory of how these errors arise and are subsequently purged from children’s productions that reduces to a theory of how cognitive inhibition develops during childhood.
Effectiveness of Intravenous Ibuprofen on Emergence Agitation in Children Undergoing Tonsillectomy with Propofol and Remifentanil Anesthesia: A Randomized Controlled Trial

Background Emergence agitation (EA) has a negative effect on the recovery from general anesthesia in children. This study aimed to evaluate the effectiveness of intravenous ibuprofen in reducing the incidence of EA in children. Methods This randomized, double-blind, placebo-controlled, single-center study analyzed data from patients aged 3–9 years undergoing tonsillectomy under general anesthesia with propofol and remifentanil. These patients were randomly assigned to receive either the ibuprofen or the placebo intraoperatively. The primary endpoint was a between-group difference in the incidence of EA at 15 min following extubation. EA was defined as Pediatric Anesthesia Emergence Delirium score ≥10. The secondary endpoint included the associated factors of EA. Results Eighty-nine patients were included in the study. Ibuprofen decreased the incidence of EA at 15 min following extubation (8.9% in the treatment group vs 34.1% in the control group; odds ratio [OR], 0.261; 95% confidence interval [CI], 0.094–0.724; P=0.004). Compared with the control group, there was a significant reduction in the number of rescue fentanyl doses (P=0.045), and fewer patients experienced moderate to severe pain at 15 min following extubation in the treatment group (P=0.048). Upon logistic regression analysis, high modified Pediatric Anesthesia Behavior and pain scores following surgery were considered the risk factors related to EA (OR, 8.07; 95% CI, 1.12–58.07, P=0.038 and OR, 2.78; 95% CI, 1.60–4.82, P<0.001, respectively). Ibuprofen administration was the protective factor related to EA (OR, 0.05; 95% CI, 0.01–0.67, P=0.023). Conclusion Intraoperative ibuprofen infusion can significantly reduce the incidence of EA following general anesthesia with propofol and remifentanil in children. Trial Registration The study was registered with the Chinese Clinical Trial Registry on 7 April 2021 (number: ChiCTR2100045128; https://www.chictr.org.cn/edit.aspx? pid=124595&htm=4).
Automated Segmentation of Prohibited Items in X-Ray Baggage Images Using Dense De-Overlap Attention Snake

Prohibited item segmentation has a wide range of applications in the security check field, such as computer-aided screening, threat image projection and material discrimination. However, the severe object overlapping in X-ray baggage images restricts the performance of common CNN-based segmentation methods greatly. Worse, no public dataset can be used to promote research in this challenging and promising area. In this paper, to cope with these problems, we present the first Prohibited Item X-ray segmentation dataset named PIXray. PIXray comprises 5,046 X-ray images, in which 15 classes of 15,201 prohibited items are annotated as instance-level masks. Besides, we contribute a dense de-overlap attention snake (DDoAS) in the context of deep learning for automated and real-time prohibited item segmentation. DDoAS mainly includes a dense de-overlap module (DDoM) and an attention deforming module (ADM). Specifically, DDoM is designed to infer prohibited item information accurately from extreme background overlaps through dense reversed connections. ADM aims to improve the low learning efficiency introduced by large variations in shapes and sizes among different prohibited items. Comprehensive evaluation on the PIXray shows the effectiveness and superiority of DDoM and ADM. DDoM excels at recognizing prohibited items from complex backgrounds than other in-domain methods and achieves consistent performance gain over various network backbones, extending the idea of tackling overlapping images data. ADM can ease the model training and further refine the mask quality. Furthermore, out-of-domain experiments prove that DDoAS can also be applied to natural images and achieves comparable performance to the state-of-the-art methods, which implies its potential applications in other fields. The dataset and source code are available at https://github.com/Mbwslib/DDoAS.
Zero-based Budgeting System: Is Budgeting System the Determinant of Budget Implementation in Nigeria?

Budget is a financial and non-financial framework in terms of cash flows that guides governments, private organizations and individuals in achieving their desired objectives in a particular period if it is properly, adequately and realistically prepared. The long standing and familiar incremental budget has been faulted by various stakeholders hence, the contemplation for an alternative system known as zero-based budgeting (ZBB). The first objective of this study is to find out whether or not the theoretical benefits accruable to ZBB can motivate the governmentsâ€™ ministries, departments and agencies to adopt and implement the proposed new system? The second objective is to determine whether or not the budgeting system has any relationship with budget implementation. Questionnaire was used in collecting data from the budget stakeholders. Descriptive statistics and simple regression were adopted in analyzing the data. It is established that the theoretical benefits accruable to ZBB can influence the adoption and implementation of the proposed ZBB. It is also revealed that the proposed budgeting system has a strong relationship with implementation. The study recommends that, despite the support for the ZBB, the current incremental budgeting system would have to be reviewed for reference into adopting and implementing the ZBB since it will be the basis for the new system. It is also recommended that the coming into operation of the new system should be a gradual process in the form of test running it to address the acknowledged challenges in the proposed system before it can be fully implemented. In addition, there should be seriousness in the whole exercise.
Automated Rehabilitation Exercise Motion Tracking

Current physiotherapy practice relies on visual observation of the patient for diagnosis and assessment. The assessment process can potentially be automated to improve accuracy and reliability. This thesis proposes a method to recover patient joint angles and automatically extract movement profiles utilizing small and lightweight body-worn sensors. Joint angles are estimated from sensor measurements via the extended Kalman filter (EKF). Constant-acceleration kinematics is employed as the state evolution model. The forward kinematics of the body is utilized as the measurement model. The state and measurement models are used to estimate the position, velocity and acceleration of each joint, updated based on the sensor inputs from inertial measurement units (IMUs). Additional joint limit constraints are imposed to reduce drift, and an automated approach is developed for estimating and adapting the process noise during on-line estimation. Once joint angles are determined, the exercise data is segmented to identify each of the repetitions. This process of identifying when a particular repetition begins and ends allows the physiotherapist to obtain useful metrics such as the number of repetitions performed, or the time required to complete each repetition. A feature-guided hidden Markov model (HMM) based algorithm is developed for performing the segmentation. In a sequence of unlabelled data, motion segment candidates are found by scanning the data for velocity-based features, such as velocity peaks and zero crossings, which match the pre-determined motion templates. These segment potentials are passed into the HMM for template matching. This two-tier approach combines the speed of a velocity feature based approach, which only requires the data to be differentiated, with the accuracy of the more computationally-heavy HMM, allowing for fast and accurate segmentation. The proposed algorithms were verified experimentally on a dataset consisting of 20 healthy subjects performing rehabilitation exercises. The movement data was collected by IMUs strapped onto the hip, thigh and calf. The joint angle estimation system achieves an overall average RMS error of 4.27 cm, when compared against motion capture data. The segmentation algorithm reports 78% accuracy when the template training data comes from the same participant, and 74% for a generic template.
RRT*-Based Path Planning for Continuum Arms

Continuum arms are bio-inspired devices that exhibit continuous, smooth bending and generate motion through structural deformation. Rapidly-exploring random trees (RRT) is a traditional approach for performing efficient path planning. RRT approaches are usually based on exploring the configuration space (C-Space) of the robot to find a desirable work space (W-Space) path. Due to the complex kinematics and the highly non-linear mapping between the C-Space and W-Space of continuum arms, a high-quality path in the C-Space (e.g., a linear path) may not correspond to a desirable path/movement in the W-Space. Consequently, the C-Space RRT approaches that are based on C-Space cost functions do not lead to reliable and effective path planning when applied to continuum arms. In this letter, we propose a RRT* path planning approach for continuum arms that is based on exploring the W-Space of the robot as opposed to its C-Space. We show the successful applications of the proposed W-Space RRT* path planner in performing path planning with obstacle avoidance and in performing trajectory tracking. In all the aforementioned tasks, the quality of the paths generated by the proposed planner is superior to that of previous approaches and to its counterpart C-Space based RRT* approach, while the paths are generated in substantially less time.
Drones Classification by the Use of a Multifunctional Radar and Micro-Doppler Analysis

The classification of targets by the use of radars has received great interest in recent years, in particular in defence and military applications, in which the development of sensor systems that are able to identify and classify threatening targets is a mandatory requirement. In the specific case of drones, several classification techniques have already been proposed and, up to now, the most effective technique was considered to be micro-Doppler analysis used in conjunction with machine learning tools. The micro-Doppler signatures of targets are usually represented in the form of the spectrogram, that is a time–frequency diagram that is obtained by performing a short-time Fourier transform (STFT) on the radar return signal. Moreover, frequently it is possible to extract useful information that can also be used in the classification task from the spectrogram of a target. The main aim of the paper is comparing different ways to exploit the drone’s micro-Doppler analysis on different stages of a multifunctional radar. Three different classification approaches are compared: classic spectrogram-based classification; spectrum-based classification in which the received signal from the target is picked up after the moving target detector (MTD); and features-based classification, in which the received signal from the target undergoes the detection step after the MTD, after which discriminating features are extracted and used as input to the classifier. To compare the three approaches, a theoretical model for the radar return signal of different types of drone and aerial target is developed, validated by comparison with real recorded data, and used to simulate the targets. Results show that the third approach (features-based) not only has better performance than the others but also is the one that requires less modification and less processing power in a modern multifunctional radar because it reuses most of the processing facility already present.
New Potentiometric Screen-Printed Platforms Modified with Reduced Graphene Oxide and Based on Man-Made Imprinted Receptors for Caffeine Assessment

Caffeine is a psychoactive drug that is administered as a class II psychotropic substance. It is also considered a component of analgesics and cold medicines. Excessive intake of caffeine may lead to severe health damage or drug addiction problems. The assessment of normal caffeine consumption from abusive use is not conclusive, and the cut-off value for biological samples has not been established. Herein, new cost-effective and robust all-solid-state platforms based on potentiometric transduction were fabricated and successfully utilized for caffeine assessment. The platforms were modified with reduced graphene oxide (rGO). Tailored caffeine-imprinted polymeric beads (MIPs) based on methacrylic acid (MAA) and ethylene glycol dimethacrylate (EGDMA) were prepared, characterized, and used as recognition receptors in the presented potentiometric sensing devices. In 50 mM MES buffer, the sensors exhibited a slope response of 51.2 ± 0.9 mV/decade (n = 6, R2 = 0.997) over the linear range of 4.5 × 10−6–1.0 × 10−3 M with a detection limit of 3.0 × 10−6 M. They exhibited fast detection of caffeinium ions with less than 5 s response time (<5 s). The behavior of the presented sensors towards caffeinium ions over many common organic and inorganic cations was evaluated using the modified separate solution method (MSSM). Inter-day and intra-day precision for the presented analytical device was also evaluated. Successful applications of the presented caffeine sensors for caffeine determination in commercial tea and coffee and different pharmaceutical formulations were carried out. The data obtained were compared with those obtained by the standard liquid chromatographic approach. The presented analytical device can be considered an attractive tool for caffeine determination because of its affordability and vast availability, particularly when combined with potentiometric detection.
Innovation support services: an evidence-based exploration of their strategic roles in the Italian AKIS

ABSTRACT Purpose: This study explores the state of the art of innovation support services within cooperation projects for innovation in Italy to shed light on the variety of actors that are supporting them and the functions they perform. Design/Methodology/Approach: The methodology applied for this study was based on a mix of methods used to monitor and investigate different aspects of cooperation projects for innovation over a period running from 2013 to the present. The analytical framework draws on the extensive literature on innovation support functions. Findings: The study highlights a transition scenario of advisory services towards ISSs. A multiplicity of actors, individually and in association, are engaged in supporting co-innovation processes providing all the functions which are used to orient actors in the different phases of the innovation process. Practical implications: These findings lay the foundations for reconsidering the role of the innovation support services within the agricultural knowledge and innovation system towards a more inclusive approach that recognises different types of services providers and involves them since the definition of the strategic framework of the CAP. Theoretical implications: The study presents a first mapping of the actors providing innovation support services in Italy, showing that all the functions described by the literature are provided during interactive innovation processes by different actors and even in the absence of a specific mandate. Originality/Values: The study validates and broadens, through a large-scale analysis, the knowledge about innovation support service providers and the functions they perform.