Limitations In Terms Research Articles

Spectacular International Labor Law: Ambush Counter-Marketing In the Spotlight of Qatar’s 2022 FIFA World Cup

AbstractOne year ago, the Qatar World Cup was in full swing, and Qatar was omnipresent in our public and private spheres. For many, the Qatar 2022 World Cup will forever be intimately connected with the plight of migrant workers. This Article dives into the confluence of spectacle, counter-marketing, international—labor and human rights—law, and local reforms, which came together in the long decade which followed FIFA’s fateful decision in December 2010 to give the 2022 World Cup to Qatar. It starts by situating the FIFA World Cup 2022 within Qatar’s drive for soft power and nation branding, before turning to re-counting how the 2022 World Cup was “ambushed” in the name of Qatar’s migrant workers and their rights, putting the issue on the global agenda and triggering the involvement of the ILO. Thereafter, the Article discusses the effects of this ambush counter-marketing by engaging with the labor reforms introduced by the Qatari government, while highlighting their limits in terms of scope and implementation. The Article concludes with a general discussion on the blind spots and shortcomings of the turn to counter-marketing as a strategy to vindicate international human rights or labor rights.

Collaborative Learning in General Graphs With Limited Memorization: Complexity, Learnability, and Reliability

We consider a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$</tex-math> </inline-formula> -armed bandit problem in general graphs where agents are arbitrarily connected and each of them has limited memorizing capabilities and communication bandwidth. The goal is to let each of the agents eventually learn the best arm. Although recent studies show the power of collaboration among the agents in improving the efficacy of learning, it is assumed in these studies that the communication graph should be complete or well-structured, whereas such an assumption is not always valid in practice. Furthermore, limited memorization and communication bandwidth also restrict the collaborations of the agents, since the agents memorize and communicate very few experiences. Additionally, an agent may be corrupted to share falsified experiences to its peers, while the resource limit in terms of memorization and communication may considerably restrict the reliability of the learning process. To address the above issues, we propose a three-staged collaborative learning algorithm. In each step, the agents share their latest experiences with each other through light-weight random walks in a general communication graph, and then make decisions on which arms to pull according to the recommendations received from their peers. The agents finally update their adoptions (i.e., preferences to the arms) based on the reward obtained by pulling the arms. Our theoretical analysis shows that, when there are a sufficient number of agents participating in the collaborative learning process, all the agents eventually learn the best arm with high probability, even with limited memorizing capabilities and light-weight communications. We also reveal in our theoretical analysis the upper bound on the number of corrupted agents our algorithm can tolerate. The efficacy of our proposed three-staged collaborative learning algorithm is finally verified by extensive experiments on both synthetic and real datasets.

Ultrasound-Based Technologies for the Evaluation of Testicles in the Dog: Keystones and Breakthroughs.

Ultrasonography is a valuable diagnostic tool extensively used in the andrology of human and domestic animals, including dogs. This review aims to provide an overview of various technologies based on ultrasound, from the basic B-Mode ultrasonography to the more recent advancements, such as contrast-enhanced ultrasonography (CEUS) and ultrasound elastography (UEl), all of which are utilized in the evaluation of canine testicles. The review outlines the principles behind each of these technologies and discusses their application in assessing normal and abnormal testicular conditions. B-mode canine testicular ultrasonography primarily focuses on detecting focal lesions but has limitations in terms of objectivity. Other technologies, including Doppler ultrasonography, B-Flow, and CEUS, allow for the characterization of vascular patterns, which could be further measured using specific applications like spectral Doppler or quantitative CEUS. Additionally, ultrasound elastography enables the assessment of parenchyma stiffness both qualitatively and quantitatively. These ultrasound-based technologies play a crucial role in andrology by providing valuable information for evaluating testicular function and integrity, aiding in the identification of pathological conditions that may impact the health and quality of life of male dogs.

An advanced laboratorial measurement technique of scour topography based on the fusion method for 3D reconstruction

Local scour is a phenomenon that occurs around water-blocking structures such as bridge piers, pile foundations, and pipelines, which threatens seriously the safety of structures. The laboratory test based on wave and flow tanks is an effective way investigating the local scour around piles. The measurement of scour depth and topography is an important basis for this type of experimental analysis, which can help the researchers to perform a quantitative analysis on the scour mechanism. The conventional measurement methods in scouring experiments have limitations in terms of operational efficiency, measurement range, and cost. Therefore, an advanced experimental measurement technique based on the RGB-D sensor and the 3D reconstruction algorithm was proposed in this work to provide the full topography information around target zone after scour. Experiments applying this 3D reconstruction method were conducted for the practical scour tests. The impact of water on the application of this technique was discussed as well. The results showed that the new technique has good accuracy in scour topography description and scour depth measurement. This work provides a new measure technique for laboratory scour tests. The proposed method has a broad application prospect in measurements for such experimental scenes.

Determination of performance characteristics using FEA-analytical for outer rotor BLDC motor

This paper presents an analytical approach to determine the performance characteristics of an outer rotor Brushless DC (BLDC) motor using finite element analysis (FEA). An accurate performance characteristic is a crucial deliverable during the early motor design process in order to minimize the cost of change while delivering a feasible design to the application. There are several popular methods to determine motor performance, including template-based software as well as FEA. However, these two methods have limitations in terms of geometry and time. Therefore, this paper proposes that the limitations can be addressed by using a combination of FEA and mathematical equations. First, the optimum motor design is determined using an FEA analytical approach. Second, the torque constant &lt;em&gt;K&lt;/em&gt;&lt;sub&gt;T&lt;/sub&gt;, is derived from the FEA results and followed by performance curve generation using the mathematical equation of speed, current, and output power in terms of torque function. Finally, the outcome results are verified against the motor requirements, including an assessment of maximum speed, stall current, and maximum output power. In conclusion, this research introduces a methodology for determining full curve motor performance prior to prototype fabrication. The leverage benefit could be gained through robust design and low-cost development.

Tennessee Academy of ScienceDistinguished Secondary School Science Teacher Award 2022

Tennessee Academy of ScienceDistinguished Secondary School Science Teacher Award 2022

Improving the quality of Cambodian higher education: Key challenges and suggestions

Higher education is one of the main drivers of economic development. Due to its significant roles, many countries, including Cambodia, have invested in developing their higher education subsector. For over two decades since 1997, Cambodia has experienced a high increase in the number of higher education institutions (HEIs). Despite the rapid institutional expansion, there are many issues impeding the quality of Cambodian higher education. Drawing on secondary sources and the authors’ extensive experience and knowledge of the subsector, this article tries to investigate and explain some of the factors affecting the quality of higher education in Cambodia. The article focuses on several issues, including political interference, fragmented governance structures, ineffective ministerial policy implementation, fragmented institutional management, limited quality of teaching personnel, low academic salaries, business-oriented higher education, and issues concerning curriculum and academic programs. The article offers some suggestions to address these challenges. Key suggestions include (a) limiting political interference in appointing top university leaders, (b) setting up effective and independent higher education quality assurance bodies, (c) creating a term limit for top university leadership roles, (d) establishing a meritocracy-based policy for university leader appointments, (e) making academic salaries comparable to those of other similar professions, (f) developing policies on minimum tuition fees and student loan programs, and (g) introducing mechanisms to improve curricula and promote enrollments in science, technology, engineering, and mathematics (STEM) majors. The article concludes with suggestions for future research.

Overview of concept designs and results of the New Acoustic Insulation Meta-Material for Aerospace (NAIMMTA) project

Reducing aircraft cabin noise to improve the comfort of passengers is an important and challenging issue in aeronautics. Relatively uncomfortable high noise levels of around 80 to 90 dBA with strong low frequency range components (below 500 Hz) and a tonal character, predominantly related to the engine fan during take-off and approach, are deemed critical. The conventional acoustic materials seem to have reached their physical limits in terms of sound proofing, and therefore non-conventional solutions, such as metamaterials, are sought for their promising performance such as, a significant noise attenuation rate (dB/m), and the capability to be tuned at tonal or narrow band frequencies. An international collaborative project was created to develop novel technologies aiming at improving the existing noise control systems by obtaining an additional 5 dB noise reduction in the low frequency range (100 to 400 Hz) without deteriorating the thermal insulation. Moreover, the proposed solutions were expected to be tunable with respect to tonal noise (bandwidth of 5 Hz) or narrowband noise (bandwidth of 40 Hz). A major design and integration constraint imposed that the metamaterial had to be embedded in the current existing insulation blanket and add a maximum of 20% additional mass in comparison to the conventional insulation. This paper presents an overview of the various solutions developed from numerical simulations and novel manufacturing procedures development and optimization to performance characterization and validations.

An implicit Z-bus based sequential power flow algorithm for VSC AC/DC systems

AC/DC power flow algorithms have been proposed and developed as essential analysis tools for the Voltage Source Converter (VSC) based AC/DC hybrid grid. However, prevailing AC/DC power flow algorithms, e.g., the Newton-Raphson based algorithm, suffer from limitations in terms of convergence, multiple solutions, and high computational complexity. To address these issues, this paper presents a novel sequential AC/DC power flow algorithm based on the implicit Z-bus (IZB) method. We firstly provide a general AC/DC power flow model in real vector space, which takes into account the interface data, internal connection and various control modes of VSC stations. Then, simple and compact IZB iterative mappings in real vector space are derived for the AC solver and DC solver. A sequential algorithmic architecture and data exchange mechanisms are designed considering losses calculation, capacity limits and control strategy switching. The impacts of tolerance settings, initial value selections and load levels on the convergence and accuracy of the algorithm are theoretically analysed. Notably, rigorous convergence analyses substantiate the superior attributes of the proposed algorithm over existing models, guaranteeing convergence to high-voltage solutions (feasible and stable solutions). Numerical tests are performed to compare the proposed algorithm with existing works in terms of accuracy, efficiency, scalability and reliability.

E-BOOK MEDIA TRENDS IN THE LEARNING PROCESS AT SCHOOL

E-books are digital teaching materials that can be integrated with various multimedia, including images, videos, animations or manipulative media. For the next e-book development process, data and information on what kind of e-book and what media have been integrated are needed. For this reason, a Systemic Literature Review (SLR) was conducted which examined what and how e-books were researched. This research uses PRISMA (Preferred Reporting Items for Systemic and Meta-Analysis) guidelines, by searching for scopus indexed articles in the 2019-2023 range involving a total of 193 articles. After the screening process, 14 articles were obtained which were then analyzed in accordance with the Research Question (RQ) that had been formulated. From the results of the study based on two RQs, it was found that (1) the most research design that examines e-books is design/development research; (2) the least integrated aspect in e-books is animation/manipulative media. These results have limitations in terms of the scope of data sources and the depth of study on the material discussed. So this research provides opportunities for further researchers both in development design and SLR.

Improved Feature Selection Algorithm for Intrusion Detection Using Data Mining Approach

With the rapid growth of Internet applications, there are more and more intrusions into network systems. In this case, it is necessary to provide security for the network through effective intrusion detection and prevention methods. This can mainly be achieved by creating effective interruption detection systems using efficient algorithms that can identify abnormal activities in network traffic and safeguard network resources from unlawful attack by interlopers. Although many interruption recognition frameworks have been proposed before, existing network intrusion detection has limitations in terms of accuracy and detection time. To overcome these shortcomings, In this paper, we propose a new intrusion detection system by developing a new intelligent feature selection algorithm based on conditional random fields (CRF) to optimize the number of features. Furthermore, algorithms based on existing hierarchical methods (LA) In this paper, we propose another interrupt recognition framework, fostering a book. Compared with the existing methods, the interruption identification framework provides high precision and achieves the efficiency of attack detection. The main advantages of this system are reduced detection time, improved classification accuracy and lower false positive rate.

One-Pot Era-CRISPR/Cas12a System for Rapid and Sensitive Detection of Multiple NPM1 Mutations

Nucleophosmin 1 (NPM1) gene c.863_864 4-bp insertion mutations occur in 30% of adult acute myeloid leukemia (AML) patients, which is associated with a unique pathological phenotype and good prognostic significance. The detection of NPM1 mutations is important for risk stratification, treatment guidance, and minimal residual disease monitoring in AML. However, the current detection methods have their limitations in terms of equipment requirements, sensitivity, turnaround time, price, etc. Moreover, simultaneous detection of multiple NPM1 mutation types is challenging. Here, we developed a one-pot ERA-CRISPR/Cas12a-based assay in which only one guide crRNA is needed to detect hundreds of NPM1 gene variants, covering over 97% of NPM1-mutated AML cases. By optimizing multiple parameters, this assay finally achieved a sensitivity of as low as 0.01% within 30 min detection under constant 39℃, without wild-type related cross signal. In clinical detection, 21 NPM1 mutation positive cases from 70 AML patient were successfully screened out using our method, including two cases not detected by Sanger sequencing. This method is one-step, rapid, sensitive, specific, inexpensive, and without the need of Sequencer or temperature cycler. It holds the potential to assist clinicians in point of care testing.

METHODOLOGICAL DEVELOPMENT OF PERSONALIZED ORTHOPEDIC SPLINTS THROUGH LOW-COST ADDITIVE MANUFACTURING

Orthopedic splints are widely used to immobilize parts of the body during the recovery from bone injuries, surgeries, or post-traumatic recovery. In the medical field, there are two main types of splints: the rigid and disposable ones made of plaster, and the non-disposable but detachable ones, typically made up of metallic and polymeric components. Rigid splints are economical and can be quickly applied, but they have limitations in terms of weight, ventilation, and comfort, and also require medical staff intervention for their removal. On the other hand, detachable splints offer greater comfort and breathability, but they are more expensive and their fit to the patient is less accurate. This work presents the development of personalized orthopedic splints, lighter and more breathable, at a reasonable cost and production time. To achieve this, a methodology is proposed that includes an economical scanning of the area to be immobilized, an accessible and efficient digital design, and its production using MEX additive manufacturing technology, meeting the necessary mechanical requirements for immobilization and stiffness, based on finite element simulations. This proposal is presented as an alternative to conventional splints, offering improved performance thanks to affordable and low-cost additive manufacturing technologies.

The Electoral Dynamics of Capital Punishment Commutations

AbstractWe explore electoral explanations for U.S. governors’ willingness to commute death sentences in their state. Across descriptive tests and pre-registered regression specifications, we find little evidence that election timing or term limits affect either the probability of commuting death sentences or the proportion of such sentences governors might commute. However, we do find evidence that governors are more likely to commute sentences – and commute sentences for a higher proportion of defendants – during the “lame duck” period after their successor’s election but before their inauguration.

Advancements in scaffold for treating ligament injuries; in vitro evaluation.

Tendon/ligament (T/L) injuries are a worldwide health problem that affects millions of people annually. Due to the characteristics of tendons, the natural rehabilitation of their injuries is a very complex and lengthy process. Surgical treatment of a T/L injury frequently necessitates using autologous or allogeneic grafts or synthetic materials. Nonetheless, these alternatives have limitations in terms of mechanical properties and histocompatibility, and they do not permit the restoration of the original biological function of the tissue, which can negatively impact the patient's quality of life. It is crucial to find biological materials that possess the necessary properties for the successful surgical treatment of tissues and organs. In recent years, the in vitro regeneration of tissues and organs from stem cells has emerged as a promising approach for preparing autologous tissue and organs, and cell culture scaffolds play a critical role in this process. However, the biological traits and serviceability of different materials used for cell culture scaffolds vary significantly, which can impact the properties of the cultured tissues. Therefore, this review aims to analyze the differences in the biological properties and suitability of various materials based on scaffold characteristics such as cell compatibility, degradability, textile technologies, fiber arrangement, pore size, and porosity. This comprehensive analysis provides valuable insights to aid in the selection of appropriate scaffolds for in vitro tissue and organ culture.

“What have YOU done in the past few years?” Deaf BSL users’ experiences of caring for people with dementia during COVID-19

PurposeDeaf people, who are British Sign Language (BSL) users, are, at times, carers for their parent or spouse when they have dementia. This can be a challenging role for the wider population, but if the common language in service provision is not one you share, then this care can be impacted by the lack of formal structures that support the Deaf caring role. This study aims to explore the experiences of Deaf carers supporting people with dementia, in an unpaid role, during the COVID-19 pandemic to understand more about their potential support preferences.Design/methodology/approachOnline interviews with seven Deaf carers sharing their experiences of supporting their family member with dementia happened during the first year of the COVID-19 pandemic. Interpretive phenomenological analysis was used to qualitatively examine each Deaf carers’ stories.FindingsFindings included Deaf carers’ lack of access to information, surrendering their autonomy to hearing family members to communicate with health-care professionals, with overarching isolation within local communities and online. Findings show a widespread infrastructural failure to meet the linguistic and specific support needs of this minority population.Research limitations/implicationsSeven Deaf carers took part in this study. This means the findings may not be generalisable.Practical implicationsHealth-care professionals and social care structures need to develop robust clear communication pathways for Deaf carers to receive the support needed to provide effective care.Social implicationsIf health-care professionals undertake training about Deaf people and BSL, that will mean Deaf carers will receive effective support to be able to care for a person living with dementia. This means that their Deaf identity will be recognised within the mainstream and the care provided will be much more effective for the person with dementia.Originality/valueTo the best of the authors’ knowledge, these are the first interviews undertaken with Deaf carers of people with dementia during the COVID-19 pandemic and its subsequent lockdowns. Understanding everyday barriers and social care limitations in terms of language access will enable Deaf carers to assert their rights and for care professionals to adapt their communication to match the individuals involved in the care package.

Asymmetry stagger array structure ultra-wideband vibration harvester integrating magnetically coupled nonlinear effects

Traditional energy harvesters often have limitations in terms of bandwidth and power output, resulting in poor performance. This study reports a multi-strategy ultra-wideband energy harvesting device that utilizes an asymmetry, stagger array, magnetic coupling, and nonlinearity strategies to achieve high power output over an ultra-wideband frequency range without the need for external power input. The energy harvesting device consists of a base, two elastic beams, and two sets of asymmetric palm-shaped piezoelectric cantilever beam structures, each with a magnet attached at the tip. By reasonably arranging the palm-shaped mechanism and magnets, a magnetic coupling effect is introduced to achieve high power density output at non-resonant frequencies. Numerical analysis is conducted to evaluate the performance of the proposed structure, assess the influence of structural parameters, and establish a dynamic model to analyze the energy harvesting device. Experimental results demonstrate that the structure achieves a maximum power output of 60.49 mW at 19.9 Hz and 0.5 g, with a peak power density of approximately 8.065 × 103 W/m3. Within an ultra-wideband frequency range that spans 6 Hz to 64.2 Hz, the energy harvester maintains an output voltage which is more than 5 V. Furthermore, temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the energy harvesting device, eliminating the need for lithium batteries and ensuring stable signal transmission. The device can be utilized for condition monitoring in any unstable vibration environment, contributing to the realization of distributed monitoring in the Internet of Things (IoT).

MetaAc4C: A multi-module deep learning framework for accurate prediction of N4-acetylcytidine sites based on pre-trained bidirectional encoder representation and generative adversarial networks

MotivationN4-acetylcytidine (ac4C) is a highly conserved RNA modification that plays a crucial role in various biological processes. Accurately identifying ac4C sites is of paramount importance for gaining a deeper understanding of their regulatory mechanisms. Nevertheless, the existing experimental techniques for ac4C site identification are characterized by limitations in terms of cost-effectiveness, while the performance of current computational methods in accurately identifying ac4C sites requires further enhancement. ResultsIn this paper, we present MetaAc4C, an advanced deep learning model that leverages pre-trained bidirectional encoder representations from transformers (BERT). The model is based on a bi-directional long short-term memory network (BLSTM) architecture, incorporating attention mechanism and residual connection. To address the issue of data imbalance, we adapt generative adversarial networks to generate synthetic feature samples. On the independent test set, MetaAc4C surpasses the current state-of-the-art ac4C prediction model, exhibiting improvements in terms of ACC, MCC, and AUROC by 2.36%, 4.76%, and 3.11%, respectively, on the unbalanced dataset. When evaluated on the balanced dataset, MetaAc4C achieves improvements in ACC, MCC, and AUROC by 2.6%, 5.11%, and 1.01%, respectively. Notably, our approach of utilizing WGAN-GP augmented training RNA samples demonstrates even superior performance compared to the SMOTE oversampling method.

Lamb waves evaluation in CFRP plates with laser shock wave technique

Lamb waves, which are guided waves propagating along the surface of a thin plate structure, have been investigated to remotely perform nondestructive testing for aircraft or infrastructures made of carbon fiber reinforced plastic (CFRP) plates. CFRP plates are prone to internal damage such as delamination, which does not occur in metallic materials. Although nondestructive methods such as X-ray, flash thermography, etc. have been investigated in several studies, they may have some disadvantages such as risks of radiation exposure or limitation in terms of size of the structures to be tested for the former, and heat diffusion for the latter. Laser thermoelasticity can generate Lamb waves remotely and nondestructively. However, the generated Lamb waves have a small amplitude; therefore, hundreds of measurements must be averaged to achieve a sufficient signal-to-noise ratio. For these reasons, it has not yet been demonstrated as possible to detect damage in large CFRP structures within a short time using nondestructive, remote-controlled methods. As a first step towards performing nondestructive tests in CFRP plates, this paper demonstrates the generation of A0 mode Lamb waves in the experimental plate samples using laser-induced plasma (LIP) shock waves. The validity of the proposed system is verified by comparing the phase velocities of Lamb waves in the CFRP plates with three different layups obtained with the proposed system, with the theoretical values obtained analytically. This system can generate vibrations in CFRP plates in frequency ranges of a few hundred kHz and allow to determine the phase velocity of the experimental Lamb waves up to approximately 100 kHz.

Intelligent UAV planning for task-offloading with limited buffer and multiple computing servers

Dynamically moving Unmanned Aerial Vehicles (UAVs) have emerged as an effective means to significantly enhance the flexibility and transmission performance of mobile edge computing (MEC). However, in practical scenarios, UAVs often face limitations in terms of data storage capacity and computational power. In this paper, a UAV-enabled MEC network with multiple users and multiple edge computing servers is proposed, where the UAV is equipped with limited-size buffers. An optimization problem is formulated to jointly optimize UAV flight trajectories, offload server pairings, task offload ratios, and UAV transmit power to minimize transmission delay, computation delay, and system energy consumption. To tackle the intractable non-convex optimization issue, an intelligent optimization algorithm based on Deep Dueling Double Q-Network (D3QN)-Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed, which is able to efficiently determine the optimal solution. Simulation results demonstrate that our proposed intelligent algorithm exhibits good convergence and achieves a favorable balance between delay and energy consumption.