Miracle-dz/UniFAR-FTUs huggingface dataset:
Review on Recent Progress in Magnetic Nanoparticles: Synthesis, Characterization, and Diverse Applications

Diverse applications of nanoparticles (NPs) have revolutionized various sectors in society. In the recent decade, particularly magnetic nanoparticles (MNPs) have gained enormous interest owing to their applications in specialized areas such as medicine, cancer theranostics, biosensing, catalysis, agriculture, and the environment. Controlled surface engineering for the design of multi-functional MNPs is vital for achieving desired application. The MNPs have demonstrated great efficacy as thermoelectric materials, imaging agents, drug delivery vehicles, and biosensors. In the present review, first we have briefly discussed main synthetic methods of MNPs, followed by their characterizations and composition. Then we have discussed the potential applications of MNPs in different with representative examples. At the end, we gave an overview on the current challenges and future prospects of MNPs. This comprehensive review not only provides the mechanistic insight into the synthesis, functionalization, and application of MNPs but also outlines the limits and potential prospects.
Integration of HIV Prevention With Sexual and Reproductive Health Services: Evidence for Contraceptive Options and HIV Outcomes Study Experience of Integrating Oral Pre-exposure HIV Prophylaxis in Family Planning Services in Lusaka, Zambia

The WHO guideline on the integration of family planning (FP) and pre-exposure HIV prophylaxis (PrEP) to enhance the health of women and adolescent girls is reflected in the Zambia Consolidated Guidelines for Treatment and Prevention of HIV Infection, 2020. There is however a dearth of data on the integration of PrEP and FP in Zambia. We describe the integration of oral PrEP in FP services using the Evidence for Contraceptive Options and HIV Outcomes (ECHO) study experience at Kamwala District Health Center in Lusaka, Zambia. The provision of oral PrEP at Kamwala started in October 2017, lasting for ~11 months, and utilized the model where initial processes to offer PrEP were on-site followed by off-site referral to laboratory and PrEP provider services. The characteristics of 658 women who enrolled in ECHO at Kamwala are representative of women accessing FP services in Lusaka. About 644 of the enrollees were offered oral PrEP. The proportion of women accepting PrEP was low at 1.08% and the proportion of study visits at which PrEP was requested was also low at 0.57%. Those who accepted PrEP were above 20 years old, married, with at least primary education, sexual behavior, and risk comparable to decliners. The ECHO study experience indicates that the setup and integration of oral PrEP and FP services are feasible in the setting. However, uptake of PrEP was very low. Possible contributory factors were as follows: (1) timing of introduction of PrEP midway in the study, (2) PrEP being a new intervention, (3) challenges of autonomy of young women to include a daily pill into their lives and anticipated challenges to adherence because of fear of adverse events, (4) possible underdetermined risk due to use of an unvalidated risk assessment tool and assessment by health care provider vs. self-assessment, and (5) extra layer of challenges to negotiate due to needing for off-site referrals. Following these findings, we conclude that further research through demonstration projects of integration of oral PrEP and FP may provide solutions to low uptake. This information is critical for scaling up of integration HIV prevention services and sexual and reproductive health (SRH) services.
Neural Mechanism of Repeated Transcranial Magnetic Stimulation to Enhance Visual Working Memory in Elderly Individuals With Subjective Cognitive Decline

Visual working memory (VWM), the core process inherent to many advanced cognitive processes, deteriorates with age. Elderly individuals usually experience defects in the processing of VWM. The dorsolateral prefrontal cortex is a key structure for the top-down control of working memory processes. Many studies have shown that repeated transcranial magnetic stimulation (rTMS) improves VWM by modulating the excitability of neurons in the target cortical region, though the underlying neural mechanism has not been clarified. Therefore, this study sought to assess the characteristics of brain memory function post-rTMS targeting the left dorsolateral prefrontal cortex. The study stimulated the left dorsolateral prefrontal cortex in elderly individuals by performing a high-frequency rTMS protocol and evaluated behavioral performance using cognitive tasks and a VWM task. Based on the simultaneously recorded electroencephalogram signals, event-related potential and event-related spectral perturbation analysis techniques were used to investigate the variation characteristics of event-related potential components' (N2PC and CDA) amplitudes and neural oscillations in elderly individuals to elucidate the effect of high-frequency rTMS. The results found that rTMS enhanced VWM performance and significantly improved attention and executive function in elderly individuals with subjective cognitive decline. We therefore speculate that rTMS enhances VWM by increasing the N2PC and CDA amplitude, alongside increasing β oscillation activity. This would improve the attention and allocation of resources in elderly individuals such as to improve an individual's VWM.
The addition of FeOOH binds phosphate in organic matter-rich sediments

ABSTRACT Due to climate change and anthropogenic nutrients’ runoff into freshwater or shallow lakes, eutrophication caused by phosphorus (P) can be seen in the frequent occurrence of cyanobacterial blooms and excessive growth of macrophytes. Subsequently, decomposition of cyanobacterial bloom biomass (CBB) and macrophytes leads to massive autochthonous organic matter (OM) and creates hypoxia in bodies of water. In this study, we investigated the effects of OM and iron on phosphorus release from lake sediments under anaerobic conditions. As with CBB, the addition of cellulose also enhanced P release from sediments during microcosm experiments, while total phosphorus (TP) concentration in the overlying water displayed an inverse relationship to cellulose amendment, with high TP concentration (0.41 ± 0.07 mg L−1) observed in the treatment of less cellulose amendment (1 g of cellulose). In addition, P release from OM-rich sediments was effectively inhibited when amorphous FeOOH was added to the microcosms. P release was inhibited by 66–92% when the weight ratio between total Fe and total P in sediments varied from 18 to 60. Thus, iron treatment was useful to inhibit P release from OM-rich sediments, and could alleviate eutrophication problems.
The Impacts of Surgery and Intracerebral Electrodes in C57BL/6J Mouse Kainate Model of Epileptogenesis: Seizure Threshold, Proteomics, and Cytokine Profiles

Intracranial electroencephalography (EEG) is commonly used to study epileptogenesis and epilepsy in experimental models. Chronic gliosis and neurodegeneration at the injury site are known to be associated with surgically implanted electrodes in both humans and experimental models. Currently, however, there are no reports on the impact of intracerebral electrodes on proteins in the hippocampus and proinflammatory cytokines in the cerebral cortex and plasma in experimental models. We used an unbiased, label-free proteomics approach to identify the altered proteins in the hippocampus, and multiplex assay for cytokines in the cerebral cortex and plasma of C57BL/6J mice following bilateral surgical implantation of electrodes into the cerebral hemispheres. Seven days following surgery, a repeated low dose kainate (KA) regimen was followed to induce status epilepticus (SE). Surgical implantation of electrodes reduced the amount of KA necessary to induce SE by 50%, compared with mice without surgery. Tissues were harvested 7 days post-SE (i.e., 14 days post-surgery) and compared with vehicle-treated mice. Proteomic profiling showed more proteins (103, 6.8% of all proteins identified) with significantly changed expression (p < 0.01) driven by surgery than by KA treatment itself without surgery (27, 1.8% of all proteins identified). Further, electrode implantation approximately doubled the number of KA-induced changes in protein expression (55, 3.6% of all identified proteins). Further analysis revealed that intracerebral electrodes and KA altered the expression of proteins associated with epileptogenesis such as inflammation (C1q system), neurodegeneration (cystatin-C, galectin-1, cathepsin B, heat-shock protein 25), blood–brain barrier dysfunction (fibrinogen-α, serum albumin, α2 macroglobulin), and gliosis (vimentin, GFAP, filamin-A). The multiplex assay revealed a significant increase in key cytokines such as TNFα, IL-1β, IL-4, IL-5, IL-6, IL-10, IL12p70, IFN-γ, and KC/GRO in the cerebral cortex and some in the plasma in the surgery group. Overall, these findings demonstrate that surgical implantation of depth electrodes alters some of the molecules that may have a role in epileptogenesis in experimental models.
Development and Implementation of Couple-Based Collaborative Management Model of Type 2 Diabetes Mellitus for Community-Dwelling Chinese Older Adults: A Pilot Randomized Trial

Background: To mobilize family's positive involvement in improving and sustaining self-management activities of older adults with diabetes, we developed a couple-based collaborative management model (CCMM) for community-dwelling older Chinese. Methods: The model was developed stepwise through applying theoretical models, interviewing older couples and community healthcare workers, as well as incorporating expert reviews. A 3-month pilot study was conducted to test the model's feasibility and its treatment effects by linear regression on 18 pairs of older couples aged 60 years+, who were equally divided into a couple-based intervention arm and a patient-only control arm. Results: The developed CCMM covered four theory-driven intervention modules: dyadic assessment, dyadic education, dyadic behavior-change training, and dyadic monitoring. Each module was delivered by community healthcare workers and targeted at older couples as the management units. Based on interviews with older couples and healthcare workers, 4 weekly education and training group sessions and 2-month weekly behavior change booster calls were designed to address older adults' main management barriers. These modules and session contents were evaluated as essential and relevant by the expert panel. Furthermore, the CCMM showed good feasibility and acceptability in the pilot, with non-significant yet more positive changes in physiological outcomes of diabetic participants and couples' well-being and exercise levels of these in the intervention arm than their controlled counterparts. Conclusion: We systematically developed a couple-based collaborative management model of diabetes, which was well-received by healthcare practitioners and highly feasible among older Chinese couples living in the community. The model's treatment effects need to be verified in fully powered randomized controlled trials. Clinical Trial Registration: http://www.chictr. org.cn/showproj.aspx? proj=42964, identifier: ChiCTR1900027137.
Can Bees Help us Find New Antibiotics?

Antibiotics are very important for treating bacterial infections in humans, pets, and livestock. However, improper use of antibiotics has resulted in antibiotic resistance, making it difficult to treat some infections. There is an urgent need to discover new antibiotics, but how do we find them? The answer potentially lies with bees and the honey they produce. Bees who feed on unique plants produce honey with high levels of antibiotic activity, due to the presence of antibacterial compounds in the nectar that the bees collect to make honey. In addition to nectar, the bees also collect pollen, which contains a sample of each visited plant’s unique DNA. By studying this DNA, we have identified plants that are sources of the antibacterial compounds found in honey. We are now working to develop new antibiotics from this honey, and we have installed beehives on university buildings to create our own super honey.
RETRACTED ARTICLE: Crossing the river: toward an embodied pedagogy for integrating arts and mathematics education

Statement of Retraction We, the Editors, authors and Publisher of the journal Youth Theatre Journal have retracted the following article: Kennedy C. Chinyowa, Willy Mwakapenda & Selloane Mokuku (2021) Crossing the river: toward an embodied pedagogy for integrating arts and mathematics education, Youth Theatre Journal, 35:1-2, 129-140. DOI: 10.1080/08929092.2021. 1919264 Since publication, concerns have been raised about the level of contribution of the research reported in this article, compared to the authors previous publication: Kennedy C. Chinyowa, Willy Mwakapenda & Selloane Mokuku (2017) Crossing the River: Towards an emerging pedagogy for integrating arts and mathematics education, Mosenodi Journal 20:1, 48-56. When approached for an explanation, the authors agreed that significant further revisions are needed to clarify the novel aspects of the research reported, and therefore, they have agreed to retract the article. The authors apologise for this oversight and plan to resubmit a revised paper for consideration by the journal. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.
An Elephant in the Room: Using Sampling for Detecting Heavy-Hitters in Programmable Switches

The ability to detect elephant flows in the forwarding device itself, i.e., a switch, facilitates the deployment of new advanced applications such as load-balancing, per-flow QoS management, etc. Sketches and Space Saving summarization techniques are used for elephant flow detection. However, their memory and computing requirements force the cooperation of an external controller device, due to the scarce resources of current programmable switches. To overcome this limitation, we adapt Sketch and Space Saving elephant flow detection techniques to operate with instant notification and sampled traffic. We evaluate the performance of the resulting techniques with three real traffic traces. The use of sampling allows the identification of a large share of the total traffic corresponding to the elephant flows with a low memory footprint and a reduction of the computing requirements in two orders of magnitude compared to unsampled versions. In turn, we observe a slight increase in the number of false positives and the number of flow notifications.
A Holographic Diffraction Label Recognition Algorithm Based on Fusion Double Tensor Features

As an efficient technique for anti-counterfeiting, holographic diffraction labels has been widely applied to various fields. Due to their unique feature, traditional image recognition algorithms are not ideal for the holographic diffraction label recognition. Since a tensor preserves the spatiotemporal features of an original sample in the process of feature extraction, in this paper we propose a new holographic diffraction label recognition algorithm that combines two tensor features. The HSV (Hue Saturation Value) tensor and the HOG (Histogram of Oriented Gradient) tensor are used to represent the color information and gradient information of holographic diffraction label, respectively. Meanwhile, the tensor decomposition is performed by high order singular value decomposition, and tensor decomposition matrices are obtained. Taking into consideration of the different recognition capabilities of decomposition matrices, we design a decomposition matrix similarity fusion strategy using a typical correlation analysis algorithm and projection from similarity vectors of different decomposition matrices to the PCA (Principal Component Analysis) sub-space , then, the sub-space performs KNN (K-Nearest Neighbors) classification is performed. The effectiveness of our fusion strategy is verified by experiments. Our double tensor recognition algorithm complements the recognition capability of different tensors to produce better recognition performance for the holographic diffraction label system.
The dark and light sides of engagement: an analysis of user-generated content in wildlife trade online communities

Recent research has focused on the role of user-generated content (UGC) in the dark side of engagement on social media. In this study, we apply this to the unique context of the online exotic wildlife trade, a critical area of research due its involvement in devastating global species loss as well as harms to human health and livelihoods. We first conduct qualitative analysis on a large data set of UGC with the automatic machine-learning lexical software Leximancer 4.5. 1 to explore the discourse that occurs in comments of posts that promote behaviour change and demand reduction. Then, we complement this by testing an extended elaboration likelihood model to determine the nature of information processing that leads to positive comment valences. Our results show that motivation, opportunity and ability factors moderate the relationship between dual-processing routes and comment valence as well as influencing the likelihood of positive comment valences that indicate attitude change. This work extends the use of theory from Information Systems and Marketing to conservation and provides both conceptual and practical recommendations to encourage behaviour change and reduce the harmful effects of engagement.
A Lyapunov Analysis of a Most Probable Path Finding Algorithm

Distributed information spreading algorithms are important building blocks in Aggregate Computing. We consider a special case, namely for finding a most probable path for message delivery from a set of sources to each device in a network. We formulate a Lyapunov function to prove its regional stability subject to initialization of estimated probabilities to the natural interval [0,1). We also prove that the algorithm converges in a finite time, and is ultimately bounded under persistent measurement errors. We provide tight bounds for convergence time, the ultimate bound, and the time for its attainment.
Hybrid Indoor Positioning System for Pedestrians With Swinging Arms Based on Smartphone IMU and RSSI of BLE

Over the last decades, researchers have invented many positioning algorithms for pedestrians in the indoor environment, such as trilateration, fingerprint, and pedestrian dead reckoning (PDR) system. However, indoor positioning is still difficult to solve with current technology. The fingerprint and trilateration based on received signal strength indicator (RSSI) usually require filters to stabilize the RSSI signal, which will cause a delay in positioning, which is fatal for real-time positioning. The PDR system with the inertial measurement unit (IMU) of smartphones usually requires the direction of the $Y$ -axis of smartphones parallel with movement. Nevertheless, pedestrians will swing their arms while walking normally, which is not considered in the traditional PDR. In this case, a huge bias in heading estimation is inevitable, which causes the traditional PDR system to be unavailable during walking with swinging arms. In this article, a hybrid indoor positioning algorithm based on IMU and RSSI is proposed for pedestrians with swinging arms. The PDR system is improved by analyzing the characteristics of walking postures. In order to eliminate the cumulative error existing in the traditional PDR system, we propose the multipoint positioning algorithm based on RSSI for calibration. Combined with the Kalman filter, positioning delay and error have been effectively reduced for indoor location-based services (ILBSs).
Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

Now a day’s advancement in technology increases the use of automation, mobility, smart devices, and application over the Internet that can create serious problems for protection and the privacy of digital data and raised the global security issues. +erefore, the necessity of intelligent systems or techniques can prevent and protect the data over the network. Cyberattack is the most prominent problem of cybersecurity and now a challenging area of research for scientists and researchers. +ese attacks may destroy data, system, and resources and sometimes may damage the whole network. Previously numerous traditional techniques were used for the detection and mitigation of cyberattack, but the techniques are not efficient for new attacks. Today’s machine learning andmetaheuristic techniques are popularly applied in different areas to achieve efficient computation and fast processing of complex data of the network. +is paper is discussing the improvements and enhancement of security models, frameworks for the detection of cyberattacks, and prevention by using different machine learning and optimization techniques in the domain of cybersecurity. +is paper is focused on the literature of different metaheuristic algorithms for optimal feature selection and machine learning techniques for the classification of attacks, and some of the prominent algorithms such as GA, evolutionary, PSO, machine learning, and others are discussed in detail. +is study provides descriptions and tutorials that can be referred from various literature citations, references, or latest research papers. +e techniques discussed are efficiently applied with high performance for detection, mitigation, and identification of cyberattacks and provide a security mechanism over the network. Hence, this survey presents the description of various existing intelligent techniques, attack datasets, different observations, and comparative studies in detail.
A Review of Reconfigurable Leaky-Wave Antennas

A comprehensive survey of the reconfigurable leaky-wave antennas (LWAs) is made in this paper. Beam-steering and unique radiating features of LWA are highlighted particularly. Therefore, the radiation mechanism of different types of LWA, including uniform, quasi-uniform, periodic, and metamaterial LWAs are discussed in detail. The guiding structures for realizing LWAs, namely microstrip, waveguide, substrate integrated waveguide (SIW), and half-mode SIW (HMSIW) are investigated as well. Basic concepts of electronic beam-scanning LWAs are also introduced, and several state-of-the-art reconfigurable LWAs are studied thoroughly. The investigated reconfigurable LWAs are suitable for beam-scanning applications due to their compactness, ease of implementation, reasonably high gain, and relatively wide beam-scanning range, as will be demonstrated through this comprehensive review.
A Temporal Pool Learning Algorithm Based on Location Awareness

Hierarchical Temporal Memory is a new type of artificial neural network model, which imitates the structure and information processing flow of the human brain. Hierarchical Temporal Memory has strong adaptability and fast learning ability and becomes a hot spot in current research. Hierarchical Temporal Memory obtains and saves the temporal characteristics of input sequences by the temporal pool learning algorithm. However, the current algorithm has some problems such as low learning efficiency and poor learning effect when learning time series data. In this paper, a temporal pool learning algorithm based on location awareness is proposed. +e cell selection rules based on location awareness and the dendritic updating rules based on adjacent inputs are designed to improve the learning efficiency and effect of the algorithm. +rough the algorithm prototype, three different datasets are used to test and analyze the algorithm performance. +e experimental results verify that the algorithm can quickly obtain the complete characteristics of the input sequence. No matter whether there are similar segments in the sequence, the proposed algorithm has higher prediction recall and precision than the existing algorithms.