Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Deterministic Transmission Services Scheduling in UAV Cluster Network

Considering the uncertainty of the forwarding delay of deterministic transmission services in UAV (Unmanned Aerial Vehicle) networks caused by the dynamic change of wireless channel in UAV cluster network, a deterministic transmission services scheduling scheme for UAV cluster networks is proposed. Firstly, a channel fading loss model in a highly dynamic UAV cluster network is established; Secondly, based on this model, the network is partitioned into several segments of stable network from the time domain; Finally, an optimization objective function model with minimum average jitter is constructed to ensure the deterministic transmission services scheduling in the UAV cluster network, and is solved by an optimization genetic algorithm. Simulation results show that, compared with other schemes, the average jitter of the proposed scheme is not higher than 60µs, which can effectively provide transmission jitter certainty for deterministic transmission.
Synthesis and antiplasmodial evaluation of novel mefloquine-based fumardiamides

Abstract The paper is focused on the synthesis and screening of the antiplasmodial activity of novel fumardiamides 5–10 with the mefloquine pharmacophore and a Michael acceptor motif. Multi-step reactions leading to the title compounds included two amide bond formations. The first amide bond was achieved by the reaction of (E)-ethyl 4-chloro-4-oxobut-2-enoate (1) and N1-(2,8-bis(trifluoromethyl)quinolin-4-yl) butane-1,4-diamine (2). The obtained ester 3 was hydrolyzed and gave acid 4, which then reacted with the selected halogenanilines in the presence of HATU/DIEA and formed products 5–10. Title compounds showed marked, dose dependent activity in vitro against hepatic stages of Plasmodium berghei. IC50 values of the most active compounds 5, 7 and 9 bearing 3-fluoro, 3-chloro and 3-trifluoromethyl substituents were 3.04–4.16 µmol L−1, respectively. On the other hand, the compounds exerted only weak activity against the erythrocytic stages of two P. falciparum strains (Pf3D7 and PfDd2) in vitro, with the exception of compound 5 (IC50 = 2.9 µmol L−1).
D2D Communications Strategy for Optimizing Energy Efficiency in Ultra-Dense Networks

The integration of Device-to-Device Communications (D2D) and millimeter wave band (mmW) within Ultra-Dense Networks (UDN) plays a crucial role in the context of the Fifth Generations (5G) networks, as it effectively addresses the substantial demands for high-volume traffic. Therefore, in UDN high-density indoor locations, internet service providers and end-users would deploy numerous Small Base Stations (SBSs) to meet the substantial User Equipment (UE) demands. The cumulative energy consumption of all SBSs is expected to be significant despite their tiny size and lower power requirements, resulting in notable environmental and economic consequences. Disabling the underutilized SBSs within current cellular networks presents an effective strategy for conserving energy without compromising the Quality of Service (QoS) experienced by UEs. This paper primarily concerns Energy Efficiency (EE) in UDN with many SBSs. It proposes a centralized awake/sleep control mechanism for SBSs in the mmW band to reduce network power consumption without compromising the network efficacy of UE. D2D communication assisted in optimizing the user's association with the grouping UEs approach. It can minimize the limited communication range in mmW bands and reduce SBSs in awake mode. Based on the evaluation results presented, with the proposed UEs association optimization and SBS awake/sleep mechanism, effectiveness in EE improvements and network efficiency can be guaranteed.
Identification and Regulation of Hypoxia-Tolerant and Germination-Related Genes in Rice

In direct seeding, hypoxia is a major stress faced by rice plants. Therefore, dissecting the response mechanism of rice to hypoxia stress and the molecular regulatory network is critical to the development of hypoxia-tolerant rice varieties and direct seeding of rice. This review summarizes the morphological, physiological, and ecological changes in rice under hypoxia stress, the discovery of hypoxia-tolerant and germination-related genes/QTLs, and the latest research on candidate genes, and explores the linkage of hypoxia tolerance genes and their distribution in indica and japonica rice through population variance analysis and haplotype network analysis. Among the candidate genes, OsMAP1 is a typical gene located on the MAPK cascade reaction for indica–japonica divergence; MHZ6 is involved in both the MAPK signaling and phytohormone transduction pathway. MHZ6 has three major haplotypes and one rare haplotype, with Hap3 being dominated by indica rice varieties, and promotes internode elongation in deep-water rice by activating the SD1 gene. OsAmy3D and Adh1 have similar indica–japonica varietal differentiation, and are mainly present in indica varieties. There are three high-frequency haplotypes of OsTPP7, namely Hap1 (n = 1109), Hap2 (n = 1349), and Hap3 (n = 217); Hap2 is more frequent in japonica, and the genetic background of OsTPP7 was derived from the japonica rice subpopulation. Further artificial selection, natural domestication, and other means to identify more resistance mechanisms of this gene may facilitate future research to breed superior rice cultivars. Finally, this study discusses the application of rice hypoxia-tolerant germplasm in future breeding research.
Osteogenic and Biomedical Prospects of Hafnium and Its Compounds: A Scoping Review

The direct engagement of hafnium (Hf) in biological processes or its critical function in living things is not well understood as of now. Unlike key elements like oxygen, carbon, hydrogen, and nitrogen, which are necessary for life, Hf is not known to have any biological activities or functions. It is essential to acknowledge that scientific research is ongoing and that new findings may have been made. This systematic review aimed to aggregate and analyze the studies that discuss biomedical applications of Hf metal. This systematic review was conducted following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement. The following search strategy was used: two independent researchers conducted electronic searches in databases including PubMed, Embase, Cochrane Database of Systematic Reviews, and Google Scholar. The search was conducted up to August 2023 using the Medical Subject Headings (MeSH) terms “transition elements,” “hafnium,” and “biomedical research. ” Boolean operators “AND” and “OR” were used to refine the search. Electronic databases, along with hand searches, identified a total of 38 studies. The various database searches resulted in a total of 38 studies, of which 12 were excluded as duplicates, and five were unavailable for full-text data. The remaining 21 full-text articles were then assessed for their eligibility based on the inclusion and exclusion criteria, and finally, a total of 12 studies were included in the present systematic review. Among the 12 chosen studies, six were on cancer-related targeted radiotherapy or chemoradiotherapy, five were on bone or apatite-forming capabilities, and one was on the treatment of inflammatory bowel disease. The common outcome measures included cell proliferation, osteoblast formation, radiotherapy intensification, and immunotherapy. This review outlines an overall picture of the biomedical uses of Hf metal, a transition element, as a potent biomaterial. In conclusion, this transition element, Hf, has some promising scope in the fields of biomedicine, with a special focus in terms of cancer radiotherapy and osteogenic capabilities.
Deep Reinforcement Learning-based Predictive Maintenance Task Offloading and Resource Allocation

In recent years, the growth of Industrial Internet of Things has enabled automated data collection and analysis, leading to the re-emerging of predictive maintenance. However, IIoT devices have insufficient computing power for computationally intensive maintenance tasks. Consequently, Mobile Edge Computing (MEC) has been introduced to relieve the computation burden. This paper focuses on predictive maintenance task offloading and resource allocation optimization based on Deep Deterministic Policy Gradient (DDPG). An Artificial Bee Colony algorithm (ABC) is further incorporated to speed up the learning convergence. The proposed algorithm minimizes the overall maintenance cost in generating the optimal task offloading and resource allocation solution. Experimental results show that this scheme outperforms the baseline scheme in reducing the total system maintenance cost.
Perioperative Blindness in Spine Surgery: A Scoping Literature Review

Perioperative vision loss (POVL) is a devastating surgical complication that impacts both the recovery from surgery and quality of life, most commonly occurring after spine surgery. With rates of spine surgery dramatically increasing, the prevalence of POVL will increase proportionately. This scoping review aims to aggregate the literature pertinent to POVL in spine surgery and consolidate recommendations and preventative measures to reduce the risk of POVL. There are several causes of POVL, and the main contribution following spine surgery is ischemic optic neuropathy (ION). Vision loss often manifests immediately following surgery and is irreversible and severe. Diffusion weighted imaging has recently surfaced as a diagnostic tool to identify ION. There are no effective treatments; therefore, risk stratification for counseling and prevention are vital. Patients undergoing prone surgery of long duration and/or with significant expected blood loss are at greatest risk. Future research is necessary to develop effective treatments.
Prevalence of IoT Protocols in Telescopeand Honeypot Measurements

With the arrival of the Internet of Things (IoT), more devices appear online with default credentials or lacking proper security protocols. Consequently, we have seen a rise of powerful DDoS attacks originating from IoT devices in the last years. In most cases the devices were infected by bot malware through the telnet protocol. This has lead to several honeypot studies on telnet-based attacks. However, IoT installations also involve other protocols, for example for Machine-to-Machine communication. Those protocols often provide by default only little security. In this paper, we present a measurement study on attacks against or based on those protocols. To this end, we use data obtained from a /15 network telescope and three honey-pots with 15 IPv4 addresses. We find that telnet-based malware is still widely used and that infected devices are employed not only for DDoS attacks but also for crypto-currency mining. We also see, although at a much lesser frequency, that attackers are looking for IoT-specific services using MQTT, CoAP, UPnP, and HNAP, and that they target vulnerabilities of routers and cameras with HTTP.
Regulatory frameworks involved in the floral induction, formation and developmental programming of woody horticultural plants: a case study on blueberries

Flowering represents a crucial stage in the life cycles of plants. Ensuring strong and consistent flowering is vital for maintaining crop production amidst the challenges presented by climate change. In this review, we summarized key recent efforts aimed at unraveling the complexities of plant flowering through genetic, genomic, physiological, and biochemical studies in woody species, with a special focus on the genetic control of floral initiation and activation in woody horticultural species. Key topics covered in the review include major flowering pathway genes in deciduous woody plants, regulation of the phase transition from juvenile to adult stage, the roles of CONSTANS (CO) and CO-like gene and FLOWERING LOCUS T genes in flower induction, the floral regulatory role of GA-DELLA pathway, and the multifunctional roles of MADS-box genes in flowering and dormancy release triggered by chilling. Based on our own research work in blueberries, we highlighted the central roles played by two key flowering pathway genes, FLOWERING LOCUS T and SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1, which regulate floral initiation and activation (dormancy release), respectively. Collectively, our survey shows both the conserved and diverse aspects of the flowering pathway in annual and woody plants, providing insights into the potential molecular mechanisms governing woody plants. This paves the way for enhancing the resilience and productivity of fruit-bearing crops in the face of changing climatic conditions, all through the perspective of genetic interventions.
Electronic Voting System Using Blockchain Technology

The election of a country holds significant importance, as it plays a pivotal role in the advancement and growth of a nation. Ensuring the safety and integrity of an election is a concern closely tied to national security. Electoral mechanisms in Sri Lanka and many other regions remain conventional and vulnerable. Despite the evolution of democracy, voting processes continue to rely on manual methods using paper ballots and pens. Although e-voting machines have emerged, these systems carry a substantial risk of manipulation and are susceptible to issues such as malfunction and breakdown, as well as unauthorized breaches. Addressing these challenges can be achieved by embracing cutting-edge technology, specifically blockchain, to establish a voting procedure that is both dependable and transparent. This paper presents a voting platform built upon the principles of blockchain with the aim of digitalizing the voting process while ensuring dependability, security, and user-friendly functionality. The ballots submitted by voters are recorded on the blockchain, making them immutable and safeguarded against possible manipulation. The system's capacity can be expanded to suit the needs of any election. Participants can access the system using biometric authentication methods, which will make the voting process more secure. Therefore, this system illustrates the potential of utilizing blockchain technology, including distributed systems, Ethereum, and smart contracts, to provide a secure, trustworthy, and transparent voting system for contemporary elections.
Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review

Literature reviews on artificial intelligence (AI) have focused on the different applications of AI in higher education, the AI techniques used, and the benefits/risks of the use of AI. One of the greatest potentials of AI is to personalize higher education to the needs of students and offer timely feedback. This could benefit students with disabilities tremendously if their needs are also considered in the development of new AI educational technologies (EdTech). However, current reviews have failed to address the perspective of students with disabilities, which prompts ethical concerns. For instance, AI could treat people with disabilities as outliers in the data and end up discriminating against them. For that reason, this systematic literature review raises the following two questions: To what extent are ethical concerns considered in articles presenting AI applications assessing students (with disabilities) in higher education? What are the potential risks of using AI that assess students with disabilities in higher education? This scoping review highlights the lack of ethical reflection on AI technologies and an absence of discussion and inclusion of people with disabilities. Moreover, it identifies eight risks associated with the use of AI EdTech for students with disabilities. The review concludes with suggestions on how to mitigate these potential risks. Specifically, it advocates for increased attention to ethics within the field, the involvement of people with disabilities in research and development, as well as careful adoption of AI EdTech in higher education.
The Role of miRNAs to Detect Progression, Stratify, and Predict Relevant Clinical Outcomes in Bladder Cancer

Bladder cancer (BC) is one of the most common types of cancer worldwide, with significant differences in survival depending on the degree of muscle and surrounding tissue invasion. For this reason, the timely detection and monitoring of the disease are important. Surveillance cystoscopy is an invasive, costly, and uncomfortable procedure to monitor BC, raising the need for new, less invasive alternatives. In this scenario, microRNAs (miRNAs) represent attractive prognostic tools given their role as gene regulators in different biological processes, tissue expression, and their ease of evaluation in liquid samples. In cancer, miRNA expression is dynamically modified depending on the tumor type and cancer staging, making them potential biomarkers. This review describes the most recent studies in the last five years exploring the utility of miRNA-based strategies to monitor progression, stratify, and predict relevant clinical outcomes of bladder cancer. Several studies have shown that multimarker miRNA models can better predict overall survival, recurrence, and progression in BC patients than traditional strategies, especially when combining miRNA expression with clinicopathological variables. Future studies should focus on validating their use in different cohorts and liquid samples.
Apple fruit surface temperature prediction using weather data-driven machine learning models

Heat stress to maturing apple fruit is a key concern to tree fruit growers in the Pacific Northwest region of the United States and around the globe. Localized weather-based fruit surface temperature (FST) prediction, a key indicator of fruit stress, can help in planning better mitigation strategies and ultimately reduce crop losses. Therefore, this study evaluated localized weather (solar radiation, temperature, relative humidity, dew point, and wind speed) and fruit size data driven multiple linear regression (MLR) and Long Short-Term Memory (LSTM) models for predicting apple FST. The models were trained on either the localized in-orchard or open field weather station data collected in the 2022 field season and validated against the actual FST of ‘Honeycrisp’ apple. The MLR model was able to predict FST with an average root mean square error (RMSE) of 2. $1^{\circ}\mathrm{C}$ using the in-orchard weather and fruit size dataset as inputs. The LSTM model prediction average RMSE for the same dataset was 2. $3^{\circ}\mathrm{C}$. Using open field weather data, the RMSE was 2. $69^{\circ}\mathrm{C}$ and 2. $25^{\circ}\mathrm{C}$ for the LSTM and MLR models, respectively. Additionally, both in-orchard and open-field trained models outperformed the existing energy balance FST prediction approach on the same dataset. Overall, these findings can be helpful to growers for real-time and reliable FST monitoring using localized as well as publicly available weather data inputs.
Foreign Direct Investment and Economic Growth

In this work we study the effect of FDI on the economies of the southern Mediterranean countries. Taking into account the positive effects and starting from the establishment of a simultaneous equation model applied to 8 countries, we have tried to demonstrate the mechanisms through which FDI affects economic growth. The estimation of our model shows that exports and, to a lesser extent, human capital and domestic investment are the most promising factors in the creation of positive effects. However, these results are weakly motivating to generate positive growth or at least to reduce the negative effects of FDI.
Energy Efficiency Optimization in Cell-Free Massive MIMO With Normalized Conjugate Beamforming

In this paper, we propose a power allocation scheme for optimizing the energy efficiency (EE) in cell-free multiple-input and multiple-output (MIMO) downlink systems using normalized conjugate beamforming (NCB) precoding. Specifically, the NCB precoding-based EE optimization problem under short-term power constraints is firstly formulated. To address its non-convex nature, a power allocation scheme is proposed, which employs the sequential convex approximation (SCA) to transform it into a series of accessible second-order cone programming (SOCP) problems, thereby enabling an iterative solution process. Moreover, an access point (AP) selection scheme based on K-means++ is also designed, which further improves EE by eliminating the pilot contamination. Simulation result confirms that the proposed power allocation scheme converges rapidly, and attains an improved EE compared to the existing NCB precoding-based power allocation schemes.
Upper ocean thermal and flow fields at 0 deg, 28 deg W (Atlantic) and 0 deg, 140 deg W (Pacific) during 1983-1985

Moored current and temperature measurements were recorded simultaneously for 2 years (August 1983 to July 1985) at six or seven depths between 10 and 250 m on the equator at 28 deg W in the Atlantic and at 140 deg W in the Pacific. The mean depth of the 20-C isotherm, which was representative of thermocline displacements, was identical at both sites. Substantially different annual cycles of the thermal and flow fields represent an enigma. The annual variation of the 20-C isotherm was much less at 140 deg W than at 28 deg W.
Indoor Positioning Based on Frequency Fading Characteristics of Wideband Signals

As location-based services in indoor environment have burgeoned, there has been a corresponding surge in research on indoor positioning technologies. Current methods, like Channel State Information (CSI) fingerprinting, impose rigorous requirements on receiving devices, mandating specialized CSI acquisition hardware and intricate data collection software. This paper introduces a novel indoor positioning algorithm leveraging the frequency fading characteristics (FFC) of wideband signals, implementable using a single-antenna apparatus for direct data capture. The initial step involves processing raw data through the Fast Fourier Transform (FFT) to obtain the FFC within the frequency domain. Subsequently, the frequency spectrum of the signals of interest was extracted. A convolutional auto-encoder (CAE) network is then harnessed to distill features from the spectrogram, primarily aiming to curtail data size, mitigate computational demands, and economize memory consumption. In the concluding step, a Residual Network (ResNet) model is adopted to categorize these features, establishing a correlation between the features and reference points. Experimental validation in a conference room setup yielded a positioning precision of 0.67m. The accuracy and robustness of the proposed algorithm outshine conventional indoor positioning approaches, including Received Signal Strength (RSS) and Angle of Arrival (AOA) positioning.
Dynamic Spectrum Access for UAV Networks: A Stackelberg Game Approach

Unmanned aerial vehicles (UAVs) have been deployed to help with poor-quality of on-ground communication systems or finishing rescue missions. However, due to the limited availability of spectrum, there is no dedicated bandwidth for UAV communication, necessitating the borrowing of spectrum from Base Stations (BSs). In this paper, we design an algorithm to help BS make the decision of how to allocate the bandwidth to connected UAVs, how to price the bandwidth and try to maximize their profits. Specifically, we first introduce a UAV-assisted communication model and consider the cost incurred by the UAVs. Then we design the Stackelberg game based bandwidth allocation model including benefit function formulations for both the BSs and the UAVs. By analyzing the interactions between the BS and the UAVs, we obtain the optimal bandwidth trading strategy. The simulation results indicate the equilibrium containing bandwidth and price of the proposed game is the best choice which could maximize the profit functions of the BS and the UAVs. At the same time, the communication speed could be guaranteed.