COVID-19 vaccination campaigns in fragile and conflict-affected settings, Somalia.

Problem: By 31 December 2021, only 5.5% (861 879/15 670 530) of the Somali population had been fully vaccinated against coronavirus disease 2019 (COVID-19). Approach: To rapidly increase COVID-19 vaccine coverage in 2022, the health ministry and its partners (World Health Organization and United Nations Children's Fund) adopted a more responsive strategy. This strategy included careful microplanning, better targeting of populations and providing people-centred vaccination services close to their homes. These services were combined with childhood vaccination and basic health-care provision using the existing polio network and community health workers. Additionally, a digital tool for recording COVID-19 vaccination data and a mobile phone-based electronic registration system were introduced. Local setting: Somalia, a fragile and conflict-affected state, faced challenges when implementing COVID-19 vaccination, including inexperience in managing mass adult vaccination, inadequate infrastructure and health workforce. Furthermore, insecurity in some areas and severe drought resulted in large-scale displacement of people. Relevant changes: The implementation of a more context-specific strategy helped Somalia reach substantially more people with COVID-19 vaccination and 42.1% coverage by 31 December 2022. Additionally, 84 600 zero-dose children received their first childhood vaccine during the integrated campaigns. The increased coverage has led to public health benefits that outweigh the investment in the COVID-19 vaccination campaigns. Lessons learnt: Successful roll-out of adult vaccination is achievable even in a fragile and conflict-affected setting through implementation of a tailored contextualized approach. Key factors include good microplanning, use of digital tools, better population-targeting, bundling vaccines together and delivering vaccination services close to people's homes.

Synthesis and applications of MXene-based composites: a review.

Recently, there has been considerable interest in a new family of transition metal carbides, carbonitrides, and nitrides referred to as MXenes (Ti3C2Tx) due to the variety of their elemental compositions and surface terminations that exhibit many fascinating physical and chemical properties. As a result of their easy formability, MXenes may be combined with other materials, such as polymers, oxides, and carbon nanotubes, which can be used to tune their properties for various applications. As is widely known, MXenes and MXene-based composites have gained considerable prominence as electrode materials in the energy storage field. In addition to their high conductivity, reducibility, and biocompatibility, they have also demonstrated outstanding potential for applications related to the environment, including electro/photocatalytic water splitting, photocatalytic carbon dioxide reduction, water purification, and sensors. This review discusses MXene-based composite used in anode materials, while the electrochemical performance of MXene-based anodes for Li-based batteries (LiBs) is discussed in addition to key findings, operating processes, and factors influencing electrochemical performance.

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data.

This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion. This paper presents a systematic assessment of numerous feature extraction techniques. In particular, three different feature encoding techniques are presented to encode multimodal time series sensory data. In the first technique, we utilized eighteen different handcrafted features which are extracted directly from the raw sensory data. The second technique follows the Bag-of-Visual-Words model; the raw sensory data are encoded using a pre-computed codebook and a locality-constrained linear encoding (LLC)-based feature encoding technique. We evaluated two different machine learning algorithms to assess the effectiveness of the proposed features in the encoding of raw sensory data. In the third feature encoding technique, we proposed two end-to-end deep learning models to automatically extract the features from raw sensory data. A thorough experimental evaluation is conducted on four large sensory datasets and their outcomes are compared. A comparison of the recognition results with current state-of-the-art methods demonstrates the computational efficiency and high efficacy of the proposed feature encoding method. The robustness of the proposed feature encoding technique is also evaluated to recognize human daily activities. Additionally, this paper also presents a new dataset consisting of the gait patterns of 42 individuals, gathered using IMU sensors.

The flavors of edible mushrooms: A comprehensive review of volatile organic compounds and their analytical methods.

Due to their distinctive flavors, edible mushrooms have gained attention in flavor-related research, and the quality of their flavors determines their consumption. The odor is a vital element of food flavor that significantly impacts consumers' perceptions and purchase decisions. The volatile organic compounds (VOCs) of the odorant ingredient is the primary factors affecting scent characteristics. VOCs analysis and identification require technical assistance. The production and use of edible mushrooms can be aided by a broader examination of their volatile constituents. This review discusses the composition of VOCs in edible mushrooms and how they affect flavors. The principles, advantages, and disadvantages of various methods for extraction, isolation, and characterization of the VOCs of edible mushrooms are also highlighted. The numerous VOCs found in edible mushrooms such as primarily C-8 compounds, organic sulfur compounds, aldehydes, ketones, alcohols, and esters are summarized along with their effects on the various characteristics of scent. Combining multiple extraction, isolation, identification, and quantification technologies will facilitate rapid and accurate analysis of VOCs in edible mushrooms as proof of sensory attributes and quality.

An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems.

With the Internet of Things (IoT), mobile healthcare applications can now offer a variety of dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed and accuracy of diagnosis, improving the quality of healthcare services. However, privacy is garnering an increasing amount of attention these days, especially concerning personal healthcare data, which are sensitive. There are a variety of prevailing privacy preservation techniques for disease prediction that are rendered. Nonetheless, there is a chance of medical users being affected by numerous disparate diseases. Therefore, it is vital to consider multi-label instances, which might decrease the accuracy. Thus, this paper proposes an efficient privacy-preserving (PP) scheme for patient healthcare data collected from IoT devices aimed at disease prediction in the modern Health Care System (HCS). The proposed system utilizes the Log of Round value-based Elliptic Curve Cryptography (LR-ECC) to enhance the security level during data transfer after the initial authentication phase. The authorized healthcare staff can securely download the patient data on the hospital side. Utilizing the Herding Genetic Algorithm-based Deep Learning Neural Network (EHGA-DLNN) can test these data with the trained system to predict the diseases. The experimental results demonstrate that the proposed approach improves prediction accuracy, privacy, and security compared to the existing methods.

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data.

Human activity recognition (HAR) aims to recognize the actions of the human body through a series of observations and environmental conditions. The analysis of human activities has drawn the attention of the research community in the last two decades due to its widespread applications, diverse nature of activities, and recording infrastructure. Lately, one of the most challenging applications in this framework is to recognize the human body actions using unobtrusive wearable motion sensors. Since the human activities of daily life (e.g., cooking, eating) comprises several repetitive and circumstantial short sequences of actions (e.g., moving arm), it is quite difficult to directly use the sensory data for recognition because the multiple sequences of the same activity data may have large diversity. However, a similarity can be observed in the temporal occurrence of the atomic actions. Therefore, this paper presents a two-level hierarchical method to recognize human activities using a set of wearable sensors. In the first step, the atomic activities are detected from the original sensory data, and their recognition scores are obtained. Secondly, the composite activities are recognized using the scores of atomic actions. We propose two different methods of feature extraction from atomic scores to recognize the composite activities, and they include handcrafted features and the features obtained using the subspace pooling technique. The proposed method is evaluated on the large publicly available CogAge dataset, which contains the instances of both atomic and composite activities. The data is recorded using three unobtrusive wearable devices: smartphone, smartwatch, and smart glasses. We also investigated the performance evaluation of different classification algorithms to recognize the composite activities. The proposed method achieved 79% and 62.8% average recognition accuracies using the handcrafted features and the features obtained using subspace pooling technique, respectively. The recognition results of the proposed technique and their comparison with the existing state-of-the-art techniques confirm its effectiveness.

Assessment of DSM Based on Radiometric Transformation of UAV Data.

Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 meters and UAV Drone data from 300 and 500 meters flying height. RAW UAV images acquired from 500 meters flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 meters flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 meters flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 meters to 0.11 meters. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.

Exploiting Superpixels for Multi-Focus Image Fusion.

Multi-focus image fusion is the process of combining focused regions of two or more images to obtain a single all-in-focus image. It is an important research area because a fused image is of high quality and contains more details than the source images. This makes it useful for numerous applications in image enhancement, remote sensing, object recognition, medical imaging, etc. This paper presents a novel multi-focus image fusion algorithm that proposes to group the local connected pixels with similar colors and patterns, usually referred to as superpixels, and use them to separate the focused and de-focused regions of an image. We note that these superpixels are more expressive than individual pixels, and they carry more distinctive statistical properties when compared with other superpixels. The statistical properties of superpixels are analyzed to categorize the pixels as focused or de-focused and to estimate a focus map. A spatial consistency constraint is ensured on the initial focus map to obtain a refined map, which is used in the fusion rule to obtain a single all-in-focus image. Qualitative and quantitative evaluations are performed to assess the performance of the proposed method on a benchmark multi-focus image fusion dataset. The results show that our method produces better quality fused images than existing image fusion techniques.

Progress Toward Measles Elimination - Eastern Mediterranean Region, 2013-2019.

In 1997, during the 41st session of the Regional Committee for the Eastern Mediterranean, the 21 countries in the World Health Organization (WHO) Eastern Mediterranean Region* (EMR) passed a resolution to eliminate† measles (1). In 2015, this goal was included as a priority in the Eastern Mediterranean Vaccine Action Plan 2016-2020 (EMVAP) (2), endorsed at the 62nd session of the Regional Committee (3). To achieve this goal, the WHO Regional Office for the Eastern Mediterranean developed a four-pronged strategy: 1) achieve ≥95% vaccination coverage with the first dose of measles-containing vaccine (MCV1) among children in every district of each country through routine immunization services; 2) achieve ≥95% vaccination coverage with a second MCV dose (MCV2) in every district of each country either through implementation of a routine 2-dose vaccination schedule or through supplementary immunization activities§ (SIAs); 3) conduct high-quality, case-based surveillance in all countries; and 4) provide optimal measles clinical case management, including dietary supplementation with vitamin A (4). This report describes progress toward measles elimination in EMR during 2013-2019 and updates a previous report (5). Estimated MCV1 coverage increased from 79% in 2013 to 82% in 2018. MCV2 coverage increased from 59% in 2013 to 74% in 2018. In addition, during 2013-2019, approximately 326.4 million children received MCV during SIAs. Reported confirmed measles incidence increased from 33.5 per 1 million persons in 2013 to 91.2 in 2018, with large outbreaks occurring in Pakistan, Somalia, and Yemen; incidence decreased to 23.3 in 2019. In 2019, the rate of discarded nonmeasles cases¶ was 5.4 per 100,000 population. To achieve measles elimination in the EMR, increased visibility of efforts to achieve the measles elimination goal is critically needed, as are sustained and predictable investments to increase MCV1 and MCV2 coverage, conduct high-quality SIAs, and reach populations at risk for not accessing immunization services or living in areas with civil strife.

Marker-Based Movement Analysis of Human Body Parts in Therapeutic Procedure.

Movement analysis of human body parts is momentous in several applications including clinical diagnosis and rehabilitation programs. The objective of this research is to present a low-cost 3D visual tracking system to analyze the movement of various body parts during therapeutic procedures. Specifically, a marker based motion tracking system is proposed in this paper to capture the movement information in home-based rehabilitation. Different color markers are attached to the desired joints' locations and they are detected and tracked in the video to encode their motion information. The availability of this motion information of different body parts during the therapy can be exploited to achieve more accurate results with better clinical insight, which in turn can help improve the therapeutic decision making. The proposed framework is an automated and inexpensive motion tracking system with execution speed close to real time. The performance of the proposed method is evaluated on a dataset of 10 patients using two challenging matrices that measure the average accuracy by estimating the joints' locations and rotations. The experimental evaluation and its comparison with the existing state-of-the-art techniques reveals the efficiency of the proposed method.

Nanosecond pulse laser generation at 1.55 and 2 µm regions by integrating a piece of newly developed chromium-doped fiber-based saturable absorber.

This paper demonstrated the nanosecond pulse laser operation at 1.55 and 2 µm wavelength regions using a newly develop chromium-doped fiber (CrDF) as a saturable absorber (SA) to convert efficiently continuous-wave laser operation to nanosecond pulse laser operation. The laser uses an erbium-doped fiber (EDF) and thulium-doped fiber as the gain medium. A piece of 10 cm long CrDF was integrated into both laser cavities to generate nanosecond pulse laser operation. In 1.55 region generation, an additional single-mode fiber (SMF) 100 m long was added into the EDF laser cavity. Stable pulse generation occurred at a repetition rate of 1 MHz with a pulse width of 432 ns and a signal-to-noise ratio (SNR) of 66 dB. The highest peak power of 24 mW was obtained at 142 mW pump power. In 2 µm region generation, the obtained repetition rate was 10 MHz with a pulse width and SNR of 59 ns and 41 dB, respectively. The highest peak power was only 8.3 mW. By looking into the findings, the newly developed CrDF SA has a potential to be further enhanced toward better generation of ultrashort pulse fiber lasers.

Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model.

Movement analysis of infants' body parts is momentous for the early detection of various movement disorders such as cerebral palsy. Most existing techniques are either marker-based or use wearable sensors to analyze the movement disorders. Such techniques work well for adults, however they are not effective for infants as wearing such sensors or markers may cause discomfort to them, affecting their natural movements. This paper presents a method to help the clinicians for the early detection of movement disorders in infants. The proposed method is marker-less and does not use any wearable sensors which makes it ideal for the analysis of body parts movement in infants. The algorithm is based on the deformable part-based model to detect the body parts and track them in the subsequent frames of the video to encode the motion information. The proposed algorithm learns a model using a set of part filters and spatial relations between the body parts. In particular, it forms a mixture of part-filters for each body part to determine its orientation which is used to detect the parts and analyze their movements by tracking them in the temporal direction. The model is represented using a tree-structured graph and the learning process is carried out using the structured support vector machine. The proposed framework will assist the clinicians and the general practitioners in the early detection of infantile movement disorders. The performance evaluation of the proposed method is carried out on a large dataset and the results compared with the existing techniques demonstrate its effectiveness.

Enhancement of Ferroelectricity for Orthorhombic (Tb0.861Mn0.121)MnO3-δ by Copper Doping.

Copper-doped (Tb0.861Mn0.121)MnO3-δ has been synthesized by the conventional solid state reaction method. X-ray, neutron, and electron diffraction data indicate that they crystallize in Pnma space group at room temperature. Two magnetic orderings are found for this series by neutron diffraction. One is the ICAM (incommensurate canted antiferromagnetic) ordering of Mn with a wave vector qMn = (∼0.283, 0, 0) with a ≈ 5.73 Å, b ≈ 5.31 Å, and c ≈ 7.41 Å, and the other is the CAM (canted antiferromagnetic) ordering of both Tb and Mn in the magnetic space group Pn'a21' with a ≈ 5.73 Å, b ≈ 5.31 Å, and c ≈ 7.41 Å. A dielectric peak around 40 K is found for the samples doped with Cu, which is higher than that for orthorhombic TbMnO3.

Structuring economic incentives to reduce emissions from deforestation within Indonesia.

We estimate and map the impacts that alternative national and subnational economic incentive structures for reducing emissions from deforestation (REDD+) in Indonesia would have had on greenhouse gas emissions and national and local revenue if they had been in place from 2000 to 2005. The impact of carbon payments on deforestation is calibrated econometrically from the pattern of observed deforestation and spatial variation in the benefits and costs of converting land to agriculture over that time period. We estimate that at an international carbon price of $10/tCO(2)e, a "mandatory incentive structure," such as a cap-and-trade or symmetric tax-and-subsidy program, would have reduced emissions by 163-247 MtCO(2)e/y (20-31% below the without-REDD+ reference scenario), while generating a programmatic budget surplus. In contrast, a "basic voluntary incentive structure" modeled after a standard payment-for-environmental-services program would have reduced emissions nationally by only 45-76 MtCO(2)e/y (6-9%), while generating a programmatic budget shortfall. By making four policy improvements--paying for net emission reductions at the scale of an entire district rather than site-by-site; paying for reductions relative to reference levels that match business-as-usual levels; sharing a portion of district-level revenues with the national government; and sharing a portion of the national government's responsibility for costs with districts--an "improved voluntary incentive structure" would have been nearly as effective as a mandatory incentive structure, reducing emissions by 136-207 MtCO(2)e/y (17-26%) and generating a programmatic budget surplus.

Milk: A Natural Guardian for the Gut Barrier.

The gut barrier plays an important role in health maintenance by preventing the invasion of dietary pathogens and toxins. Disruption of the gut barrier can cause severe intestinal inflammation. As a natural source, milk is enriched with many active constituents that contribute to numerous beneficial functions, including immune regulation. These components collectively serve as a shield for the gut barrier, protecting against various threats such as biological, chemical, mechanical, and immunological threats. This comprehensive review delves into the active ingredients in milk, encompassing casein, α-lactalbumin, ß-lactoglobulin, lactoferrin, the milk fat globular membrane, lactose, transforming growth factor, and glycopeptides. The primary focus is to elucidate their impact on the integrity and function of the gut barrier. Furthermore, the implications of different processing methods of dairy products on the gut barrier protection are discussed. In conclusion, this study aimed to underscore the vital role of milk and dairy products in sustaining gut barrier health, potentially contributing to broader perspectives in nutritional sciences and public health.

Effect of Lactobacillus helveticus exopolysaccharides molecular weight on yogurt gel properties and its internal mechanism.

The processing characteristics of yogurt are closely related to the composition and arrangement of exopolysaccharides (EPS) in lactic acid bacteria (LAB). To fully understand and develop the functional properties of EPS and to study the effect of EPS molecular weight on yogurt and its mechanism, the physicochemical properties of high molecular weight EPS-LH43, medium molecular weight EPS-LH13, and low molecular weight EPS-LH23, as well as the gel properties and protein conformation of yogurt, were determined and analyzed in this experiment. The results indicate that EPS-LH43 and EPS-LH13 are both composed of mannose, rhamnose, galacturonic acid, glucose, and galactose. EPS-LH23 is composed of mannose, galacturonic acid, glucose, and galactose. Their Number-average Molecular Weight is 5.21 × 106 Da, 2.39 × 106 Da and 3.76 × 105 Da, respectively. In addition, all three types of EPS have good thermal stability and can improve the stability of casein. In addition, the analysis of the texture, particle size, potential, water holding capacity, rheology, low field nuclear magnetic resonance, microstructure, and flavor characteristics of yogurt confirmed the relationship between the molecular weight of LAB EPS and the gel properties of yogurt. Fluorescence spectrophotometer and circular dichroism analysis indicate that the different molecular weights of LAB EPS have different effects on protein structure, which is an intrinsic factor leading to significant differences in the gel properties of the three types of fermented milk. These findings provide new references for enhancing the understanding of the structure-activity relationship of EPS and indicate that EPS-LH43 can be used to improve the gel properties of dairy products.

Conducting nationwide cohort COVID-19 serology surveys on a nation with limited resources and decentralized governance: Evidence from Indonesia.

Knowing the level of SARS-CoV-2 transmission is crucial for decision-making on Coronavirus disease (COVID-19) handling. Guidelines for measuring SARS-CoV-2 antibodies to estimate seroprevalence are conceptually straightforward and internationally available. However, challenges exist for developing countries with limited laboratory capacity, diverse geographical topography, fragmented health information systems and limited resources such as Indonesia. One year after the first case was confirmed in Indonesia, approximately ten infections were undiagnosed or underreported for every reported case. Under those circumstances, we described the method and lessons learned in conducting nationwide cohort COVID-19 serology surveys in a nation with limited resources, such as Indonesia. •Strong cross-sectoral collaboration between ministries and levels of governance (central and subnational) enables strategic use of civil registration database for efficient sampling.•All agglomeration districts (highly dense population and urban area) were selected as study sites, and additional non-agglomeration districts were selected using probability-proportional-to-size (PPS).•Involving the experienced phlebotomist in community health service provider and district laboratory cut down preparation time.

CFD study of self-cleaning system of multi-stage tangential roller threshing unit for precise buckwheat breeding.

Buckwheat is a globally recognized, nutritionally rich crop with robust adaptability, serving as a multi-purpose plant for its health benefits. Achieving precise and mechanized plot seed harvesting is a critical step in obtaining accurate results in breeding experiments. However, plot breeding requires no seed retention, no mixing, and ensures no accumulation of seed in the threshing unit. A self-cleaning technology was developed to prevent seed retention, mixing, and accumulation in the multistage tangential cylinder threshing unit. The newly designed cleaning system has five air inlets and a centrifugal fan for pneumatic cleaning. CFD simulations were conducted for each inlet position, coupled with four varying inlet velocities and the rotation speed of the main threshing cylinder. During the post-processing stage of the CFD modeling, a line consisting of fifty points was drawn beneath the threshing drums, and the air velocity at these points was recorded. The optimal configuration of inlet position, inlet air velocity, and main threshing drum rotation speed for efficient cleaning was identified based on the ratio of points beneath the drums where the airflow speed surpassed the suspension speed of buckwheat to the points where the airflow speed was lower than the suspension speed of buckwheat. The optimal configuration for "inlet_1" was identified based on the suspension velocity of buckwheat grain, with an inlet velocity of 4 m/s and a main threshing drum speed of 450 rpm.

The need for active and integrated involvement of the community and health professionals in the prevention and control of dengue hemorrhagic fever in Indonesia.

Successful control and prevention of dengue fever requires active involvement from all parties. For this reason, three innovative programs are needed, namely: i) increasing knowledge, attitude and practice (KAP) of the community and health professionals as capital in controlling dengue fever in a sustainable manner; ii) application of "3M Plus" to suppress vector breeding in household settings; iii) promotion of the "Jumantik" program as an effective community empowerment approach to prevent and control dengue fever based on community independence. It was concluded that successful control of dengue fever requires integration of the community and health workers through various innovative programs.

Molybdenum tungsten hydrogen oxide doped with phosphorus for enhanced oxygen/hydrogen evolution reactions.

The development of efficient electrocatalysts for hydrogen and oxygen evolution reactions (HER and OER) is pivotal for advancing cleaner and sustainable fuel production technologies. The conventional electrocatalysts have limited stability and higher overpotentials, and there is demand to explore advanced materials and synthesis methods. In this context, a novel bifunctional electrocatalyst has been devised through the phosphidation of tungsten molybdenum oxide (P-Mo0.69W0.31H0.98O3) at relatively low temperatures. This innovative approach aims to enhance the efficiency of HER and OER while minimizing the overpotential values and maintaining higher stability. Specifically, the individual performance of Mo0.69W0.31H0.98O3 has been significantly boosted by doping it with phosphorus at a low temperature of 300 °C. This doping process results in a unique morphology for the catalyst, leading to a notable improvement in OER/HER performances. P-Mo0.69W0.31H0.98O3 exhibits a potential of 320 mV at 10 mA cm-2 in a KOH electrolyte, demonstrating both high activity and long-term stability. Additionally, P-Mo0.69W0.31H0.98O3 exhibits commendable HER performance, requiring only 380 mV at 100 mA cm-2. This combination of efficient OER and HER performance positions P-Mo0.69W0.31H0.98O3 as representing a significant advancement in the field of electrocatalysis, additionally addressing the fundamental gap by providing stable and hybrid catalyst for various electrochemical devices. Given its cost-effectiveness and exceptional activity, P-Mo0.69W0.31H0.98O3 holds significant potential for advancing the field of electrocatalysis and contributing to the development of cleaner and sustainable fuel production methods.