Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic variation in mineral profiles, yield contributing agronomic traits, and foliage yield of stem amaranth

The study was evaluated to estimate mineral profiles and find out selection criteria for high foliage yielding cultivars by assessing the magnitude of genetic variability, heritability, genetic advance, association, and contribution of characters of sixteen stem amaranth genotypes in a randomized block design with three replications. Based on high heritability and high GA and GAPM and close values between Vg vs. Vp and GCV vs. PCV, all the traits could be selected to improve the foliage yield of stem amaranth. A significant and desirable positive correlation was observed for Fe, Ca, Mn, K, and leaves plant-1 both at genotypic and phenotypic levels. These five traits could be selected for improving the foliage yield of stem amaranth as these traits exhibited less influence on the environment. The insignificant genotypic correlation values were observed between mineral vs. mineral and mineral vs. foliage yield and yield contributing agronomic traits, which indicate that selection for high mineral content might be possible without compromising yield loss. A high to moderate direct effect along with a significant correlation was found in Fe, K, Ca, and Mn. Fe, K, Ca, and Mn had the most significant contribution to foliage yield of stem amaranth as these traits exhibited considerable positive direct effects and significant correlation coefficients on foliage yield. The accessions AS7, AS4, AS5, AS14, and AS16 had high foliage yields containing moderate to high mineral profiles. These five accessions could be selected as high-yielding cultivars. Selection could be made based on Fe, K, Ca, and Mn content to improve stem amaranth. The accessions AS7, AS4, AS5, AS14, and AS16 could be used as high foliage yielding and mineral profiles enrich cultivars.
Distributed Sensor Clustering Using Artificial Neural Network With Local Information

Internet of Things (IoT) has facilitated the emergence of various applications which require deploying a large number of sensors over a wide geographical area. For efficient communications, sensors with similar data within a vicinity are grouped into clusters. Compared to centralized sensor clustering, a distributed scheme is more scalable, can avoid traffic congestion, and does not suffer from single-point failure. However, due to a lack of global information, distributed sensor clustering may create more clusters than a centralized scheme. In this article, we propose to use an artificial neural network (ANN) as a tool to summarize the experience of a centralized clustering scheme in the presence of global information. Then, this experience becomes a learned knowledge to be transferred to distributed decision makers, which may subsequently approximate the centralized scheme in making the clustering decision even with access to only local information. To achieve the desired performance, we derive some secondary local information from original local information, and use it as input to ANNs to compensate for the loss of global information. Evaluation results show that it is feasible in achieving at a distributed decision maker, a same clustering solution as the centralized scheme, with about 5% clustering error probability.
Scoping Review of Yoga in Schools: Mental Health and Cognitive Outcomes in Both Neurotypical and Neurodiverse Youth Populations

Yoga is used widely as a therapeutic tool for physical and mental well-being. The mind-body activity could be an inclusive and cost-effective intervention used within schools to help tackle the mental health crisis in youth populations. To date, research has focused on mainly neurotypical youth populations. However, greater acknowledgement of the impact for neurodiverse youth populations is warranted. Therefore, the aim of this scoping review is to understand what is known about the relationships between yoga in schools and mental health and cognition in neurotypical and neurodiverse youth populations. Methods: This review followed O’Malley and Arskey’s methodological framework. A comprehensive database search using fundamental keywords and index terms was conducted. Screening was carried out in CovidenceTM software. Results: Substantial evidence to support the use of school-based yoga programmes for the improvement of anxiety, self-concept, resilience, depression, self-esteem, subjective and psychological well-being, executive function, inhibition, working memory, attention and academic performance in neurotypical populations was found. Evidence to support school-based yoga programmes in neurodiverse populations with improvements in self-concept, subjective well-being, executive function, academic performance and attention was also found. Conclusions: The findings support the provision of yoga in schools to improve mental health and cognition whilst also creating clear pathways for future research and school-based yoga intervention development.
A 184-μW Error-Tolerant Real-Time Hand Gesture Recognition System With Hybrid Tiny Classifiers Utilizing Edge CNN

This article proposes a low-power real-time hand gesture recognition (HGR) system with high recognition accuracy for smart edge devices. This design balances accuracy and power consumption by utilizing computation-efficient hybrid classifiers assisted with a majority voting scheme. By combining the recognition results of consecutive frames, the HGR system shows improved immunity to misclassification. In addition, the compressed input data before high-level processing dramatically reduce the on- chip memory and computational load. The proposed Edge-convolutional neural network (CNN) core with interactable processing engines reduces the memory accessing and the feature register toggling rate by 27% and 50%, respectively. The sequence analyzer based on majority voting improves the static and dynamic gesture recognition accuracy by ~7% and ~8% only with 9.4% hardware overhead. The test chip was fabricated in 65-nm CMOS technology, occupying the area of 1 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 1.5 mm2. It consumes the lowest power of <inline-formula> <tex-math notation="LaTeX">$184~\mu \text{W}$ </tex-math></inline-formula> at 25 MHz and 0.6 V. The proposed HGR system can recognize six static gestures and 24 dynamic hand gestures with an average accuracy of 87.25%–95% and 85.4%–94.9%, respectively.
Anthocyanins and Carotenoids Characterization in Flowers and Leaves of Cyclamen Genotypes Linked with Bioactivities Using Multivariate Analysis Techniques

The present study was carried out to evaluate and compare in vitro antioxidant (2,2-diphenyl-1-picrylhydrazyl (DPPH), Trolox equivalent antioxidant capacity (TEAC), and ferric reducing antioxidant power (FRAP)), antimicrobial, anticancer activities, and the individual carotenoids and anthocyanins content of methanol extracts of the Cyclamen genotypes: Persian cyclamen accessions (Cyclamen persicum Mill.), sowbread (C. mirabile Hildebr.), and ivy-leaved cyclamen (C. hederifolium Mill.) aerial parts. The HPLC-PDA analysis revealed the presence of five individual carotenoids (i.e., neoxanthin, violaxanthin, lutein, β-carotene, and cis-β-carotene) as the main compounds in Cyclamen leaves, and the presence of seven individual anthocycanins (i.e., cyanidin 3,5-di-O-glucoside, peonidin-rutinoside, peonidin 3,5-di-O-glucoside, peonidin 3-O-glucoside, malvidin 3-O-glucoside, malvidin 3,5-di-O-glucoside, and malvidin-rutinoside) in Cyclamen flowers reported, hereby, for the first time. The highest phenolic content was found in the leaves of LC6, C. mirabile (46.32 ± 0.14 mg/g gallic acid equivalents [GAE]), and in the flowers of C. persicum Merengue Magenta (FC15) (58.63 ± 0.17 mg/g GAE), whereas the highest flavonoid content was reported in C. persicum Halios Falbala leaves, namely LC9 (54.90 ± 0.27 mg/g quercetin equivalents [QE]) and in flowers of C. persicum Victora (FC2) (77.87 ± 0.25 mg/g QE). The highest antioxidant activity in DPPH and FRAP assays was reported in C. persicum Dark Violet (LC1) and Victoria (LC2), whereas C. mirabile (LC6) had the highest activity in the TEAC assay. In flowers, high antioxidant activities in DPPH and TEAC were noticed in C. persicum Superserie Red (FC7) and Dark Violet (FC1), respectively, and Halios Falbala (FC9) exhibited the highest activity in the TEAC assay. Additionally, FC9 exhibited the highest antibacterial activity in almost all tested bacteria compared with the leaves extracts. Furthermore, the highest in vitro citotoxicity in MDA-MB-231 cells was noticed in C. hederifolium LC18 (56.71–69.35%) and FC18 (40.07–41.43%), with a lower effect against BJ cells demonstrating selective toxicity. The above findings, highlight the potential use of the Cyclamen flower and leaf extracts as significant anticancer agents along with their antioxidant and antimicrobial properties.
Resonance frequency-based protection scheme for ultra-high-voltage direct-current transmission lines

Direct-current (DC) line faults are a major issue in ultra-high-voltage direct-current systems due to the complex and wicked environment. Thus, reliably detecting and isolating the DC line faults are not a trivial engineering task. This study proposes a DC line protection scheme based on resonance frequency. The scheme mainly consists of a fault discrimination criterion and a faulty pole selection criterion. The fault discrimination criterion is based on the phase of the resonance frequency impedance, and it is deployed to differentiate the faults on the protected DC line from the external faults. The faulty pole selection criterion is based on the ratio of the resonance frequency currents, and it is employed to select the faulty pole. The sensitivity analysis demonstrates that the performance of the proposed protection is promising. The simulations carried out in PSCAD/EMTDC under various conditions verify the feasibility and validity of the proposed protection scheme.
Patterns of zoonotic diseases and associated socio-economic factors in tanzania: A scoping review

Zoonotic diseases (ZDs) are important contributors of infectious disease burden especially in developing nations. In Tanzania, several factors have been associated with the distribution of ZDs among different populations. This review aimed at describing such a pattern together with their associated socio-economic factors. The search for relevant articles was carried in PubMed/MedLine with additional hand searched articles through Google and Google-Scholar. We identified a total of 1,087 relevant articles, 27 of which met our inclusion criteria. Our findings showed that the prevalence of Brucellosis, Leptospirosis, Q Fever, Rift Valley Fever, Cysticercosis, Echinococcosis, Schistosomiasis, Toxoplasmosis, Fascioliasis and Cryptosporidiosis were 0.6 - 48.4%, 10 - 33.9%, 5 - 20.3%, 4.5 - 5.2%, 2.7 - 16.7%, 11.3%, 15.8 - 63.91%, 57.7%, 21% and 4.3% respectively, depending on geographical locations. On other hand, levels of education, occupation, residence and ethnicity were associated with increased risks of ZDs in Tanzania. This review reinforces the need for more resilient surveillance and monitoring systems that can offer quality data for evidence-based policing. Likewise, it underscores the neglected burdens of most ZDs in Tanzania.
Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

Reducing energy consumption is a pressing issue in low-power machine-type communication (MTC) networks. In this regard, the Wake-up Signal (WuS) technology, which aims to minimize the energy consumed by the radio interface of the machine-type devices (MTDs), stands as a promising solution. However, state-of-the-art WuS mechanisms use static operational parameters, so they cannot efficiently adapt to the system dynamics. To overcome this, we design a simple but efficient neural network to predict MTC traffic patterns and configure WuS accordingly. Our proposed forecasting WuS (FWuS) leverages an accurate long short-term memory (LSTM)-based traffic prediction that allows extending the sleep time of MTDs by avoiding frequent page monitoring occasions in the idle state. Simulation results show the effectiveness of our approach. The traffic prediction errors are shown to be below 4%, being a false-alarm and miss-detection probabilities, respectively, below 8.8% and 1.3%. In terms of energy consumption reduction, FWuS can outperform the best benchmark mechanism by up to 32%. Finally, we certify the ability of FWuS to dynamically adapt to traffic density changes, promoting low-power MTC scalability.
A Self-Healing Routing Strategy Based on Ant Colony Optimization for Vehicular Ad Hoc Networks

In recent years, in-vehicle applications based on vehicular ad hoc networks (VANETs) have been continuously expanded. Many applications not only focus on delay and effective forwarding rate but also pay more attention to routing path multiplexing and throughput. However, in VANETs, it is challenging to establish real-time and robust multihop forwarding paths due to volatile topological information, disconnected network, churn rate, etc. In order to adapt to the new development trend of VANETs, a self-healing routing strategy (SR) with the ant colony optimization (ACO) is proposed in this article. SR introduces the ACO algorithm to establish routing paths to ensure connectivity and immediacy. The routing-build-ability (RBA) is defined to measure the forwarding capability of a vehicle. The RBA is derived from the delay and packet delivery ratio (PDR) using the fuzzy logic system, which can reduce the computational complexity. To reduce the overhead of path reconstruction performed due to path disconnection, in-road-repairing and intersection-repairing methods are proposed in this article, which prolong the duration of the optimal path and improve throughput. The simulation results and mathematical analyses demonstrate that the feasible SR can reduce the delay by 30%, shorten the time overhead to one sixth, promote the routing duration by three times, and enhance the throughput by three times.
Time synchronization protocol for the KLJN secure key exchange scheme

The information theoretically secure Kirchhoff-law-Johnson-noise (KLJN) key exchange scheme, similarly to quantum key distribution (QKD), is also potentially vulnerable against clock attacks, where Eve takes over the control of clock synchronization in the channel. This short note aims to introduce a time synchronization protocol scheme for Alice and Bob, which is resistant against arbitrary time delay attacks, both symmetric and asymmetric ones. We propose and explore various ways of clock synchronization for the KLJN system and propose an ultimate protocol that preserves time and hardware integrity under arbitrary attacks.
Secondary fracture prevention: global approaches

Abstract Osteoporotic fractures, a major cause of disability and associated medical care expenditures, are expected to continue to increase worldwide. Adults who sustain primary osteoporotic fractures are more likely to sustain additional subsequent fractures, but few receive interventions to prevent those secondary fractures. Fracture liaison service models have been developed to systematically streamline preventative care for patients who have sustained a primary fragility fracture, proving cost-effective and efficacious. These programs have been developed in many countries, with varying degrees of success. There continues to be a need to better understand the various approaches to fracture liaison service programs in different regions enabling best practices to be adopted, successful approaches to be shared, and common obstacles to be addressed. The reports in this supplement address the status of fracture liaison service programs in different countries, focusing on national standards and guidelines, successes and barriers, and future directions. This work represents a collaborative effort of member societies of the International Orthopaedic Trauma Association (IOTA), an international association of orthopaedic societies dedicated to the promotion of musculoskeletal trauma care through advancements in patient care, research, and education. The expectation is that the information in these reports will aid efforts to address the growing need for fragility fracture programs.
Joint Task Offloading and Resource Allocation for Multihop Industrial Internet of Things

Task offloading in edge computing is important for the Industrial Internet of Things (IIoT) to implement computation-intensive applications in real time. However, achieving efficient task offloading in IIoT is very challenging due to the limited computing resources of IIoT devices, the coupling of computing and communication resources, and the unreliability in multihop wireless transmission. In this article, we construct a link model by considering the influence of unreliable links in multihop transmission to reveal the relationship between reliability and transmission delay. Then, a nonconvex optimization problem that minimizes task processing delay is formulated, and task offloading is decided by considering transmission path selection, bandwidth allocation, and computational resource allocation. To solve this problem, an algorithm based on the alternating direction method of multipliers (ADMM) is designed using auxiliary variables and reformulation linearization technology (RLT). The simulation results show that our proposed algorithm can fully utilize the computing power of the edge server and reduce the task processing delay. Compared with the centralized algorithms, the performance of the proposed scheme is only 1% worse, but the calculation time can be reduced by 40%.
Privacy-Preserving and Verifiable Cloud-Aided Disease Diagnosis and Prediction With Hyperplane Decision-Based Classifier

With the vigorous development and gradual maturity of machine learning (ML) technologies, the AI-assisted disease diagnosis and prediction (<inline-formula> <tex-math notation="LaTeX">$\mathcal {AADP}$ </tex-math></inline-formula>) system has been extensively studied and can be expected to be intensively deployed in the real world. However, as the scale of ML data increases exponentially, the training and application of ML models impose a great burden on resource-constrained terminals. Designing cloud/edge server-aided <inline-formula> <tex-math notation="LaTeX">$\mathcal {AADP}$ </tex-math></inline-formula> protocols is becoming a popular topic. Whereas, the sensitivity of ML data, the intellectual property of ML models, and the uncontrollability of servers bring great security challenges to this promising computing paradigm. In this article, we initialize a new four-party framework for the <inline-formula> <tex-math notation="LaTeX">$\mathcal {AADP}$ </tex-math></inline-formula> system which consists of users, third-party test institution, AI doctor, and cloud/edge server. With this framework, we design two high-efficiency and secure outsourcing <inline-formula> <tex-math notation="LaTeX">$\mathcal {AADP}$ </tex-math></inline-formula> protocols under two different security models. By comprehensively employing secure hash functions, Householder transformations, and random permutations, we realize the following design objectives: 1) user’s actual identification is invisible to the other parties; 2) user’s feature vector is blinded to the AI doctor and the server; 3) the ML model of the AI doctor is confidential to the server; 4) AI doctor can obtain decent computational savings compared with achieving the diagnosis task by itself; and 5) AI doctor can verify the server’s misbehaviors with a nonnegligible probability under the security model with a fully malicious server. We argue these claims with rigid theoretical proofs and corroborate them with extensive experimental analysis.
Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system

Abstract. The Los Alamos Sea Ice Model (CICE) is used by several Earth system models where sea ice boundary conditions are not necessary, given their global scope. However, regional and local implementations of sea ice models require boundary conditions describing the time changes of the sea ice and snow being exchanged across the boundaries of the model domain. The physical detail of these boundary conditions regarding, for example, the usage of different sea ice thickness categories or the vertical resolution of thermodynamic properties, must be considered when matching them with the requirements of the sea ice model. Available satellite products do not include all required data. Therefore, the most straightforward way of getting sea ice boundary conditions is from a larger-scale model. The main goal of our study is to describe and evaluate the implementation of time-varying sea ice boundaries in the CICE model using two regional coupled ocean–sea ice models, both covering a large part of the Barents Sea and areas around Svalbard: the Barents-2.5 km​​​​​​​, implemented at the Norwegian Meteorological Institute (MET), and the Svalbard 4 km (S4K) model, implemented at the Norwegian Polar Institute (NPI). We use the TOPAZ4 model and a Pan-Arctic 4 km resolution model (A4) to generate the boundary conditions for the sea ice and the ocean. The Barents-2.5 km model is MET's main forecasting model for ocean state and sea ice in the Barents Sea. The S4K model covers a similar domain but it is used mainly for research purposes. Obtained results show significant improvements in the performance of the Barents-2.5 km model after the implementation of the time-varying boundary conditions. The performance of the S4K model in terms of sea ice and snow thickness is comparable to that of the TOPAZ4 system but with more accurate results regarding the oceanic component because of using ocean boundary conditions from the A4 model. The implementation of time-varying boundary conditions described in this study is similar regardless of the CICE versions used in different models. The main challenge remains the handling of data from larger models before its usage as boundary conditions for regional/local sea ice models, since mismatches between available model products from the former and specific requirements of the latter are expected, implying case-specific approaches and different assumptions. Ideally, model setups should be as similar as possible to allow a smoother transition from larger to smaller domains.
Efficient Encapsulation and Controlled Release of N,N-Diethyl-3-methylbenzamide (DEET) from Oil-in-Water Emulsions Stabilized by Cationic Nanocellulose and Silica Nanoparticles

Oil-in-water emulsions containing N,N-diethyl-3-methylbenzamide (DEET) were developed aiming to extend the sustained release of the active compound, by using two oppositely charged nanomaterials, namely silica nanoparticles (SiNP) and cationic cellulose nanofibrils (CCNF), as stabilizers, and a mixture of food-grade nonionic surfactants to avoid precipitation by electrostatic aggregation. The formulations were stable for more than four months at room temperature, and strongly resistant to destabilization upon centrifugal and thermal stress. The results were correlated with the effect of SiNP on strengthening the network formation of CCNF in the aqueous phase by electrostatic interactions, which increased the viscosity of the external phase and, hence, emulsion stability. There was a significant size reduction of the internal oil phase containing DEET in the presence of CCNF, which was attributed to the increased viscosity in the external aqueous phase, as well as to interfacial stabilization. The combined action of CCNF and unmodified SiNP in the stabilization of the DEET-containing oil phase significantly decreased the release rate of the active compound, compared to non-emulsified DEET. Moreover, in the emulsions containing the CCNF/SiNP mixture there was a more sustained release for the period of 6 h, demonstrating the potential of these formulations for extended protection.