Miracle-dz/UniFAR-FTUs huggingface dataset:
An Overview of Mobility Management Mechanisms and the Related Challenges in 5G Networks and Beyond

Ensuring a seamless connection with various types of mobile user equipment (UE) items is one of the more significant challenges facing different generations of wireless systems. However, enabling the high-band spectrum – such as the millimeter wave (mmWave) band – is also one of the important factors of 5G networks, as it enables them to deal with increasing demand and ensures high coverage. Therefore, the deployment of new (small) cells with a short range and operating within the mmWave band is required in order to assist the macro cells which are responsible for operating long-range radio connections. The deployment of small cells results in a new network structure, known as heterogeneous networks (HetNets). As a result, the number of passthrough cells using the handover (HO) process will be dramatically increased. Mobility management (MM) in such a massive network will become crucial, especially when it comes to mobile users traveling at very high speeds. Current MM solutions will be ineffective, as they will not be able to provide the required reliability, ﬂexibility, and scalability.Thus, smart algorithms and techniques are required in future networks. Also, machine learning (ML) techniques are perfectly capable of supporting the latest 5G technologies that are expected to deliver high data rates to upcoming use cases and services, such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). This paper aims to review the MM approaches used in 5G HetNets and describes the deployment of AI mechanisms and techniques in ″connected mode″ MM schemes. Furthermore, this paper addresses the related challenges and suggests potential solutions for 5G networks and beyond.
Post-Ischemic Permeability of the Blood–Brain Barrier to Amyloid and Platelets as a Factor in the Maturation of Alzheimer’s Disease-Type Brain Neurodegeneration

The aim of this review is to present evidence of the impact of ischemic changes in the blood–brain barrier on the maturation of post-ischemic brain neurodegeneration with features of Alzheimer’s disease. Understanding the processes involved in the permeability of the post-ischemic blood–brain barrier during recirculation will provide clinically relevant knowledge regarding the neuropathological changes that ultimately lead to dementia of the Alzheimer’s disease type. In this review, we try to distinguish between primary and secondary neuropathological processes during and after ischemia. Therefore, we can observe two hit stages that contribute to Alzheimer’s disease development. The onset of ischemic brain pathology includes primary ischemic neuronal damage and death followed by the ischemic injury of the blood–brain barrier with serum leakage of amyloid into the brain tissue, leading to increased ischemic neuronal susceptibility to amyloid neurotoxicity, culminating in the formation of amyloid plaques and ending in full-blown dementia of the Alzheimer’s disease type.
The Effect of Brand Activation on Brand Awareness

This study aimed to determine how the effect of brand activation on brand awareness at Teras Komuji. In this study, the independent variable (X) is brand activation, consisting of several sub-dimensions: identity, employee, product and service, and communication. The dependent variable (Y) is brand awareness. The type of research used is descriptive and verification with an explanatory survey method and a cross-sectional study approach. The data in the study used primary data from a sample of 220 respondents, namely customers who had visited and had activities at Teras Komuji. The data analysis technique used was multiple regression using the assistance of a statistical calculation program. The results showed that, together and separately, the brand activation variables significantly affected brand awareness at the Teras Komuji
Register Allocation Using Control Trees

Register allocation is like trying to fit blocks in a box. There are two distinct problems: first, the volume of the blocks may be too large for the box, and second, the shapes of the blocks may make the box difficult to pack. No matter how good your packing technique is, you cannot succeed if the volume of blocks is too large. On the other hand, that the volume of blocks is acceptable does not guarantee that the box can be packed. In the analogy to register allocation, each live range associated with a candidate (object to be allocated) is a block. Other algorithms view blocks as having uniform density. Our blocks have {\em holes}, regions in which the candidate''s live range is reference-free. We prune portions of the program where the number of live candidates is greater than the number of registers (the {\em register pressure} is too high) by storing the value in memory on entry to the region and reloading it on exit. It is always possible to prune enough that the volume of the candidates does not exceed the volume of the box. In theory, once the volume problem is solved, packing is easy if one has a chainsaw: when something doesn''t fit, cut it in two and try again. In practice, the problem is difficult to solve well because splitting has a cost: {\tt move} instructions must be inserted between the split regions. It is important to select not only the proper candidate to split but also the proper location of the split to facilitate packing the remaining blocks and also to minimize the costs of the {\tt move} instructions. Both the pruning and the splitting use the {\em control tree} to take into account the program structure in determining costs. Since the algorithm uses the control tree to guide the pruning and splitting decisions, the algorithm is called {\em control-tree register allocation} algorithm.
A Cryogenic 12 GHz Frequency Doubler With Temperature Compensation for Trapped-Ion Quantum Computer

This brief presents a frequency doubler (FD) for a microwave-control system intended for an <inline-formula> <tex-math notation="LaTeX">$^{171}\text {Yb}^{+}$ </tex-math></inline-formula> ion-trap-based quantum computer. The circuit is optimized for frequency multiplication from 11GHz to 13 GHz with a ≥29 dBc fundamental- and third harmonic rejection over the entire frequency range and peak HRR1/HRR3 of 37 dBc/ 39 dBc. This enables the microwave frequency generation to drive the hyperfine transitions in the electronic ground state of an <inline-formula> <tex-math notation="LaTeX">$^{171}\text {Yb}^{+}$ </tex-math></inline-formula> ion at 12.6 GHz. Cryogenic measurements of the FD down to 4.5 K enable circuit functionality verification for the intended low-temperature operation. Additionally, insights into the cryogenic temperature effects of the employed BiCMOS technology are obtained and used to derive a biasing control methodology for constant performance in the temperature range of 300K to 4.5K. The proposed circuit is silicon-proven and fabricated in a <inline-formula> <tex-math notation="LaTeX">$\mathrm {0.13~ \mu \text {m} }$ </tex-math></inline-formula> SiGe BiCMOS process, consuming a core area of only 0.034 mm2.
Reinforcement Learning for Intelligent Healthcare Systems: A Review of Challenges, Applications, and Open Research Issues

The rise of chronic disease patients and the pandemic pose immediate threats to healthcare expenditure and mortality rates. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, leveraging the recent advances of Internet of Things and smart sensors. Meanwhile, reinforcement learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for distinct applications and services. Thus, this article presents a comprehensive survey of the recent models and techniques of RL that have been developed/used for supporting Intelligent-healthcare (I-health) systems. It can guide the readers to deeply understand the state-of-the-art regarding the use of RL in the context of I-health. Specifically, we first present an overview of the I-health systems’ challenges, architecture, and how RL can benefit these systems. We then review the background and mathematical modeling of different RL, deep RL (DRL), and multiagent RL models. We highlight important guidelines on how to select the appropriate RL model for a given problem, and provide quantitative comparisons, showing the results of deploying key RL models in two scenarios that can be followed in monitoring applications. After that, we conduct an in-depth literature review on RL’s applications in I-health systems, covering edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and future research directions to enhance RL’s success in I-health systems, which opens the door for exploring some interesting and unsolved problems.
Designing co-curriculum experiential learning practice in permaculture for studying science classics

General education (GE) plays a critical role in university education. It embraces a broad intellectual pursuit through the study of and reflection on liberal arts, science knowledge, and real-world situations. The inspiring yet challenging common-core GE foundation course UGFN1000 In Dialogue with Nature, which requires students to study science classics, was launched at CUHK in 2012. To enhance students’ learning of the selected science classics, such as Origin of Species and Silent Spring, co-curriculum experiential learning practice in permaculture was designed and employed. This paper illustrates the design of the pre-session preparation, practice sessions, and post-session reflection. It provides a practical and insightful reference to educators interested in developing experiential learning through permaculture in GE and beyond.
An Eternal Domination Problem in Grids

A dynamic domination problem in graphs is considered in which an infinite sequence of attacks occur at vertices with mobile guards; the guard at the attacked vertex is required to vacate the vertex by moving to a neighboring vertex with no guard. Other guards are allowed to move at the same time, and before and after each attack, the vertices containing guards must form a dominating set of the graph. The minimum number of guards that can defend the graph against such an arbitrary sequence of attacks is called the m-eviction number of the graph. In this paper, the m-eviction number is determined exactly for small grids and upper bounds are given for all m ≥ n ≥ 8.
Biofilms and mechanics: a review of experimental techniques and findings

Biofilms are developmentally-dynamic communities of sessile microbes that adhere to each other and, often, to other structures in their environment. The cohesive mechanical forces binding microbes to each other confer mechanical and structural stability on the biofilm and give rise to biofilm viscoelasticity. The adhesive mechanical forces binding microbes to other structures in their environment can promote biofilm initiation and mechanosensing that leads to changes in biological activity. Thus, physical mechanics is intrinsic to characteristics that distinguish biofilms from free-swimming or free-floating microbes in liquid culture. However, very little is known about the specifics of what mechanical traits characterize different types of biofilms at different stages of development. Even less is known about how mechanical inputs impact microbial biology and how microbes can adjust their mechanical coupling to, and interaction with, their environment. These knowledge gaps arise, in part, from the challenges associated with experimental measurements of microbial and biofilm biomechanics. Here, we review extant experimental techniques and their most-salient findings to date. At the end of this review we indicate areas where significant advances in the state-of-the art are heading.
Evaluation of Generic Fertiliser as an Alternative Inorganic Nitrogen Source for Ethanolic Glucose Fermentation by Saccharomyces cerevisiae

In the studies and production of bioethanol, the preferred fermenting yeast (Saccharomyces cerevisiae) is usually cultured in liquid broth that contains yeast extract and peptone. However, the use of these laboratory and scientific grade chemicals is costly, making them impractical for mass bioethanol production. Therefore, this study was conducted to evaluate the feasibility of glucose ethanolic fermentation by S. cerevisiae using generic fertiliser formulations to provide inorganic nitrogen, phosphorus, potassium and trace elements (NPK-TE). Fermentation media of different generic fertiliser strength at 0.5X, 1.0X and 2.0X Fertiliser Nitrogen Equivalents (FNE), as compared to the conventional Yeast Extract-Peptone (YEP) medium as control, was used as fermentation broth during the ethanolic fermentation of glucose. Based on the results, S. cerevisiae cultured in YEP broth produced the highest cell concentration for both wet (21.93 g/L) and dry cells (3.87 g/L), with rapid increment observed in the first 72 h of fermentation. By the end of the fermentation period, lactic acid (3.14 g/L) and acetic acid (0.96 g/L) levels were recorded to be the lowest in YEP medium while their concentration (lactic acid, 8.08 g/L) and (acetic acid, 2.67 g/L) were highest in 2.0X FNE fertiliser medium. Results indicated that the best theoretical ethanol yield (TEY) among the fertiliser media was achieved when fermentation was performed in the 0.5X FNE fertiliser medium, with a TEY of 86.18%. TEY yields were 78.68% and 51.54% in broth with 1.0X and 2.0X FNE, respectively. In general, all three fertiliser media supported ethanolic fermentation of glucose, with the 0.5X FNE fertiliser broth showing a yield that is significantly close to the conventional YEP medium, as seen in the statistical analysis. Similarities in other fermentation profiles such as acetic acid, lactic acid, and biomass production, as well as glucose utilisation, between the results from the YEP samples and samples from the fertiliser broths (at 0.5X and 1.0X FNE) have also shown that generic fertiliser has the potential to be used as an alternative medium to replace the conventional YEP to produce ethanol at a lower cost.
Time-invariant radon transform by generalized Fourier slice theorem

Time-invariant Radon transforms play an important role in many fields of imaging sciences, whereby a function is transformed linearly by integrating it along specific paths, e.g. straight lines, parabolas, etc. In the case of linear Radon transform, the Fourier slice theorem establishes a simple analytic relationship between the 2-D Fourier representation of the function and the 1-D Fourier representation of its Radon transform. However, the theorem can not be utilized for computing the Radon integral along paths other than straight lines. We generalize the Fourier slice theorem to make it applicable to general time-invariant Radon transforms. Specifically, we derive an analytic expression that connects the 1-D Fourier coefficients of the function to the 2-D Fourier coefficients of its general Radon transform. For discrete data, the model coefficients are defined over the data coefficients on non-Cartesian points. It is shown numerically that a simple linear interpolation provide satisfactory results and in this case implementations of both the inverse operator and its adjoint are fast in the sense that they run in \begin{document}$O(N \;\text{log}\; N)$\end{document} flops, where \begin{document}$N$\end{document} is the maximum number of samples in the data space or model space. These two canonical operators are utilized for efficient implementation of the sparse Radon transform via the split Bregman iterative method. We provide numerical examples showing high-performance of this method for noise attenuation and wavefield separation in seismic data.
Deployable Parallelogram Mechanism and Constrained Bendable Segment for Flexible Robot Toward Transoral Procedures

A deployable parallelogram mechanism and a constrained bendable segment are proposed for a flexible robot to address the transoral robotic procedure’s demands. The deployable mechanism based on a parallelogram mechanism increases the kinematic flexibility of manipulators. The bendable segment with a controllable constraint allows the robot to have a variable bending radius, expanded workspace, and increased distal dexterities. This paper presents the robotic system, including the manipulators, the bendable segment, the actuation parts, and the master device. The kinematic analysis and the workspace characterization of the manipulator and the bendable segment are conducted. The benchtop experiments, including deploying force, load capacity, and performance tests, have validated the deployable mechanism and the bendable segment.
A Phenomenology Approach to Rethinking Coastal Community Vulnerability Toward Sea-Level Rise

Due to its proximity to the ocean and the danger it poses, such as sea level rise, coastal floods, tsunamis, and many other natural disasters, the coastal community has been long regarded as one of the most vulnerable communities. However, coastal communities that have prohibited these areas for a long period have grown accustomed to its threats, especially coastal floods, and have adopted localized survival and mitigation strategies. These events have tested the coastal communities' ability to adapt using limited resources, knowledge, and power. Using a phenomenology approach, this paper aims to illustrate the vulnerability exposed to the coastal community in Muara Baru, Penjaringan District, city of North Jakarta, Indonesia, using a geographic and exposure criteria to determine participants. Data gathered from in-depth interviews with head villagers and 25 residents of Muara Baru along with field observations to capture their lived experience as a community in terms of adaptation strategies and how they cope with the rising sea-level and coastal flood. Results show that contrary to previous conceptions, Muara Baru’s coastal community are no longer vulnerable to sea-level rise and this research highlights the rising concern for social risks they experience. By focusing on this factor rather on the disadvantage due to their geographical location, policy designs can be tailored to support the enhancement and well-being of the community.
Contextualizing the Neural Vulnerabilities Model of Obesity

In recent years, investigators have focused on neural vulnerability factors that increase the risk of unhealthy weight gain, which has provided a useful organizing structure for obesity neuroscience research. However, this framework, and much of the research it has informed, has given limited attention to contextual factors that may interact with key vulnerabilities to impact eating behaviors and weight gain. To fill this gap, we propose a Contextualized Neural Vulnerabilities Model of Obesity, extending the existing theory to more intentionally incorporate contextual factors that are hypothesized to interact with neural vulnerabilities in shaping eating behaviors and weight trajectories. We begin by providing an overview of the Neural Vulnerabilities Model of Obesity, and briefly review supporting evidence. Next, we suggest opportunities to add contextual considerations to the model, including incorporating environmental and developmental context, emphasizing how contextual factors may interact with neural vulnerabilities to impact eating and weight. We then synthesize earlier models and new extensions to describe a Contextualized Neural Vulnerabilities Model of Obesity with three interacting components—food reward sensitivity, top-down regulation, and environmental factors—all within a developmental framework that highlights adolescence as a key period. Finally, we propose critical research questions arising from the framework, as well as opportunities to inform novel interventions.
Non-formal Faculty Development. Conceptual considerations and implementation in practice

Non-formal faculty development offerings might reach those lecturers who, for a variety of reasons, are reluctant to attend formal faculty development programs. It can also provide an opportunity to supplement informal workplace learning with evidence-informed knowledge. A centre for teaching and learning at a German university has implemented non-formal learning opportunities through an open learning portal. Lehrblick.de offers lecturers from all departments access to a bilingual, accessible service that provides them with concise, quality-assured content on innovative topics in teaching and learning in higher education, regardless of time or location. With this approach, it reaches nearly 10 times as many lecturers every month as the university's formal faculty development program.This contribution describes theoretical considerations of non-formal faculty development, its design and implementation in faculty development practice.
Edible indigenous fruits in Zimbabwe: A review on the post-harvest handling, processing, and commercial value

Abstract Indigenous fruits are considered to be part of the diet mostly by people in rural communities of Zimbabwe, but they are still being underutilized. They can be eaten raw or processed into different products dating long back. There are various indigenous food post-harvest handling methods that have been used in Zimbabwean rural communities in order to maintain food security and add variety to their diets. Post-harvest handling of some commonly consumed indigenous fruits has been reported in the literature. Systematic review of literature method was used. Results showed that traditional processing and preservation methods of only 22 indigenous fruits had been documented out of 82 wild fruits. However, the information on post-harvest handling of most indigenous fruits is limited and incomplete. The review aimed to interrogate existing knowledge in literature on post-harvest handling of indigenous fruits in Zimbabwe and identify gaps for further research. This review attempts to contribute to this effort by evaluating existing literature. There is a need for indigenous fruit surveys to be conducted so as to get more information on indigenous fruits.
Associations between Stress, Anxiety, Depression and Sleep Quality among Healthcare Students

Objectives: Stress, anxiety, and depression among students have many negative health consequences and may predispose students to poor sleep quality; therefore, this research aimed to investigate the perceived stress, anxiety, and depression and their relation to the level of sleep quality among healthcare students. Methods: A cross-sectional study using a validated survey was conducted among Saudi healthcare students from different regions during the period from 26 September 2022 to 30 October 2022. The Pittsburgh Sleep Quality Index (PSQI) was utilized to assess sleep quality. PSPP Statistical Analysis Software version 1.2. 0 was used for all statistical analyses. Results: 701 respondents participated in this research; the response rate was 73.8%. About 60% of the study sample was female, and the average age was 20.9 years. 80.3% of students have poor sleep quality; the participants’ mean sleeping hours per night was 6.81 ± 1.88 h. About three-quarters of students (72.9%) perceive themselves as stressed. A significant positive correlation was observed between sleep quality and perceived stress (r-value = 0.363), anxiety (r-value = 0.387), and depression (r-value = 0.347). Poor sleep quality was more likely among those with stress than those without stress (AOR = 1.79; 95% CI 1.07, 2.99) and two times more likely among students with cases of anxiety than those without anxiety (AOR = 2.07; 95% CI 1.10, 3.94). Conclusions: Our study highlights the high prevalence of poor sleep quality, anxiety, depression, and stress among healthcare students in Saudi Arabia. Students’ reported stress, anxiety, and residence region were associated with poor sleep quality. These results imply the necessity of regular screening and appropriate intervention for sleep problems, stressors, and anxiety among healthcare students.