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Adefunke Olarinre Babatola,1,2 Oladele Simeon Olatunya,1,2 Ezra Olatunde Ogundare,1,2 Adebukola Bidemi Ajite,1,2 Isaac Oludare Oluwayemi,1,2 Awolowo Anthony Thomas,3 Adekunle Bamidele Taiwo,2 Odunayo Adebukola Fatunla,2 Akinwumi Kolawole Komolafe,2 Airemionkhale Alfred21Department of Paediatrics, Ekiti State University, Ado-Ekiti, Ekiti State, Nigeria; 2Department of Paediatrics, Ekiti State University Teaching Hospital, Ado-Ekiti, Ekiti State, Nigeria; 3Department of Radiology, Ekiti State University, Ado-Ekiti, Ekiti State, NigeriaAbstract: Vitamin A supplementation program where single high-dose vitamin A supplements are provided to the qualified (infants and children) at regular intervals is operational in Nigeria as a public health initiative to control vitamin A deficiency which is prevalent in our country in accordance with the WHO recommendations. Reports of symptomatic acute hypervitaminosis A are scarce. We report a case of acute hypervitaminosis A resulting from accidental ingestion of vitamin A supplement capsules. This is to reiterate the need for caregivers to keep drugs out of the reach of children. Clinicians should also have a high index of suspicion.Keywords: vitamin A supplementation, malaria, accidental ingestion, raised intracranial pressure

Self-replicating gene drives that modify sex ratios or infer a fitness cost could be used to control populations of invasive alien species. The targeted deletion of Y sex chromosomes using CRISPR technology offers a new approach for sex bias that could be incorporated within gene-drive designs. We introduce a novel gene-drive strategy termed Y-CHromosome deletion using Orthogonal Programmable Endonucleases (Y-CHOPE), incorporating a programmable endonuclease that ‘shreds’ the Y chromosome, thereby converting XY males into fertile XO females. Firstly, we demonstrate that the CRISPR/Cas12a system can eliminate the Y chromosome in embryonic stem cells with high efficiency (c. 90%). Next, using stochastic, individual-based models of a pest mouse population, we show that a Y-shredding drive that progressively depletes the pool of XY males could effect population eradication through mate limitation. Our molecular and modeling data suggest that a Y-CHOPE gene drive could be a viable tool for vertebrate pest control.

Light-harvesting complex II (LHCII) serves a central role in light harvesting for oxygenic photosynthesis and is arguably the most important photosynthetic antenna complex. In this work, we present two-dimensional electronic-vibrational (2DEV) spectra of LHCII isolated from spinach, demonstrating the possibility of using this technique to track the transfer of electronic excitation energy between specific pigments within the complex. We assign the spectral bands via comparison with the 2DEV spectra of the isolated chromophores, chlorophyll a and b, and present evidence that excitation energy between the pigments of the complex are observed in these spectra. Finally, we analyze the essential components of the 2DEV spectra using singular value decomposition, which makes it possible to reveal the relaxation pathways within this complex.

We present two-dimensional electronic-vibrational spectra of the chlorophyll (Chl) binding protein light harvesting complex II (LHCII) from spinach, and of isolated Chl a and Chl b. We show how energy transfer from Chl b to Chl a and subsequent relaxation can be directly observed in LHCII using this multidimensional technique.

Two dimensional electronic spectroscopy has proved to be a valuable experimental technique to reveal electronic excitation dynamics in photosynthetic pigment-protein complexes, nanoscale semiconductors, organic photovoltaic materials, and many other types of systems. It does not, however, provide direct information concerning the spatial structure and dynamics of excitons. 2D infrared spectroscopy has become a widely used tool for studying structural dynamics but is incapable of directly providing information concerning electronic excited states. 2D electronic-vibrational (2DEV) spectroscopy provides a link between these domains, directly connecting the electronic excitation with the vibrational structure of the system under study. In this work, we derive response functions for the 2DEV spectrum of a molecular dimer and propose a method by which 2DEV spectra could be used to directly measure the electronic site populations as a function of time following the initial electronic excitation. We present results from the response function simulations which show that our proposed approach is substantially valid. This method provides, to our knowledge, the first direct experimental method for measuring the electronic excited state dynamics in the spatial domain, on the molecular scale.

Carotenoids play an essential role in photoprotection, interacting with other pigments to safely dissipate excess absorbed energy as heat. In cyanobacteria, the short time scale photoprotective mechanisms involve the photoactive orange carotenoid protein (OCP), which binds a single carbonyl carotenoid. Blue-green light induces the photoswitching of OCP from its ground state form (OCPO) to a metastable photoproduct (OCPR). OCPR can bind to the phycobilisome antenna and induce fluorescence quenching. The photoswitching is accompanied by structural and functional changes at the level of the protein and of the bound carotenoid. Here, we use broadband two-dimensional electronic spectroscopy to study the differences in excited state dynamics of the carotenoid in the two forms of OCP. Our results provide insight into the origin of the pronounced vibrational lineshape and oscillatory dynamics observed in linear absorption and 2D electronic spectroscopy of OCPO and the large inhomogeneous broadening in OCPR, with consequences for the chemical function of the two forms.

Two-dimensional electronic-vibrational spectroscopy is used to study the excited state evolution of the carotenoid β-apo-8'-carotenal in solution. This new multidimensional spectroscopy is unique in its ability to directly follow the electronic and nuclear degrees of freedom simultaneously. © 2014 OSA.

This paper considers the transceiver design in a multiuser multiple-input multiple-output (MIMO) full-duplex (FD) amplify-and-forward (AF) relay downlink communication system, where users simultaneously transmit data via an FD relay node. The design incorporates an imperfect loop interference (LI) cancellation which results in a residual LI. Linear precoders are employed at the sources and relay, and minimum mean-squared-error (MMSE) combiners are employed at the destinations to mitigate the effect of the residual LI. The corresponding design problem is highly nonconvex, so a closed-form solution is intractable. Thus, an iterative method is developed to solve this optimization problem. Simulation results are presented which show that the proposed iterative algorithm provides better performance than the corresponding half-duplex (HD) solution in terms of the achievable rate under residual LI.

Abstract The impact of fog on visibility is a major factor affecting traffic congestion and safety. This paper proposes a microscopic traffic model that captures the features of traffic in foggy weather and characterizes it based on visibility. The intelligent driver (ID) model is based on a constant acceleration exponent and produces similar traffic behaviour for all conditions, which is unrealistic. The performance of the ID and proposed models is evaluated on a 2.2 km ring road for 250 s with a platoon of 51 vehicles. Results are presented which show that the proposed model characterizes traffic realistically with lower acceleration and deceleration compared to the ID model. Further, it does not create stop‐and‐go waves and is stable even during foggy weather. The proposed model can be used to reduce fuel consumption and pollution resulting from traffic congestion.

The applications of non-square binary matrices span many domains including mathematics, error-correction coding, machine learning, data storage, navigation signals, and cryptography. In particular, they are employed in the McEliece and Niederreiter public-key cryptosystems. For the parity check matrix of these cryptosystems, a systematic non-square binary matrix H with dimensions m × n , n > m , m = n − k , there exist 2 m ( n − m ) distinct inverse matrices. This article presents an algorithm to generate these matrices as well as a method to construct a random inverse matrix. Then it is extended to non-square matrices in arbitrary fields. This overcomes the limitations of the Moore-Penrose and Gauss-Jordan methods. The application to public-key cryptography is also discussed.

Abstract Over the past several decades, major efforts have been directed toward the optimization of carrier concentrations to maximize thermoelectric performance. Chemical doping is an effective way to control carriers, but electrostatic gating provides a continuous tuning knob that enables effective and dynamic changes to the carrier density. Here, a method is reported that uses an electric‐double‐layer (EDL) transistor‐based ionic liquids gating to adjust the thermoelectric properties of thin films made from Nb‐doped SrTiO3 (Nb‐STO). This technique allows us to effectively change these properties at room temperature by varying the concentration of charge carriers within a broad range. A combination of lower film thickness and intrinsic carrier concentration leads to an enhanced ionic liquid‐gated response, resulting in an 18‐fold enhancement in power factor at room temperature for a 14 nm thin 4% Nb‐STO film at gate voltages within ±3.0 V. The present study offers new insights and strategies toward enhanced gate tunable thermoelectric properties in thin films.

Abstract Digital signature schemes are used for the authentication and verification of signatures. The Courtois–Finiasz–Sendrier (CFS) digital signature is a well‐known code‐based digital signature scheme based on the Niederreiter cryptosystem. However, it is not widely used due to the computation time of the signing algorithm. Most code‐based digital signature schemes are based on the Niederreiter cryptosystem. This paper proposes a new code‐based digital signature that is based on the McEliece cryptosystem. Key generation, signing, and verification algorithms are presented. The key generation algorithm constructs a public key using random inverse matrices. The signing algorithm has lower complexity and requires less computation time than the CFS scheme to sign a document. The verification algorithm is able to detect forgeries. It is shown that the proposed scheme is secure against public key structural attacks.

ABSTRACTVaccines may alter the ability to combat infections unrelated to the target disease, i.e. have “nonspecific effects.” The non-live Diphtheria-Tetanus-Pertussis vaccine (DTP) has been associated with increased child mortality, especially for females. In 2008, the DTP-containing Pentavalent vaccine replaced DTP vaccine in Guinea-Bissau. We investigate female relative to male mortality after Penta vaccination. In Guinea-Bissau, Bandim Health Project (BHP) registered children’s vaccination and vital status at biannual village visits and provided vaccines. Among children Penta-vaccinated by BHP, we compared mortality of males and females in Cox proportional hazards models. Children aged 6 weeks to 8 months entered the analysis at the date of vaccination and were followed for up to 6 months. Between September 2008 and December 2017, 33,989 children aged 6 weeks to 8 months were under surveillance. Of these 12,753 (females: 6,363; males: 6,390) received Penta by the BHP and entered the study contributing with 19,667 observations. The mortality rate following Penta vaccination was 25.2 per 1,000 person years for females and 26.6 for males, resulting in an adjusted Female/Male mortality rate ratio of (F/M aMRR) 1.01 (0.82–1.25). The association between sex and mortality differed by timeliness of vaccination, F/M aMRR: 0.62 (0.41–0.93) for children vaccinated below median age, and F/M aMRR: 1.38 (0.90–2.13) for children vaccinated above median age. We did not find higher overall mortality in females than males after Penta vaccination. Our findings suggest that mortality differences between males and females following Penta vaccination may depend on timeliness of Penta vaccination.

Quantum field theory (QFTh) simulators simulate physical systems using quantum circuits that process quantum information (qubits) via single field (SF) and/or quantum double field (QDF) transformation. This review presents models that classify states against pairwise particle states |ij〉, given their state transition (ST) probability P|ij〉. A quantum AI (QAI) program, weighs and compares the field’s distance between entangled states as qubits from their scalar field of radius R≥|rij|. These states distribute across 〈R〉 with expected probability 〈Pdistribute〉 and measurement outcome 〈M(Pdistribute)〉=P|ij〉. A quantum-classical hybrid model of processors via QAI, classifies and predicts states by decoding qubits into classical bits. For example, a QDF as a quantum field computation model (QFCM) in IBM-QE, performs the doubling of P|ij〉 for a strong state prediction outcome. QFCMs are compared to achieve a universal QFCM (UQFCM). This model is novel in making strong event predictions by simulating systems on any scale using QAI. Its expected measurement fidelity is 〈M(F)〉≥7/5 in classifying states to select 7 optimal QFCMs to predict 〈M〉’s on QFTh observables. This includes QFCMs’ commonality of 〈M〉 against QFCMs limitations in predicting system events. Common measurement results of QFCMs include their expected success probability 〈Psuccess〉 over STs occurring in the system. Consistent results with high F’s, are averaged over STs as 〈Pdistribute〉yielding 〈Psuccess〉≥2/3 performed by an SF or QDF of certain QFCMs. A combination of QFCMs with this fidelity level predicts error rates (uncertainties) in measurements, by which a P|ij〉=〈Psuccess〉

Smart mobility is crucial for future smart cities. Traffic simulation software (TSS) is an important tool for efficient planning and management of road networks to achieve this goal. Many TSS tools have been developed for both microscopic and macroscopic homogenous traffic flow. However, only two (SUMO and HetroSim) are applicable to heterogeneous traffic. In this paper, HetroTraffSim is proposed to simulate macroscopic heterogeneous traffic flows at road bottlenecks. It is developed using the Unity3D engine and is based on a second-order traffic flow model. It is evaluated for a 360 m road segment on University Road, Peshawar, Pakistan. This segment contains a 78.5 m bottleneck which causes traffic congestion. This bottleneck is due to the construction of a pedestrian overhead bridge which reduces the three-lane road to two lanes. HetroTraffSim provides normalized traffic velocity, average traffic density, traffic flow, and time, as well as the temporal and spatial evolution of traffic. The results obtained show that a change in the distance headway affects the traffic flow, velocity, and density. Further, HetroTraffSim can be extended to automated traffic flows using raycasting. It can easily be used to create realistic traffic scenarios and the computational complexity is low due to the small-degree polynomials employed. HetroTraffSim can be used by traffic planners to improve traffic flow and public safety.

Traffic simulation software (TSS) is employed for planning, designing, and managing road networks. Among existing TSS, only SUMO and HETROSIM can be used for heterogeneous traffic. The objective of this work is to develop new software for heterogeneous traffic simulation which is effective and efficient. This is motivated by the fact that traffic in developing countries is typically heterogeneous. The HetroTraffSim TSS was developed using Unity3D and is based on a recently developed macroscopic traffic flow model. A 360 m section of University Road which is a two-lane arterial road in Peshawar, Pakistan, is used to evaluate the performance using real traffic data. The results obtained show that an increase in density decreases the velocity. Further, HetroTraffSim can be used to characterize and predict heterogeneous traffic behavior.

Increasing building energy consumption has led to environmental and economic issues. Energy demand prediction (DP) aims to reduce energy use. Machine learning (ML) methods have been used to improve building energy consumption, but not all have performed well in terms of accuracy and efficiency. In this paper, these methods are examined and evaluated for modern building (MB) DP.

A microscopic traffic flow model is developed that incorporates vehicle vibrations due to pavement condition. The Intelligent Driver (ID) model employs a fixed exponent so traffic behavior is the same regardless of the road condition. Thus, it ignores the underlying physics. To address this limitation, the proposed model employs the Pavement Condition Index (PCI) in describing traffic behavior. The performance of both models is evaluated on a 3000 m circular road using the Euler numerical discretization technique. The results show that the performance of the proposed model varies with the pavement condition (PCI), as expected. Furthermore, the traffic flow increases with vehicle speed. The oscillations in speed and density with the proposed model decrease as the PCI increases, and are larger when the speed is higher. Consequently, the results with the proposed model align more closely with reality as they are based on the PCI, and so are a more accurate representation of traffic behavior.

In this work, we propose a new directed digital signature scheme over a group ring whose security relies on the hardness of the discrete logarithm problem and the factorization search problem. This scheme is efficient as it requires very few operations for both signing and verifying signatures. Furthermore, the security of the proposed scheme is examined.

Road infrastructure in good condition is a key requirement for efficient transportation systems which leads to economic prosperity and improved quality of life. However, road surface conditions deteriorate over time according to traffic loads and environmental factors. Poor road conditions lead to congestion, accidents, lost productivity, and driver fatigue. This work considers the relationship between road pavement condition and vehicle speed, vibration, and in-vehicle noise. A 7 km section of the Grand Trunk Road, Peshawar, Pakistan divided into 280 segments (140 for each lane), of length 50 m was observed and the Pavement Condition Index (PCI) of each segment was determined based on the recurrent distress type and density according to ASTM D6433-011 guidelines. The number of very good, satisfactory, fair, poor, and very poor conditions are 51, 52, 81, 48, and 42, respectively. The mobile app BotlnckDectr was employed to measure vehicle speed, RPM, noise, vibration, GPS location, and time. Statistical analysis was employed to determine the relationship between PCI and vehicle speed, vibration, and in-vehicle noise. The results obtained indicate that noise and vibration increase by 3.3% and more than 30%, respectively, as the pavement condition changes from good to very poor, and vehicle speed decreases by 8.8%.

Abstract Global threats to health and health security are growing. Fragile and failed states, armed groups, ungoverned spaces, outbreaks and potential unknown “Disease X” threats, antimicrobial resistance (AMR), hybrid and gray zone conflict all exacerbate complex medical emergencies. These growing threats increase preventable morbidity and mortality of the most vulnerable populations. In an effort to promote best practices, standardize responses, and prevent excess death and disability in these contexts, The Kofi Annan International Peacekeeping Training Centre (KAIPTC), with support from multiple international partners and a volunteer facilitator faculty, administered the pilot course for military and civilian health officers involved in U.N. peacekeeping missions entitled, “Comprehensive Medical Support in Complex Emergencies (CMSCE 19).” This brief review paper provides a description of the process in designing and delivering an interdisciplinary course for providers and decision makers responding to complex emergencies. We conclude with best practices and next steps for course evolution.

Traffic flow analysis is essential to develop smart urban mobility solutions. Although numerous tools have been proposed, they employ only a small number of parameters. To overcome this limitation, an edge computing solution is proposed based on nine traffic parameters, namely, vehicle count, direction, speed, and type, flow, peak hour factor, density, time headway, and distance headway. The proposed low-cost solution is easy to deploy and maintain. The sensor node is comprised of a Raspberry Pi 4, Pi camera, Intel Movidius Neural Compute Stick 2, Xiaomi MI Power Bank, and Zong 4G Bolt+. Pre-trained models from the OpenVINO Toolkit are employed for vehicle detection and classification, and a centroid tracking algorithm is used to estimate vehicle speed. The measured traffic parameters are transmitted to the ThingSpeak cloud platform via 4G. The proposed solution was field-tested for one week (7 h/day), with approximately 10,000 vehicles per day. The count, classification, and speed accuracies obtained were 79.8%, 93.2%, and 82.9%, respectively. The sensor node can operate for approximately 8 h with a 10,000 mAh power bank and the required data bandwidth is 1.5 MB/h. The proposed edge computing solution overcomes the limitations of existing traffic monitoring systems and can work in hostile environments.

A macroscopic model based on analogies with Little’s Law is proposed. The relaxation time is employed to characterize changes in density. This change is large for a small relaxation time and large interactions between vehicles occur. The relaxation time encompasses driver behavior which includes the time to perceive and process traffic situations and the resulting actions. Thus, it incorporates the perception and reaction time of a driver. The proposed model is evaluated for a transition caused by a bottleneck on a road and is compared with Zhang model. Performance results are presented which show that traffic evolution with the proposed model is more realistic than with the Zhang model.

Road surface wear leads to the formation of cracks and holes known as potholes. Potholes disrupt the smooth flow of traffic and can lead to accidents. The Intelligent Driver (ID) model is commonly employed but it assumes uniform traffic behavior for all conditions. This oversimplified approach is unrealistic as it does not consider the impact of real-world factors such as potholes on traffic patterns. This paper proposes a microscopic traffic model to address the impact of these road surface irregularities on traffic. The effect of small, medium, and large conical potholes is investigated using fundamental diagrams for traffic flow and velocity. The results obtained indicate that the proposed model outperforms the ID model as it can more accurately characterize how potholes and driver sensitivity affect vehicle behavior.

A new microscopic traffic model is proposed that characterizes driver response according to reaction and sensitivity. Driver response in the intelligent driver (ID) model is based on a fixed acceleration exponent and so does not follow traffic physics. This inadequate characterization results in unrealistic traffic behavior. With the proposed model, drivers can be aggressive, sluggish, or typical. It is shown to be string stable, and for appropriate distance headway and velocity (speed), the traffic flow is smooth. Furthermore, the proposed model has better stability than the ID model because it is based on driver reaction and sensitivity, while the ID model is based on a fixed exponent. The ID and proposed models are evaluated on a circular road of length 1200 m with a platoon of 21 vehicles for 150 s. The results obtained show that the proposed model characterizes traffic more realistically than the ID model.

A microscopic traffic model is presented which employs differences in velocity to characterize driver behavior. The Intelligent Driver (ID) model is based on an acceleration constant which cannot capture different traffic conditions. Further, it is not based on traffic physics and so can produce inaccurate results. The proposed model is an improved ID model and both are evaluated on a 2000 m circular road. The results obtained show that the proposed model can appropriately characterize traffic flow and density. Further, the variations in flow and velocity are smoother than with the ID model. This is because the proposed model is based on actual traffic parameters rather than an unrealistic traffic exponent.

At T-junctions, a minor road meets a main road and merging vehicles from the minor road create traffic bottlenecks as significant interactions between vehicles occur. These interactions reduce road capacity resulting in congestion. The Lighthill-Whitham-Richards (LWR) model is widely employed to characterize traffic flow. However, this model only considers small changes in flow which can be inadequate for T-junctions. Thus, a new model is proposed which is based on driver response during transitions at these junctions. The proposed and LWR models are evaluated using the first order upwind scheme (FOUS). The Courant-Friedrich-Lewy (CFL) condition is employed to ensure model stability. Results are presented which show that the proposed model better characterizes traffic flow at T-junctions.

The anisotropic Zheng and Jiang models characterize traffic with a constant rearward velocity. They also consider a constant driver sensitivity which can result in unrealistic behavior. In this paper, a new anisotropic model based on a variable rearward velocity is proposed. The transition width, driver reaction, and sensitivity at transitions are used to characterize this velocity. This width impacts traffic alignment with forward conditions and driver reaction can be aggressive, slow or typical. The performance of the proposed, Zheng and Jiang models is evaluated over a 2000 m circular (ring) road with an inactive traffic bottleneck. Results are presented which show that traffic evolution with the proposed model is more realistic than with the Zheng and Jiang models.

In this paper, the security of two-way relay communications in the presence of a passive eavesdropper is investigated. Two users communicate via a relay that depends solely on energy harvesting to amplify and forward the received signals. Time switching is employed at the relay to harvest energy and obtain user information. A friendly jammer is utilized to hinder the eavesdropping from wiretapping the information signal. The eavesdropper employs maximal ratio combining and selection combining to improve the signal-to-noise ratio of the wiretapped signals. Geometric programming (GP) is used to maximize the secrecy capacity of the system by jointly optimizing the time switching ratio of the relay and transmit power of the two users and jammer. The impact of imperfect channel state information at the eavesdropper for the links between the eavesdropper and the other nodes is determined. Further, the secrecy capacity when the jamming signal is not perfectly cancelled at the relay is examined. The secrecy capacity is shown to be greater with a jammer compared to the case without a jammer. The effect of the relay, jammer, and eavesdropper locations on the secrecy capacity is also studied. It is shown that the secrecy capacity is greatest when the relay is at the midpoint between the users. The closer the jammer is to the eavesdropper, the higher the secrecy capacity as the shorter distance decreases the signal-to-noise ratio of the jammer.