For biomechanical consideration of dental implants, an understanding of the three-dimensional (3D) load exerted on the implant is essential, but little information is available on the in vivo load, including the measuring devices.This review aimed to evaluate studies that used specific load-measuring devices that could be mounted on an implant to measure the functional load in vivo.An electronic search utilizing the internet research databases PubMed, Google Scholar, and Scopus was performed. The articles were chosen by two authors based on the inclusion and exclusion criteria.In all, 132 studies were selected from the database search, and 16 were selected from a manual search. Twenty-three studies were finally included in this review after a complete full-text evaluation. Eleven studies were related to the force measurements using the strain gauges, and 12 were related to the piezoelectric force transducer. The principles of the two types of devices were completely different, but the devices produced comparable outcomes. The dynamics of the load magnitude and direction on the implant during function were clarified, although the number of participants in each study was small.The load exerted on the implant during function was precisely measured in vivo using specific measuring devices, such as strain gauges or piezoelectric force transducers. The in vivo load data enable us to determine the actual biomechanical status in more detail, which might be useful for optimization of the implant prosthetic design and development of related materials. Due to the limited data and difficulty of in vivo measurements, the development of a new, simpler force measurement device and method might be necessary.The online version contains supplementary material available at 10.1186/s40729-022-00454-y. The excessive load exerted on an implant has been considered one of the primary factors causing peri-implant bone resorption, and the harmfulness of so-called “overload” has been discussed since the 1990s . ThereafOn the other hand, an appropriate magnitude of load is generally essential to maintain or increase bone density , 8. IndeNamely, it is essential to know the actual load exerted on the implant during relevant activities to better elucidate the influence of load on peri-implant bone remodelling. Several devices have been used both clinically and experimentally to measure the actual load on the implant in vivo . In mostA specific high-precision small load-measuring device must be installed into the structure of implant-supported prosthetic devices to measure the load on the implant during various oral functions. Furthermore, the measuring device itself must be in a form that does not impair oral function so that the functional load on the implant can be measured in real time. Normally, engineering expertise is indispensable for developing such high-precision devices. Therefore, load measurements on implants in vivo have been very challenging, and few studies have been carried out. This study reviewed the articles that performed in vivo measurements of the load imposed on implants using specific devices and summarized the measured load data and clinical findings; these data will be useful in clinical practice and related biomechanical and mechanobiological research.An electronic search was performed utilizing the internet research databases “PubMed”, “Google Scholar”, and “Scopus”. The search terms were constructed from the following terms:  OR (Abutment)) AND ((Bite force) OR  OR (Loading) OR (Mastication)) AND (In vivo) AND (Measurement)). A manual search was performed in addition to these database searches by checking the bibliography of all identified articles for potentially relevant additional studies. The search and selection process were concluded on 14 March 2022.The inclusion criteria established that the studies must record an in vivo load measurement and involve the usage of a force measuring device designed to measure force on each implant prosthesis. Additionally, the full text of the studies was required to be available in English. Studies were excluded from this review if they were in vitro or animal studies and used the methodology involving the external biting-type load-measuring devices, or the full text was not available.Figure A total of 132 studies were matched with the search terms, and 16 studies were selected from the manual hand search results by two authors (IA and NY). The abstracts and titles of all articles were screened. Unrelated titles and articles with nonclinical force measurement experimental methodology were excluded.n = 125) in the review after reading the full texts and reasons for exclusion are listed in Additional file Finally, 23 studies were selected (by IA and NY) for inclusion in the present review. Any discrepancies in the results from the two authors were discussed with other authors (MI and TS). It was found that either strain gauges or a piezoelectric force transducer was utilized as a tool to measure the load on the implant in all selected studies. Eleven studies were related to force measurements using strain gauges, and 12 were related to measurements using the piezoelectric force transducer. Studies not included  with the in vivo load measurement system from Tohoku University in Japan. The authors , 34 inveAmong various studies regarding in vivo measurement of load on implants, this review paper focused on those in which the load was measured during function, such as clenching and mastication, in a natural occlusal condition by incorporating a specific sensing device into the implant components or superstructure. Due to their suitable size, high accuracy, wide measuring range, and high adaptability, both strain gauge-based devices and piezoelectric force transducers have been utilized for such measurements thus far.For the measurement using the strain gauges, multiple strain gauge units are needed to acquire an accurate load pattern. Glantz et al.  used theLoad data measured in vivo that quantitatively demonstrated the changes in the load magnitude and direction in real time enabled us to know the actual biomechanical status during oral function in more detail. In the 3-unit implant-supported fixed prosthesis, for example, a remarkably large load, greater than 600 N, was exerted on the implant adjacent to the pontic when using the cantilevered bridge-type superstructure during gum chewing, although the patient was an elderly woman . Ducky eOn the other hand, investigating the behaviour of the load on the implant compared to that on the natural tooth with the periodontal ligament is also important. Bassit et al.  revealedThe measured force on the implant may be affected by not only the tasks but also individual factors, such as facial morphology or masticatory muscle force. In vivo load measurement will be valuable to understand the effects of individual factors on the loads. Considering the large individual differences in human bite force , the widTo date, the durability of the implant components against occlusal load and force-related peri-implant bone changes have been verified in numerous experimental studies . Most ofSome studies utilized in vivo measured load data for in silico simulation studies. Bing et al.  reportedA common limitation of the in vivo measurement experiments in any study introduced in this review was the small number of human participants. This is thought to be due to the cost of preparing each load-measuring device. Additionally, mounting the device to the implant components or superstructure for measuring the load under the natural occlusal condition, unlike the method of just biting the device, such as the Dental prescale, takes a large amount of time and requires great effort. Elaborate parts and precise work are essential for accurate measurement devices, which are challenging to prepare in large numbers. Difficulty securing participants was also expected. New measurement methods that replace the previous ones using strain gauges or piezoelectric transducers introduced in this review will thus be needed. We were unable to statistically analyse the loading status on implant because of the high degree of heterogeneity in load-measuring methods and the tasks performed during the measurement. Furthermore, the load-measuring devices introduced in this review are incapable of measuring the stress distribution not only in the implant components but also in the peri-implant bone as an important effect of the loading on living organisms. However, as mentioned above, the actual in vivo load data should be useful for applications in computational aided engineering methods such as FEA for investigating those stress distributions.An understanding of the functional load exerted on dental implants is essential to obtain long-term favourable treatment outcomes. This review demonstrated that for the in vivo measurement of the load on implants, attaching strain gauges to the implant components and using piezoelectric force transducers have been the mainstream methods. Although both approaches have demonstrated the actual measured load on the implant during function, 3D quantitative load measurement could be carried out by using the method with a piezoelectric transducer. The development of new measurement methods is also expected to accumulate much more patient data.Additional file 1: Table S1. Studies not included (n=125) in the review after full text reading and reason for exclusion.


Traditional pottery identification methods are time consuming and costly. In order to cater to more pottery industry needs, we propose a Mask R-CNN-based pottery identification method to build an automatic pottery identification system. We first improve the loss function of Mask R-CNN by using generalized intersection over union loss function, through the pattern of migration learning, to compensate for the disadvantage of a small collective amount of pottery data. For different types of pottery, we use the mask algorithm to enhance the features of the outer contour of the pottery. In addition, we use the minimum external matrix algorithm to accurately extract the outer contour bit pose features of pottery to improve the model's accuracy in recognizing the outer contour of pottery. To meet the testing conditions of pottery, with the support of potters and archaeologists, we make our pottery data set according to pottery categories. The experimental results prove that our method performs best in the comprehensive recognition accuracy of pottery, with the recognition accuracy above 90%. The recognition accuracy is also the best in pottery color decoration and grain decoration, and the grain recognition accuracy stays above 87%, which is better than other pottery recognition methods. Pottery is an important point in the history of human civilization. As early as the Neolithic era, primitive ancestors had mastered the art of kneading and firing pottery from clay. With the development of human civilization, ancient people continued to research and advance pottery technology, people experimented with the selection of clay types and the mixing ratio with water, and the shape of pottery evolved from practical cooking utensils to works of art. According to archaeologists, for the primitive ancestors, the production and use of pottery were mainly around the development of household items, and in later historical evolution the pottery that was buried in the soil for a long time still maintain the original appearance. The biggest advantage of pottery is that it can be used for a long time and is not easily graded in clay. So much so that pottery was mass produced as a craft in ancient times. After a long history, it has been preserved intact. What has been preserved in pottery is not only the skillful pottery of the generation of craftsmen but also the history and culture of the dynasty. The pottery provides a valuable reference for later archaeologists and antique enthusiasts.According to historical records, in the development of ancient pottery, the ancients had an in-depth study of the materials, proportions, fire, and pottery art. The study of pottery in ancient times was a knowledge covering analytical chemistry, literature, art and art, sculpture, handicraft, and so on , 2. AnciFrom the appearance of the pottery for quality and age judgment is the first stage of pottery identification, a more professional way to identify pottery is to start with the pottery material analysis. In order to obtain more comprehensive information on pottery, archaeologists resort to chemical analysis of pottery materials or physical processing methods. This has significant advantages for the quality analysis of pottery shards, and researchers mostly use multivariate calibration method and metrological analysis in the pottery material analysis data. Such delicate research work is a must for archaeologists, and for the amateur pottery enthusiasts, these pottery identification works are too costly and of low applicability , 5. To mWe refer to different neural network models, and finally, we propose a Mask R- –based pottery recognition method to build a pottery automatic recognition system. We improve the loss function of Mask R- by using generalized intersection over union loss function, through the pattern of migration learning, to compensate for the disadvantage of a small collective amount of pottery data. For different types of pottery, we use the mask algorithm to enhance the features of the outer contour of the pottery. To meet the test conditions of pottery, we make our pottery data set according to pottery categories with the support of potters and archaeologists. Finally, we experimentally demonstrate the effectiveness of our method.The rest of the paper is organized as follows. The style and texture of pottery are one of the important sources of information about pottery, and the social customs and trade information of the time can be determined based on the appearance and shape of the pottery. In China's Tang Dynasty, potters would carefully select different clays according to the needs of their crafts. Pottery fired from different clays varied greatly in appearance and texture, which is a source of information for archaeologists to identify pottery. The sculptural styles pursued by potters varied from period to period, and the art of hand relief carving took on different styles with the rise and fall of dynasties. In the opinion of decorative researchers, the relief style of pottery represents the artistic trends of the time, which is also a great source of information for pottery identification. By the 1870s, the pottery industry began to take shape, and pottery research was gradually increasing, with pottery analysis and research becoming a popular industry. The traditional pottery identification industry relied heavily on experienced pottery experts, potters, and archaeologists. They use visual perception and empirical judgments to identify types of pottery, techniques, and chronological information. Traditional methods of pottery identification are time consuming and costly. To cater to the needs of more pottery industries, researchers have tried to adopt artificial intelligence to identify pottery information automatically and gradually started to build a pottery feature database to complete the pottery inventory and classification tasks by matching the feature database with additional samples. Considering that pottery artifacts are affected by external environmental factors in the process of preservation, the problem of degradation of pottery relief patterns and its texture occurs. Some researchers try to use deep learning methods to ensure the robustness of the pottery identification system. Some researchers have tried to build a pottery textual feature database by 3D scanning technology and classify the feature database by manual labeling. Some researchers try to seek the relevant factors for pottery classification from different neural network combination strategies, to enhance the sensitivity of neural networks to pottery pattern features.To reduce the cost of artifact classification and inventory work, researchers in the literature  proposedResearchers in the literature  found inThere are many different types of pottery, from domestic pottery items to pottery craft artworks, each with a unique pattern of decoration. The design of specialized pottery identification models for different categories of pottery is the mainstream research philosophy of researchers today. To ensure a faster pottery classification at a later stage, each pottery is manually labeled at the pottery laser scanning stage, and the source of the labeled information behind the pottery is directly accessible when extracting the pottery 3D model. Researchers in the literature  were insWe investigated many neural network algorithms and performed experimental validation, and finally, we chose Mask R-CNN as the network base. Mask R-CNN belongs to the instance segmentation algorithm, and in pottery category identification, Mask R-CNN can perform pottery feature acquisition from pixel level and also instance segmentation from the pottery 3D model and take different colors to mask over the target features . Mask R-Mask R-CNN is obtained by optimizing based on R-CNN, which is a network to mine more feature information from the target, and the authors use a region generation network to build a feature pyramid. To prevent overfitting by stacking the network too much, Mask R-CNN borrowed the VGG network proposed by Google, and the authors proposed the ResNet network based on the VGG network with the adaptive improvement of Mask R-CNN, which has the biggest advantage of using ROIAlign instead of the region of interest pooling. The structure of the Mask R-CNN network is shown in K anchor frames of different sizes accordingly, and each anchor frame will correspond to a background label independently. When the intersection ratio between the target real frame and the predicted frame is greater than the predefined maximum threshold, the predicted frame will be considered as the foreground. When the intersection ratio is less than the predefined minimum threshold, it will be considered as the background. When the intersection ratio is between the maximum and minimum thresholds, the prediction frame is considered the no-target case. The final output of RPN has 4K dimensions of location information and 2K dimensions of final output feature information. R-CNThe region proposal network (RPN) is the core of the Mask R-CNN network. Its network structure is shown in Each target prediction box generated by the RPN network will exist in the pattern of x, y, w, h], where x and y denote the pixel coordinates of the top left corner of the anchor box, w denotes the width of the anchor box, and h denotes the height of the anchor box. After the initial prediction box is feature mapped, the final prediction box then has the following mathematical expression.If the pixel position of each anchor box needs to be determined, it needs to be calculated using the border regression algorithm, and the area between the prediction box and the real box obtained by the algorithm is the prediction generation area of the target. Each box has corresponding parameter coordinates [d∗(P) denotes the initial predicted value and the true value is known, assuming d∗(P)=w∗TΦ5(P), Φ5(P) denotes the feature vector of the input initial value, and w represents the parameter to be learned, and we also use the least-squares method to reduce the gap between the predicted value and the true value.In the feature extraction process of the RPN network, the prediction frame will be automatically scaled according to the size of the real frame, and then the translation convolution will be calculated.W is obtained by bringing LOSS to a minimum value. The expression k of the ROI region can be obtained through the RPN network.k0 denotes the layer where the ROI with area w × h = 224 × 224 is located. In addition, the value of k needs to be kept as an integer so that the large-scale ROI can extract large target features smoothly even on the low-resolution feature map, and the small-scale ROI can extract small target features from the high-resolution feature map.Lcls denotes the classification loss function, Lbox denotes the regression loss function, and Lmask denotes the mask loss function. Each independent loss function uses the L1 function and L2 function in predicting the edges and the true edges. Most of the loss functions take the cross-merge ratio as a constraint for calculating the overlap value between the prediction frame and the real frame, but the cross-merge ratio cannot reflect the form of overlap between the prediction frame and the real frame, resulting in the distance between the two cannot be accurately estimated, so we propose the GIOU method, which is calculated as shown below.The loss function of the Mask R-CNN network contains the classification loss function and regression loss function. The loss function of the Mask R-CNN model has an additional mask branch compared to the R-CNN network, and the mathematical expression is as follows:A and B. C denotes the smallest closed object from the overlapping part of A and B. Generalized intersection over union (GIOU) is similar to the calculation of IOU with scale invariance. The value of IOU is always greater than the value of GIOU. When the prediction frame is far away from the real frame, the merge between them increases and GIOU decreases. The problem of not being able to judge the relative position between the predicted frame and the real frame can be solved by the way of this and the other.Assume two arbitrary polygons D(S) denotes the Source domain, D(t) denotes the Target domain, x denotes the feature interval of the domain, and P(x) represents the edge probability distribution corresponding to the feature space. We adopt the pottery data set as the auxiliary sample Xa and the pottery enhancement data set as the spatial sample Xb and assume that Y = {0, 1, 2, 3}, which contains 4 types of pottery as samples, the training data T and the test data S satisfy the following equations.T is divided into two subsets Ta and Tb. xit ∈ Xb, where i = 1, 2,…, k, xia ∈ Xa, where i = 1, 2,…, n,xjb ∈ Xb, where j = 1, 2,…, m. In the migration learning design of the pottery recognition network, we first normalize the pottery data set so that the data set satisfies a linear distribution. Then we update the sample weights of the pottery data set by using the public data set as network pretraining and comparing the pottery training results for error rate reset. For the correctly trained samples, we reduce the correlation weights, and for the incorrectly trained samples, we take the means of weight superposition. All pottery samples are updated iteratively until the error rate reaches within the specified range. In the pretraining process, we use ResNet as the base network to train the pottery for a phase of parameter calibration, adjust the parameters, and set the weights for the Mask R-CNN network according to the training results [Different deep neural networks have different training settings and different data set training durations. Our research focuses on the type identification of pottery, and pottery database construction is a huge project. The collective amount of pottery data that has been constructed at present is not large and the number of samples is limited. To verify the effectiveness of our method in a limited sample, we proposed a transfer learning method. We also refer to the migration model of feature parameters mentioned in the literature , which r results . The detReferring to a large number of pottery recognition studies, we choose to base on the Mask R-CNN network. Based on this, we introduced the GIOU loss function to enhance the extraction ability of the model on the generalized features of the target, and we chose Compute Unified Device Architecture to accelerate the model computation ability. To improve the pottery identification system, we added a laser scanner as the data source center of computer vision. The pottery scanning data samples are convolved to get the pyramid feature layer, and then the candidate regions are extracted on the feature layer, and the candidate regions are pooled and convolved to extract features. We introduce the GIOU loss function after the pooling layer and set 3 threshold criteria for the GIOU loss function and finally get the type features, grain location, and shape features of pottery by iterative training of pottery feature update and CUDA model acceleration. The model gets the pottery plane position after recognizing the pottery, but the laser scanner input is the three-dimensional information of the pottery, the pottery carving information requires spatial position features and angle features, and the bit pose estimation method mainly collects the pottery texture, edge shape, and other features. In the real environment, the imaging quality will be degraded due to factors such as lighting and texture, and the above geometric feature-based method is susceptible to great influence.After the traditional deep learning target detection algorithm recognizes the pottery, it will be seriously affected by the lighting, texture, and complex background, etc., which leads to the difficulty of edge extraction and has a big impact on the pottery 3D feature prediction because there is no reinforcement of the edge information. Our method can also perform pixel-level feature segmentation for pottery in a complex environment using the mask algorithm. The mask reinforces the pottery edge features and can better perform the task of locating the pottery 3D boundaries, and the edges of the already segmented image are extracted using the mask to generate pixel-level masks. We extract the mask obtained from the algorithm separately to generate the mask map and use the minimum external moment algorithm to accurately extract the pottery angle information, which can be used with the coordinate information obtained from the target recognition to obtain the pottery carving texture features . The detThere is no systematic pottery data set in the current pottery identification research.To validate our pottery identification method, we made our pottery data set with the support of potters and archaeologists. To standardize the types of pottery, we set five major pottery categories at the early stage of pottery data set production, namely color pottery, faience, painted pottery, stamped pottery, and glazed pottery. Color pottery is mainly made of clay, ming, and material mixed with water in appropriate proportions, then shaped, dried, and fired. Faience is pottery made with iron oxide and manganese oxide as coloring elements. Painted pottery is pottery made by painting colors on the finished product after firing. Stamped pottery is pottery made by laying the clay on a bamboo mat or linen cloth and printing a pattern or cloth pattern. Glazed pottery is a compound of lead as an accelerant, after several firings of pottery, pottery external glaze is not only colorful but also not easy to fall off, commonly used in the language of crafts pottery production. According to the five major categories of pottery, we made different pottery data sets, respectively. The detailed data set information is shown in To ensure the independence and stability of the pottery identification system, we configured an independent host computer for the system, and all the integrated systems were developed with the host computer as the platform. In our experiments, we mainly configured the experimental environment with the Anaconda system. Considering the different requirements of the vision system and the pottery identification system for the programming environment, we configured multiple programming environments on the host computer to suit different needs. In the construction of the pottery identification neural network, we mainly use TensorFlow as the main framework. With the support of TensorFlow's powerful community module, our pottery identification network can be successfully built. The detailed training parameters are shown in R) and precision (P) as general evaluation metrics. Where XTP denotes correctly identified pottery, XFN denotes unidentified pottery, and XFP denotes misclassified pottery. The detailed mathematical calculation is shown below.For the method testing efficiency metrics, we chose recall (To verify the effectiveness of our pottery recognition system, we compared machine learning methods and deep learning methods. Among the machine learning methods, we chose the SVM  algorithXTP in the experiment denotes the recognition efficiency per 500 pottery samples. From the experimental results in The From the experimental results of the second stage, it is clear that the recognition accuracy of traditional machine learning methods in various types of pottery is between 65% and 76%, which does not meet the practical needs in pottery classification work. Both SSD and Faster R-CNN methods keep the pottery recognition accuracy between 80% and 90%, and the pottery recognition efficiency is more stable. Our methods both maintain above 90% in pottery recognition accuracy, which proves the superiority of our methods in the second phase of experimental validation.In the third phase of the experiment, to verify the recognition accuracy of the pottery identification system for three-dimensional pattern decoration and color decoration, we divided the pottery into two categories: pattern-decorated pottery and color-decorated pottery, where pattern-decorated pottery contains stamped and glazed pottery, and the remaining three categories are regarded as color-decorated pottery. The experimental results are shown in From the experimental results of the third stage, it is clear that all the methods have higher recognition accuracy in color pottery decoration than in pattern pottery decoration. Because patterned pottery decoration requires high 3D features, the traditional SVM method cannot capture pottery 3D features. SSD and Faster R-CNN outperform the SVM method in capturing 3D features, but still need to be optimized. Our method is the best performer in both color pottery decoration recognition and pattern pottery decoration recognition, and the pottery recognition accuracy is better than SSD and Faster R-CNN. It proves the effectiveness of our method in the third stage of experimental validation.In this paper, we propose a Mask R-CNN-based pottery recognition method and build a pottery automatic recognition system. We first improve the loss function of Mask R-CNN by using generalized intersection over union loss function, through the pattern of migration learning, to compensate for the disadvantage of a small collective amount of pottery data. For different types of pottery, we use the mask algorithm to enhance the features of the outer contour of the pottery. In addition, we use the minimum external matrix algorithm to accurately extract the outer contour bit pose features of pottery to improve the model's accuracy in recognizing the outer contour of pottery. To meet the testing conditions of pottery, with the support of potters and archaeologists, we make our pottery data set according to pottery categories. The experimental results prove that our method performs best in the comprehensive recognition accuracy of pottery, with the recognition accuracy above 90%. The recognition accuracy is also the best in pottery color decoration and grain decoration, and the grain recognition accuracy is maintained above 87%. It proves that our method performs well in both feature capture at the two-dimensional level and three-dimensional level of pottery. Compared with traditional machine learning methods and deep learning methods, our method has higher recognition accuracy and better stability.Compared with machine learning methods and deep learning methods, our method still has much room for improvement in recognition accuracy and recognition efficiency. Although it performs the best in the experiment of grain decorated pottery recognition. In future research, we will try to add a generative adversarial neural network as an auxiliary classification in the adversarial network to optimize the recognition of spatial pattern features during pottery pattern feature segmentation and improve the robustness and generalization of the network.


With an estimated population of 8.3 million, Sierra Leone has no existing pharmaceutical manufacturing company at present. The recent COVID-19 pandemic brought to the limelight the fragility and weakness of the Sierra Leonean pharmaceutical industry as it resulted in severe drug shortages and medicine insecurity in the country due to the restrictions imposed on importations, which validates the uncertainties if the country can sufficiently meet its local pharmaceutical needs and health demands. In this paper, we highlight the current situation of the Sierra Leonean pharmaceutical sector and provide recommendations for the country towards building a functional pharmaceutical industry based on lessons learnt from the COVID-19 pandemic. The pharmaceutical industry manufactures medicines, medical devices, and diagnostic tools for treating or preventing diseases. The global market value of the pharmaceutical sector is about US $1.42 trillion, making it a significantly huge industry [For decades, the African continent has been confronted with the problem of severe drug shortages. According to the World Health Organization (WHO), about half of the total population in the continent do not have regular access to even the most important medicines . This fuIn total, there are about 375 pharmaceutical companies in the continent, mainly in North Africa, and serving about 1.3 billion people . HoweverWhile the spotlight has always been centered on the African continent as a whole, current developments in the pharmaceutical industry reinforce the need to focus on country-specific measures and roadmaps in a bid to provide further insights and understanding on how functional the pharmaceutical industry of every country in the continent is in meeting domestic health needs, targets and priorities. One of such countries worth exploring is Sierra Leone.With an estimated population of 8.3 million, Sierra Leone is a West African country along the Atlantic Ocean, and adjacent to Liberia and Guinea . A recenBefore the advent of the COVID-19 pandemic in Africa . During The response of the Sierra Leonean government to the COVID-19 pandemic was swift and decisive . HoweverTax incentives must be provided to the country’s big importers to establish a local pharmaceutical industry.Importers who have their pharmaceutical companies in countries like Nigeria and Ghana, must be encouraged to open sub-factory outlets in the country for local manufacturing.A memorandum of understanding should be drawn up between the National Medicines Supply Agency in Sierra Leone and the proposed new pharmaceutical industry that they would be purchasing their products from as long as quality is assured.Government land in the outskirts of the industrial cities should be provided for the proposed pharmaceutical industry.Due to the experiences associated with the COVID-19 pandemic, national governments throughout the African continent have demonstrated their political will to prioritize local pharmaceutical manufacturers over foreign manufacturers and suppliers, as long as they can sufficiently meet the needs and demands of the local health system. It is now widely recognized that establishing a functional local pharmaceutical industry can boost economic growth, provide employment, promote research, and improve access to medicines and pharmaceutical products in the case of potential disease outbreaks. As a result, a rise in foreign pharmaceutical companies looking to establish themselves in African countries to provide drugs and other essential medical commodities has taken place. This provides an incentive for Sierra Leone to key into this untapped potential and work towards achieving self-sufficiency in the manufacture of medicines and pharmaceutical products via the following recommendations:No health system can survive without a functional pharmaceutical industry, as access to essential medicines and pharmaceutical products are a cannot-do-without. Sierra Leone must, therefore, prioritize the establishment of a functional pharmaceutical industry based on lessons learnt from the recent COVID-19 pandemic. The recommendations provided in the context of this paper should be explored by the national government in order to safeguard the integrity and ensure self-sufficiency of the Sierra Leone pharmaceutical sector.


Due to the recent advances in the domain of smart agriculture as a result of integrating traditional agriculture and the latest information technologies including the Internet of Things (IoT), cloud computing, and artificial intelligence (AI), there is an urgent need to address the information security-related issues and challenges in this field. In this article, we propose the integration of lightweight cryptography techniques into the IoT ecosystem for smart agriculture to meet the requirements of resource-constrained IoT devices. Moreover, we investigate the adoption of a lightweight encryption protocol, namely, the Expeditious Cipher (X-cipher), to create a secure channel between the sensing layer and the broker in the Message Queue Telemetry Transport (MQTT) protocol as well as a secure channel between the broker and its subscribers. Our case study focuses on smart irrigation systems, and the MQTT protocol is deployed as the application messaging protocol in these systems. Smart irrigation strives to decrease the misuse of natural resources by enhancing the efficiency of agricultural irrigation. This secure channel is utilized to eliminate the main security threat in precision agriculture by protecting sensors’ published data from eavesdropping and theft, as well as from unauthorized changes to sensitive data that can negatively impact crops’ development. In addition, the secure channel protects the irrigation decisions made by the data analytics (DA) entity regarding the irrigation time and the quantity of water that is returned to actuators from any alteration. Performance evaluation of our chosen lightweight encryption protocol revealed an improvement in terms of power consumption, execution time, and required memory usage when compared with the Advanced Encryption Standard (AES). Moreover, the selected lightweight encryption protocol outperforms the PRESENT lightweight encryption protocol in terms of throughput and memory usage. An increasing number of research studies on smart agriculture has been motivated by several challenges. First, the explosive growth of the world population . AccordiPrecision agriculture integrates wireless sensor networks (WSNs) with traditional agriculture to improve crop yields. This is carried out by using a large number of low-power multi-function wireless communication sensors to remotely monitor the farmland to collect environmental data, crop growth data, and livestock health data to guarantee a reduction in the possible threats to the production process and help farmers make better decisions. Recently, there is a shift from the usage of WSNs for smart agriculture to the IoT as the main enabling technology for smart agriculture. The IoT combines several technologies such as radio frequency identification, wireless sensor networks, middleware systems, end-user applications, and cloud computing.In ,3, the aFurthermore, smart irrigation is a precision agriculture application  that aimIn , the autIn , the autMoreover, smart irrigation systems developed based on IoT technology consist of constrained nodes in terms of power, memory, and processing resources. As a result, conventional security protocols can not be supported in such systems ,21. TranOn the other hand, the Message Queue Telemetry Transport (MQTT) protocol has been widely deployed as an application layer messaging and information exchange protocol in machine-to-machine (M2M) communication . This isMotivated by the increasing importance of smart irrigation systems in conserving water as a scarce natural resource, the role of precision agriculture in agriculture development, and the urgent need to apply information security techniques in the IoT part of the smart agriculture ecosystem, our aim in this research is to integrate a lightweight cryptography layer into the IoT ecosystem for smart agriculture and to investigate the deployment of a lightweight encryption protocol (the Expeditious Cipher) to create a secure channel between the sensing layer and the broker of MQTT protocol as well as between the broker and its subscribers in smart irrigation systems. This secure channel protects the sensors’ published sensitive data from eavesdropping and theft and preserves the integrity of data, in addition to protection of the decision that is made by the DA entity and returned to actuators. It should be noted that the security in IoT-based systems lies in IoT local systems consisting of devices constrained in energy and computing power.The following points summarize the main contributions of this article.The main contribution of this article is the integration of a lightweight cryptography layer to the IoT ecosystem for smart agriculture that meets the requirements of constrained devices used in smart agriculture in general and specifically, in our proposed IoT-based irrigation system.The article investigates the deployment of a lightweight encryption protocol (Expeditious Cipher (X-cipher)) to create a secure channel between the sensing layer and the broker in the MQTT protocol as well as a secure channel between the broker and its subscribers in smart irrigation systems (our case study).The proposed model is evaluated through simulation to validate the lightweight property of the chosen encryption protocol in terms of power consumption, execution time, and memory usage. Moreover, a performance comparison is carried out between the Expeditious Cipher (X-cipher), AES, and PRESENT cipher (lightweight standard protocol) in terms of power consumption, execution time, memory usage, and average throughput.The security requirements of IoT-based agriculture systems and the potential attacks against them are discussed.A state-of-the-art lightweight security architectures proposed for securing MQTT protocol is reviewed after highlighting the concept of lightweight cryptography.The remainder of the article is organized as follows: In this section, the proposed secure IoT-based irrigation system architecture is outlined. First, a brief review of the proposed lightweight security techniques for securing the MQTT protocol is given. Then, the proposed model architecture and hardware components are explained in detail. In addition, the applicability of the selected lightweight protocol (X-Cipher) on NodeMCU is verified.MQTT runs over TCP with few security techniques such as simple authorization policies and basic authentication techniques. TLS/SSL with session key management has been suggested for securing MQTT; however, proposed security techniques for MQTT should be lightweight for deployment in IoT environments. TLS/SSL has been proven to have performance issues in terms of processing time, memory required, and energy consumption, in addition to the various possible attacks such as Heartbleed, CRIME, BEAST, and RC4. Furthermore, TLS does not provide fine-grained access control . MoreoveThe main objective of our proposed system is the addition of a lightweight cryptography layer to the agriculture IoT ecosystem, as shown in The sensing layer is made up of multiple low-power sensors that are spread on agricultural land to continuously collect soil temperature and humidity. The DHT11 sensors are used to measure the temperature value in degrees Celsius and the humidity value in percentage. The DHT-11 sensor has three pins: two of which are for power and one is for output data transmission. The three pins are used to connect the DHT11 sensor to the NodeMCU ESP8266 board. The DHT-11 library must be added to the Arduino IDE software before uploading to the NodeMCU ESP8266 board the code written to read the humidity and temperature value from the DHT11 sensor. Together, the DHT-11 sensor and the NodeMCU form the IoT node. The NodeMCU runs the MQTT client code and publishes the sensed soil parameters (the temperature and humidity readings) to the MQTT broker after encrypting the message using the Expeditious Cipher (X-cipher). The MQTT broker runs on Raspberry Pi, which is a low-power single-board computer. NodeMCU ESP8266 has a built-in WiFi module that is used as the communication protocol between NodeMCU and the Raspberry Pi. The MQTT protocol is chosen due to its short message transmission capability and low-bandwidth requirement, which makes it more convenient for machine-to-machine (M2M) communication. Moreover, the Raspberry Pi was selected as a processing layer because of its low cost and common use in IoT applications. Mosquitto is an open-source lightweight MQTT broker and is selected as it is suitable for Raspberry Pi. The Mosquitto broker decrypts the message and adds the information in the message to the related topic. Then, information is encrypted again before being published (broadcast) to all subscribers. The ThingSpeak cloud platform is used among subscribers that execute cloud analysis for making irrigation decisions regarding the time of irrigation and the quantity of water. Then the irrigation decision is published to the broker to be decrypted and then encrypted again before being published to the actuator. The nodeMCU at the actuator layer decrypts the message and then passes the control signal to the attached pump to activate the irrigation.In this layer, a lightweight protocol must be deployed, because the proposed IoT-based irrigation system is made up of constrained devices in terms of memory and energy. Characteristics of lightweight cryptography were highlighted in ISO/IEC 29192 and ISO/IEC JTC 1/SC 27. Lightweight properties are evaluated based on chip size and energy consumption, and small code and/or RAM size in case of software implementation . So, theThe communication between NodeMCU ESP8266 and the Raspberry Pi is achieved using WiFi, as the NodeMCU has a built-in WiFi module. On the other hand, the communication between the Raspberry Pi and the internet can be carried out using LoRaWAN technology taking into consideration the low data rate characteristic of LPWA technologies discussed in In this section, we validate through simulation that the chosen lightweight cipher satisfies the requirements of constrained devices in IoT-based smart irrigation systems in terms of power consumption and memory usage. Moreover, we compare the chosen lightweight cipher with PRESENT, a standard lightweight protocol, and AES, a standard encryption protocol.In this section, we compare the performance of the X-cipher versus the PRESENT ultra-lightweight cipher  presenteSmart irrigation systems integrate IoT technology with smart agriculture to conserve water consumption during the irrigation of agricultural land. This research focuses on the evaluation of the adoption of a lightweight security protocol to secure communication in smart irrigation systems. The adopted encryption algorithm (Expeditious Cipher) creates a secure channel between publishers and the broker of the MQTT protocol as well as between the broker and its subscribers. MQTT was chosen as the IoT application protocol for messaging in our proposed irrigation system, as it is a bandwidth- and energy-efficient protocol. The evaluated lightweight protocol reduces resource consumption in terms of memory usage and power consumption as well as minimizes the execution time, which makes it suitable for IoT-based resource-constrained systems. There is a trade-off between the security level and the memory and energy-processing resources required. AES achieves a higher security level at the cost of more memory usage and energy consumption, which causes performance degradation in constrained devices, opposite to lightweight cipher, which achieves an adequate level of security with low memory usage and reduced energy consumption. Comparing our selected X-cipher protocol with the PRESENT cipher reveals a lower memory usage and higher throughput in favor of the X-cipher. In the future, we will investigate the integration of our proposed system with software-defined networking (SDN) technology to implement the security models at the controller to offload publishers and brokers from the additional processing required by security algorithms. Moreover, we will develop a deep-learning model to predict the irrigation time and the required amount of water required for irrigation.


We investigate the rich potential of the multi-modal motions of electrostatically actuated asymmetric arch microbeams to design higher sensitivity and signal-to-noise ratio (SNR) inertial gas sensors. The sensors are made of fixed–fixed microbeams with an actuation electrode extending over one-half of the beam span in order to maximize the actuation of asymmetry. A nonlinear dynamic reduced-order model of the sensor is first developed and validated. It is then deployed to investigate the design of sensors that exploit the spatially complex and dynamically rich motions that arise due to veering and modal hybridization between the first symmetric and the first anti-symmetric modes of the beam. Specifically, we compare among the performance of four sensors implemented on a common platform using four detection mechanisms: classical frequency shift, conventional bifurcation, modal ratio, and differential capacitance. We find that frequency shift and conventional bifurcation sensors have comparable sensitivities. On the other hand, modal interactions within the veering range and modal hybridization beyond it offer opportunities for enhancing the sensitivity and SNR of bifurcation-based sensors. One method to achieve that is to use the modal ratio between the capacitances attributed to the symmetric and asymmetric modes as a detector, which increases the detection signal by three orders of magnitude compared to a conventional bifurcation sensor. We also present a novel sensing mechanism that exploits a rigid arm extending transversely from the arch beam mid-point and placed at equal distances between two side electrodes. It uses the asymmetry of the arch beam motions to induce rotary motions and realize a differential sensor. It is found to increase the detection signal by two orders of magnitude compared to a conventional bifurcation sensor. Micro-electro-mechanical systems (MEMS) based devices have demonstrated great capability in sensing applications, such as bio-mass ,2,3, gasSeveral detection mechanisms have been proposed to enhance the performance of electrostatic MEMS mass and gas sensors, including the activation of more sensitive higher-order modes via partial-length electrode actuation ,19,20,21Given the rich dynamics of electrostatically actuated arch microbeams, several studies have examined their potential use in sensing and actuation ,32,33,34Arch beams expand the range of stable motions under electrostatic actuation. This may be prove useful in sensing since it allows for the creation of stronger signals and potentially higher signal-to-noise ratios (SNR). Arch beams also give rise to crossing and veering phenomena between the symmetric and asymmetric modes, as the natural frequencies of symmetric modes increase with the initial rise while those of the asymmetric modes remain unaffected. While symmetric mechanical and electrical energy distributions across the sensor result in crossing between those modes, asymmetry results in veering between the modes. It can be introduced to the electrical field using partial electrodes with the limiting case being a half-length electrode. In this paper, we examine potential uses of half-length electrode arch microbeams in inertial gas sensing. First, we conduct a comprehensive nonlinear analysis of the sensor dynamics to examine the interactions between its symmetric and asymmetric modes. Based on that, we conduct a comparison among two traditional detection mechanisms and two novel mechanisms that exploit modal interactions between the first symmetric and the first anti-symmetric beam modes. The present work constitutes the first stage in a multi-phase multidisciplinary research program meant to investigate the feasibility of those novel gas detection mechanisms.l and width b, and thickness h. It is electrically actuated by a voltage drop applied between the beam and a side electrode. Different actuation configurations are considered to enable the activation of higher symmetric and anti-symmetric vibration modes. For instance, asymmetric actuation via electrode 1 or 2, so-called ‘half’ electrode configuration), enables the activation of the first anti-symmetric mode, whereas symmetric actuation via both electrodes would not be able to activate that mode. We note that the dynamic response of straight and arch microbeams under partial electrode actuation has been extensively studied in the literature [The gas sensor under study, terature ,32,35,36d is the initial gap distance between the line connecting the fixed supports and the electrode, The capacitance per unit length between the arch microbeam and the fixed half-length electrode can be written, including the fringing field, as (1)Cl=Hcx and over dots denote derivative with respect to the nondimensional time parameter t.Following Euler–Bernoulli beam assumptions, accounting for the mid-plane stretching and squeeze-film damping, and electrostatic force in Equation , the govThe function E is Young’s modulus, b and h are the width and thickness of the microbeam, and The nondimensional variables, axial position, transverse deflection, initial rise function and time, are respectively obtained by lettingThe ratio of the mass added by gas sorption to the beam mass is denoted c is the linear viscous damping coefficient, The nondimensional coefficients in Equation  are giveThe model predictions are validated experimentally. Specifically, the experimental study is carried out to investigate the impact of half-length electrode actuation on the response of the sensor, and whether it can activate interactions between symmetric and asymmetric modes. These interactions are the focus of this work and proposed sensing mechanisms. Schematics of the fixed-fixed beam under test, showing the side and top views of the device, are shown in The experimental setup in During half-electrode excitation, we actuate the left electrode at the natural frequency of the beam’s nth mode while grounding the right electrode and the beam. During full-electrode excitation, both bottom electrodes are actuated while the beam is grounded. In both case, the excitation amplitude is The LDV was used to scan the beam at a grid made of 50 equidistant and record the magnitude and phase of the velocity at each points. The data are used to construct the mode shape of the vibration. Unlike several previous works, the analysis of the mode shapes was carried out experimentally. For instance, Alcheikh et al.  presenteThe first and third modes of the beam are symmetric, while the second mode is antisymmetric. Symmetric modes can be obtained by either half-electrode or full-electrode excitation; We swept the excitation frequency in the vicinity of each mode’s natural frequency, over a duration of 20 ms using the voltage waveform We develop a nonlinear reduced-order model following the numerical procedure described in  to obtaiWith a view to using the model to investigate the feasibility and potential advantages of novel detection mechanisms, we start by deploying it to establish the basic response of the curved beam resonator. We consider a gas sensor made of an arch beam under electric actuation. Its specifications are given in We first examine the static response of the arched microbeam for the full- and half-electrode actuation configurations. The formulation of the static reduced-order model using the differential quadrature method (DQM) is given in In the subsequent analysis, the focus will be on half-electrode actuation to exploit symmetry switching for sensing purposes. The initial rise of the arched microbeam In this section, we conduct a free vibrations analysis of arch microbeams by solving the corresponding linear damped eigenvalue problem, Equation , using tWe plot in For comparison, we also show in mer case .We used the model to investigate the interaction between the mode shapes around the veering point under half-electrode actuation. ng point ,41. It iIn this section, we obtain the frequency–response curves of half-electrode actuated arch microbeams. The response orbits are evaluated following the numerical procedure described in . The modWe first determine the number of modes required for the convergence of the dynamic solution. In We show in i) bifurcations, where a Floquet multiplier exits the unit circle through +1; two Hopf bifurcations (HPi), where two complex conjugate multipliers exit the unit circle; and a period-doubling (PD) bifurcation, where a multiplier exits the unit circle through −1. The bifurcation points are marked accordingly on the mid-point curve. While the response of mode 1 shows evidence of softening, the response of modes 2 and 3 show evidence of hardening.For the low initial rise case of i, a third Hopf bifurcation HP3, and second (reverse) period-doubling bifurcation PD2 appearing on the merged primary resonance curve. The responses of the quarter-point and mid-point are similar and larger than that of the three quarters-point The primary and superharmonic resonances of modes 1 and 2 start to merge as the initial rise increases to The merger between modes 1 and 2 is complete at the veering point Beyond the veering point. As the natural frequency of mode 1 continues to increase beyond that of mode 2 with the initial rise, We examine the effectiveness of four plausible detection mechanisms for asymmetric arch beam sensors.First, we examine the traditional frequency shift detection mechanism. This is based on the shift in the frequency of a resonant peak due to changes in the mass of the microbeam in the presence of the target gas. We used the eigenvalue problem to determine the responsivity of modes 1 and 2 to added mass asi et al.  as(9)S=2 and CF6, under a drop in the natural frequency, due to added mass, to implement the sensor. Those bifurcations provide for a reusable sensor since they avoid jumps to pull-in, and a large qualitative detection signal generated by a sudden expansion in the orbit size.Bifurcation-based sensing mechanisms rely on a frequency shift a bifurcation point instead of a resonant peak and exploit the qualitative change in response beyond the bifurcation as a detector. We consider a sensor made of an arch microbeam with an initial rise of Conventional bifurcation-based sensors detect the presence or concentration of a gas by measuring a corresponding change in the orbit size subsequent to a jump from a lower to an upper branch of orbits. Capacitance-based transduction mechanisms are typically used for this purpose. The instantaneous capacitance of the sensor can be found in terms of the beam position with respect to the fixed electrode by integrating the capacitance per unit length, given by Equation , over thSince the measurand is the orbit size, rather than the instantaneous displacement, we evaluate the RMS of the capacitance over the orbit as a surrogate. We normalize the sensor capacitance with respect to its initial capacitance2 and CF6 bifurcations are examined. The sensors operate in the gray shaded regions of the figure with their excitation frequencies set to We show in We propose a novel detection method that exploits response asymmetry to amplify the detection signal (capacitance change) of bifurcation based sensors. The detector in this case is the ratio of the capacitance attributed to mode 1 to that attributed to mode 2 rather than the RMS of the total capacitance 2 and CF6 sensors described above. The figure shows the modal ratios of the stable orbits before and after gas detection  under this detection mechanism capacitance, we set the minimum detectable capacitance to Equation  as , bifurcation-based sensors can realize better sensitivity and/or higher SNR, whereas frequency shift sensors become inoperable as complex dynamics turn the regular (periodic) orbits present at smaller resonant peaks to quasiperiodic orbits, chaotic attractors, or unstable orbits at larger resonant peaks.The paper examined two novel gas detection mechanisms. The first uses the modal participation ratio of an arch beam. The second adds a transverse arm and uses differential capacitance measurements. The modal interactions within the veering range as well as the mode hybridization realized beyond that range offer opportunities to improve the sensitivity and SNR of bifurcation-based sensors. We demonstrated that a modal ratio-based detection method increases the size of the detection signal by three orders of magnitude from that of conventional bifurcation-based sensors. Another method to exploit those rich dynamics is to use the rotational motions induced by asymmetries in the sensor response to realize differential capacitance measurements. The latter was found to increase the signal level by by two orders of magnitude from that of conventional bifurcation-based sensors.Those detection methods allow bifurcation sensors to replace conventional capacitance-based transduction mechanisms with mechanisms that offer larger SNR. They also allow the sensors to operate in analog or binary modes. However, this class of detection methods need to be tailored to the specific modal interactions underlying the bifurcation; otherwise, they may degrade the SNR rather than improve it.Finally, we note that curved beam resonators require the use of a thick structural layer, which limits available fabrication processes to SOI and similar processes. It also poses challenges in the characterization of those sensors since classical vibrometers cannot detect in-plane motion, and electric measurements cannot differentiate between modes involved in 1:1 interactions since they have the same frequency-domain profile.


This paper revisits the debate around the link between population density and the severity of COVID-19 spread in the USA. We do so by conducting an empirical analysis based on graphical evidence, regression analysis and instrumental variable strategies borrowed from the agglomeration literature. Studying the period between the start of the epidemic and the beginning of the vaccination campaign at the end of 2020, we find that the cross-sectional relationship between density and COVID-19 deaths changed as the year evolved. Initially, denser counties experienced more COVID-19 deaths. Yet, by December, the relationship between COVID deaths and urban density was completely flat. This is consistent with evidence indicating density affected the timing of the outbreak—with denser locations more likely to have an early outbreak—yet had no influence on time-adjusted COVID-19 cases and deaths. Using data from Google, Facebook, the US Census and other sources, we investigate potential mechanisms behind these findings. Historically, cities have been associated with the propagation of infectious diseases.2The notion that dense cities would be hotbeds of virus transmission prompted a flurry of academic research on the topic. Initial studies—especially those looking at the United States’ experience—suggested urban density fostered a faster spread of the disease.4Now that massive vaccination campaigns have gradually reduced the threat of COVID-19 worldwide, can we draw any definitive conclusions about the mediating role of density in shaping the health impact of COVID-19 in cities? We turn to this question by looking at the evolution of the epidemic in the contiguous USA, in the period between the first registered cases in January 2020 and the beginning of the vaccination campaign in mid-December. By looking at the whole of 2020, we seek to understand how the results of initial studies indicating density was an important determinant of the impact of COVID-19 progressively led to more ambiguous findings as the pandemic evolved. Our empirical analysis combines descriptive evidence with an instrumental variable strategy borrowed from the agglomeration literature in economics. In doing so, our methodological approach avoids some of the pitfalls of conventional regression estimates and is close to methods that are familiar to both economists and economic geographers.We find convincing evidence that density affected the timing of the outbreak in each county, with denser locations more likely to have an early outbreak. We show this leads to an initially positive and significant relationship between the impact of COVID-19 and population density at the county level, consistent with the results of early studies on the spread of the virus in the USA. However, after adjusting for the timing of the onset of the disease in each county, we find no evidence that population density is positively associated with the impact of COVID-19. Interestingly, we find a negative relationship between density and the spread of COVID-19 within a county at the very beginning of an outbreak, but this relationship fades completely within 2 months. We also show that, by the end of 2020, density could no longer explain the cross-sectional pattern of accumulated cases or deaths. Dense locations were hit first, but, as the pandemic evolved, they were not hit harder. These results help us frame other studies on this topic and understand how the findings in that literature changed as the pandemic developed.The fact that—by the end of 2020—density had no effect on the local impact of COVID-19 appears counter-intuitive. The virus spreads via human contact and denser areas provide more opportunities for human interaction. Yet, this is not the only way in which density can affect the spread of disease. Several mediating factors can make the direction of this relationship theoretically ambiguous. We analyze social/behavioral factors that could explain our findings, bearing in mind that the spread of disease is a social as well as a biological phenomenon , that may also simultaneously affect local economic conditions. Insofar as the COVID-19 outbreak is affected by economic factors, unobservable locational advantages can confound the effect of density on the spread and severity of the disease. Second, differences in the timing of the onset of the disease can generate cross-sectional differences in the severity of the outbreak at one point in time in the absence of true differences in the local reproduction rate. Finally, data on COVID-19 cases might be reported with error due to variation in local testing strategy and capacity.geological IV approach, we use the presence of aquifers, earthquake risk, and soil drainage capacity to as instruments for density  strategies borrowed from the agglomeration literature in economics to induce plausibly exogenous variation in population density without affecting COVID-19 cases and deaths directly. More specifically, in our As discussed above, a number of papers have examined the link between density and COVID-19 incidence in the USA.Outbreak Sub-sample consists of 1441 counties representing Our dataset combines information on COVID-19 cases and deaths, population density, demographics, social connectedness, behavioral changes, voting behavior, healthcare provision, income and geological features at the US county level. We will use COVID data extending over the period between the 22nd of January, when the first US case was confirmed in King County, up until the 15th of December 2020, the day after the COVID vaccination campaign began in the USA. We restrict our sample to urban countiesfirst-wave, we refer to those taking place up to the 5th of July, which is the minimum in the moving average of deaths after April 2020.We obtain a panel of daily confirmed COVID-19 fatalities and cases for US counties from usafacts.org.Based on the US census for 2010, we compute two measures of population density. The first is simply the total population of a county over its total area. This will constitute the independent variable of interest throughout most of our analysis. The second variable takes the population density for all census-blocks within a county and computes the associated population-weighted mean. Population-weighted density is meant to measure average “experienced” density and was popularized in economics by Glaeser and Kahn  and RappFor our geological instrumental variable estimates, we use three different instruments. More specifically, we use variables measuring earthquake risks and presence of aquifers from the US Geological Survey (USGS) . This database aggregates extensive anonymized mobile device GPS user data and estimates the percentage change in activities  by county and day. The five week period from January 3rd to February 6th before the start of the COVID-19 outbreak in the US serves as the corresponding baseline period.We obtain data on county-level demographic characteristic estimates for 2018 from the US census. Social connectedness is measured with Facebook’s Social Connectedness Index (Facebook SCI), which captures the intensity of the link between locations using the number of friend links in this social network  results for COVID-19 spread in this section appear surprising given that the virus spreads via human contact and denser areas can provide more opportunities for human interactions. Nevertheless, there are several mediating factors that might offset this intuitive mechanism. For example, density itself might attract younger residents who are less likely to develop significant symptoms. In addition, both behavioral and/or policy induced changes in behavior may be different in dense counties. In fact, studies on previous pandemics  also show that population density is not necessarily linked with the spread and severity of a disease .ting Eq.  using thNext, we study how density affects behavioral responses to the pandemic . We use data from the Google COVID-19 Community Mobility Reports (CMR) to measure how mobility patterns in each county have changed relative to baseline levels measured in January 2020. In Panels B and C of Table Several factors could explain this difference in behavior across dense and sparse counties. One candidate that could account for both policy responses and individual differences in behavior relates to ideological or political views. Allcott et al.  show thaWe can arrive at two conclusions from the results reported in Table Finally, in Table Overall, our points relating to behavioral responses, healthcare provision and demographics provide probable explanations for the surprisingly flat relationship between density and COVID-19-related mortality reported in panel C of Table In this section, we provide several tests to evaluate the robustness of our main findings. We first revisit our results for the time-adjusted COVID-19 deaths by controlling for time of onset. In Panel A of “Appendix Table Much of the evidence featured in the discussion around the role of urban density in shaping the impact of COVID-19 has focused on the conventional, area-weighted definition of density . In order to speak to that debate, this has been the object of our main analysis. But we can evaluate the robustness of our results to the definition of density by studying the effect of population-weighted densities. In “Appendix Table cases of COVID-19. As argued above, the number of cases is more likely to be affected by variation in testing resources and by the presence of asymptomatic cases. This motivates our focus on number of deaths in much of the main analysis. In Table Finally, we test whether density affects the time-adjusted number of reported Urban areas are often places of intense social interaction, crowded living and close contact. Whether Justinian’s Constantinople, fourteenth century Florence or 1918 Philadelphia - cities have historically been associated with the propagation of infectious disease. In the first three months of the global COVID-19 pandemic, large, dense urban areas around the world such as New York, Madrid and London were identified as disease hotspots. Increased awareness of the risks of present and future epidemics has understandably prompted a debate about the future of cities. Did density—the defining feature of cities—promote the spread of the disease?Our analysis of the onset of the COVID-19 pandemic in the USA raises a series of important points regarding these questions. First, density is associated with an early arrival of COVID-19, so that urban cores and superstar cities get a head start on the spread of the disease. Second, the subsequent spread—once COVID-19 has arrived—is not faster or deadlier than in smaller towns or sparsely populated peripheries. Cities get hit first, but do not get hit harder. We argue this is one of the reasons why many of the early studies of the impact of density on the impact of COVID-19 reported positive findings. A wider look at the whole period before vaccination began yields a different overall view of this relationship.Several mechanisms may explain these findings. Large cities are intensely inter-connected with other locations, which can explain early onset. In the case of within-city spread, different offsetting forces may be at play. Crowding may promote the spread of the disease but differences in precautionary measures, access to healthcare and demographics may contain it. As a result, our findings emphasize the importance of distinguishing between differences in spread between and within locations.Our study contributes to the understanding of how a summary feature of urban structure—population density—shapes spread of disease and deaths. The way in which other elements or urban form, cities’ transport infrastructure or housing conditions  shaped the impact of the COVID-19 pandemic is not addressed here and remains an active area of research (see e.g., Kamis et al.


Aiming at the existing Direction of Arrival (DOA) methods based on neural network, a large number of samples are required to achieve signal-scene adaptation and accurate angle estimation. In the coherent signal environment, the problems of a larger amount of training sample data are required. In this paper, the DOA of coherent signal is converted into the DOA parameter estimation of the angle interval of incident signal. The accurate estimation of coherent DOA under the condition of small samples based on meta−reinforcement learning (MRL) is realized. The meta−reinforcement learning method in this paper models the process of angle interval estimation of coherent signals as a Markov decision process. In the inner loop layer, the sequence to sequence (S2S) neural network is used to express the angular interval feature sequence of the incident signal DOA. The strategy learning of the existence of angle interval under small samples is realized through making full use of the context relevance of spatial spectral sequence through S2S neural network. Thus, according to the optimal strategy, the output sequence is sequentially determined to give the angle interval of the incident signal. Finally, DOA is obtained through one-dimensional spectral peak search according to the angle interval obtained. The experiment shows that the meta−reinforcement learning algorithm based on S2S neural network can quickly converge to the optimal state by only updating the gradient of S2S neural network parameters with a small sample set when a new signal environment appears. Direction of arrival (DOA) serves as an important research field. In particular, the algorithm represented by Multiple Signal Classification (MUSIC) ,2,3,4 anTherefore, scholars have proposed various data-driven methods such as neural network and support vector machine (SVM) ,11,12,13Among them, a new unsupervised DNN neural network learning strategy for incoherent DOA is proposed in document , which cHowever, based on the existing neural network methods, there are still the following problems in coherent DOA estimation: (1) When the signal environment changes, that is, the training parameters such as signal-to-noise ratio and number of snapshots, are not consistent with the test parameters, the estimation accuracy will decline under the new signal environment. So, the neural network needs a large number of samples to learn again. (2) To improve the estimation accuracy, the quantization angle step needs to be relatively small, which leads to an increase in the amount of search, thus greatly increasing the computational complexity. (3) When the quantization angle step based on the (2) problem is relatively small, there will be some errors for the neural network to solve the long sequence, which will affect the final DOA results. In order to solve the above problems that will be faced, we can give the corresponding preliminary scheme, that is, we can solve the (1) existing problem by using meta−reinforcement learning through a small sample set to obtain the angle interval of the incident signal, and then complete the two-dimensional to one-dimensional spectral peak search through its angle interval, and then solve the (2) existing problem, and finally get the DOA results. Therefore, this paper proposes a small sample meta reinforcement learning method based on Markov Decision Process (MDP) to improve DOA based on coherent angle interval feature. It is intended to model the decision process of the angle interval feature sequence of coherent DOA of meta−reinforcement learning method as MDP, and then the target output can be weighted by the attention mechanism of the sequence to sequence (S2S) network. This will affect the selection of context information of small sample data sets to improve the accuracy of the output long sequence decoder; that is, the probability output of solving each angle interval in the angle interval feature vector sequence will be improved to solve the (3) existing problem, and then complete the whole machine translation process.Let the incoming wave direction of the coherent signal In the above equation, R can be derived from the observations of the covariance matrix R of the array output R be the covariance matrix of In the following, the quadratic spatial spectral characteristics  of R canDefine Here, (.)H denotes the Hermitian matrix, According to the above equation and definition, the quadratic characteristic space spectrum Among thrdr, In order to avoid a two-dimensional search of the network output when solving the coherent DOA, the quadratic characteristics of the coherent angular interval feature vector are further extracted based on the above discrete set of quantized directions. The variation interval of the coherent angular interval quantities is The problem is equivalent to inputting a sequence feature When the quantization unit n as MDP  , which is B.Test-time analysisTwo coherent signal sources are estimated under different methods of setting the SNR of 5 dB and the number of snapshots of 40. The test-time comparison in Aiming at the problems of performance degradation or even failure of existing coherent DOA estimation methods based on neural network when training samples are insufficient, the coherent DOA estimation method of small sample S2S neural network based on meta−reinforcement learning is studied. Firstly, the method is based on the coherent angle interval feature vector sequence of the array. According to the characteristics of the angle interval feature vector sequence, it establishes the MDP model and the definition of its state, action, and state transition income, solves the angle interval of the incident signal, and then completes the reduced-dimension spectral peak search, which greatly reduces the computational complexity. At the same time, based on the support of the meta−learning algorithm, the MDP process is converted into a meta−reinforcement learning algorithm, which then solves the problem of small-sample coherent DOA. Secondly, due to the intentional reduction of the quantization error, the sequence length of the required solution increases. According to the simulation results, due to the introduction of the attention mechanism in the S2S neural network expression, the context information between the long sequence tasks can be better utilized, thus contributing to the decoding of coherent sources. The experimental results show that the method based on this paper has the advantages of quickly adapting to the new signal environment, has good robustness to quantization error, and even has great advantages in accuracy and generalization ability in the harsh environment such as low signal-to-noise ratio and small angle domain, so this method is particularly suitable for coherent DOA estimation in challenging application environments.


One of the most common types of wheel damage is flats which can cause high maintenance costs and enhance the probability of failure and damage to the track components. This study aims to compare the performance of four feature extraction methods, namely, auto-regressive (AR), auto-regressive exogenous (ARX), principal component analysis (PCA), and continuous wavelet transform (CWT) capable of automatically distinguishing a defective wheel from a healthy one. The rail acceleration for the passage of freight vehicles is used as a reference measurement to perform this study which comprises four steps: (i) feature extraction from acquired responses using the specific feature extraction methods; (ii) feature normalization based on a latent variable method; (iii) data fusion to enhance the sensitivity to recognize defective wheels; and (iv) damage detection by performing an outlier analysis. The results of this research show that AR and ARX extraction methods are more efficient techniques than CWT and PCA for wheel flat damage detection. Furthermore, in almost every feature, a single sensor on the rail is sufficient to identify a defective wheel. Additionally, AR and ARX methods demonstrated the potential to distinguish a defective wheel on the left and right sides. Lastly, the ARX method demonstrated robustness to detect the wheel flat with accelerometers placed only in the sleepers. Nowadays, due to the increasing importance of railway transportation infrastructures, many studies have been conducted on their cost-effectiveness, particularly in terms of operation and maintenance costs ,2,3. OneMany types of damage can affect a train’s operational performance and one of the most important is defective wheels, which include two categories of defects, localized defects in the wheel tread , and defects around the complete wheel perimeter .Wheel flats are the most common type of defect in train wheels and remarkably affects running safety and causes significant damage to the infrastructure, namely the rails and sleepers, due to the higher impact forces induced in the track . The iniTo do this, an automated approach must be developed that can clearly distinguish between a healthy and damaged wheel. Therefore, finding effective methods for the early detection and identification of wheel flats is of great interest to railway administrations and rolling stock operators.In the last few decades, researchers have proposed several onboard and wayside systems for detecting wheel defects in operation conditions, most based on the concept that the interaction force between the train and the track increases in a defective wheel ,9. Many Alternatively, wayside measurement systems are currently the preferred solution to identify wheel flats since all wheels are evaluated during the train passage at the specific system location ,13,14. PTypically, the phases for damage identification methods are related to data acquisition, feature extraction, feature normalization, data fusion, and feature classification ,19. The One of the main challenges to detect a damaged wheel is to remove the environmental and operational effects from the dynamic responses to obtain features that are mainly sensitive to damage but insensitive to environmental and operational changes (EOVs). Therefore, to reduce the variation caused by EOVs and enhance the sensitivity to damage, feature normalization is performed by using various linear and non-linear correction models, such as, PCA , kernel For feature fusion and dimension reduction, several algorithms, including neighborhood-preserving embedding (NPE) , neural In recent years, machine-learning (ML) approaches in combination with advanced signal processing methods have been applied for feature classification to differentiate a healthy wheel from a defective one ,33. UnsuMost of the previous research on wheel flat detection is based on engineering field tests. However, numerical analysis is very useful for understanding the mechanism and physical consequences based on dedicated models. Additionally, models can be used for deeper comprehension and prediction in situations that cannot be reproduced in experimental tests. For example, external elements, such as noise, environmental disturbances, measurement errors, and electromagnetic interferences, easily influence the measurement process and may affect the results, causing a decrease in measurement accuracy. Additionally, numerical simulation makes it possible to define each unknown variable separately to check how it affects the results.(1)Development of an unsupervised data-driven methodology using acceleration responses on the rail for detecting defective wheels from healthy ones;(2)Implementation of AR, ARX, CWT, and PCA for feature extraction from multiple sensors to transform the time series measurements into damage-sensitive features, where the correlation with the damage can be more easily observed;(3)Analysis of the performance of the four feature extraction methods considering the different number and locations of the sensors on the rails;(4)Comparison of the sensitivity of the proposed methodologies to the side (left vs. right) of the defective wheel in a train axle;(5)Evaluation of the effectiveness of the proposed method with respect to the minimalist layout of sensors;(6)Improvement in wheel flat detection by applying a two-stage fusion process: in the first step, the features from each sensor are merged and, in the second stage, the multi-sensor information is fused to enhance the sensibility to the damage.It should be highlighted that the initial research on this topic was developed by Mosleh et al. ,8, who pIn this study, by using in-house software vehicle–structure interaction (VSI), the simulations for numerical train–track dynamic interaction were carried out. The vehicle–structure interaction analysis is explained and validated in detail in the work of Montenegro et al.  and has The software ANSYS  was usedA set of eight accelerometers were considered along the track as part of the wheel flat-detecting system. For testing and validating the automatic wheel flat-identification method proposed in this work, baseline (undamaged) and damaged wheel scenarios were considered. After validation, this method can reproduce real experimental data, from different types of trains with various wheel defects, running at different speeds on the rail track with distinct rail irregularities profiles.w the radius of the wheel.As shown in The vertical profile deviation of the wheel flat (Z) is defined as follows :Z =−D2 1Wheel-rail contact force values are significantly affected by imperfections in a real-condition environment, where the rails are not completely smooth. Although these irregularities are very small, they should be considered in the numerical analyses. Four real unevenness track profiles are taken into consideration in this study. The selected unevenness profiles of the rail are measured on the Northern Line of the Portuguese Railway network based on the track inspection vehicle EM120 and according to the details provided by Mosleh et al. . The totTo evaluate the proposed methodology, the accelerations on eight positions of the rail were evaluated in both baseline (undamaged) and damaged scenarios. The baseline condition corresponds to a train passing with healthy wheels, while the damaged scenarios correspond to the passage of trains with defective wheels. The numerical signal was then polluted with artificial noise (5% of the amplitude) based on the maximum response of the signal for a more realistic reproduction of the measured rail response. In Features extraction: application of four advanced data-driven models, including the continuous wavelet transform (CWT), auto-regressive model (AR), principal component analysis (PCA), and ARX to extract the damage-sensitive features from the time series;Feature normalization: normalization of the extracted features by the principal component analysis (PCA) method to increase the sensitivity to damage and remove environmental and operational variations (EOVs);Data fusion: implementation of a Mahalanobis distance (MD) to merge the features derived from each sensor and detect wheel defects more effectively. In the first stage, the features from each sensor are merged and, in the second stage, the multi-sensor information is fused to enhance the sensibility to the damage ,32;Outlier analyses: upon completion of the previous step, a damage indicator (DI) is generated for each train passage; to distinguish each DI into a defective or a healthy wheel a statistical-based approach is used, in particular, an inverse cumulative distribution function that allows estimating a statistical confidence boundary (CB).The purposed methodology for the automatic detection of wheel flats presented in The theoretical framework of each technique implemented within the methodology is available in the authors’ previous publications ,8,19,45.This section presents the application of the unsupervised learning methodology of wheel flat detection to the case of a freight train and considers different feature extraction methods, namely the AR, ARX, CWT and PCA. The purpose of this comparison was to assess the sensitivity to damage of each extraction method.Damage-sensitive feature extraction from dynamic signals is the first step of the automatic damage detection methodology. The main goal of this step is to reduce the dimensions of the three-dimensional dynamic features matrices 143−by−q−by−n, in which, 143 is the total number of scenarios, including 113 baseline scenarios and 30 damage scenarios, q is the number of sensors (four sensors) and n is the dimension of dynamic time-histories . For this purpose, the extraction of features sensitive to the effects of wheel flats was performed by considering auto-regressive model (AR), principal component analysis (PCA), continuous wavelet transform (CWT), and auto-regressive model with exogenous input (ARX).Several AR models were analyzed to determine the appropriate model order based on the Akaike information criteria (AIC), particularly the orders between 1 and 50. The AIC function for 30 damaged scenarios is shown in Extracted features from dynamic responses by implementing the AR method are obtained in 143−by−4−by−40 matrices which means that the number of features is significantly reduced from 70,000 to 40. The auto-regressive model with exogenous input (ARX) is the second technique that was used to extract dynamic damage-sensitive features. This method of time-series analysis can perform a significant fusion while accurately generalizing the information contained in the data by adjusting the ARX (143−by−4−by−80) models. By using the ARX model the number of features is enlarged to 80 in comparison to the AR model. Another methodology that was implemented for feature extraction to reduce the size of the feature matrices was the continuous wavelet transform. By using CWT, the number of features is decreased from 70,000 to 468 and the obtained features matrices are of size 143−by−4−by−468. Data science frequently uses principal component analysis (PCA) to extract features based on the data projection into a new dimensionless subspace. PCA identifies the covariance matrix eigenvectors with the highest values ,5,8,19. Data normalization allows to distinguish changes in the features acquired from sensor readings influenced by environmental and operational variations. One of the significant issues in damage detection is the difficulty of isolating environmental and operational disturbances from the observed dynamic properties to obtain features that are primarily sensitive to damage. Without the requirement to measure these actions, implementing a latent variable approach, such as PCA, to the retrieved features may effectively limit the effects of EOVs. In the feature normalization procedure, during the modeling phase, a cumulative percentage of the variance of components with a variance greater than 80% is removed [By implementing the PCA method to AR parameters to normalize the features, for each train passage, a 4−by−40 matrix with PCA-based features was generated. Feature normalization was also applied to the features extracted from the ARX method, and as a result, a matrix with dimension 4−by−80 was obtained individually for each train passage. The results of The MD values for the ARX-normalized features are presented in μ-, and standard deviation, σ, of the baseline feature vector. Finally, feature damage indicators equal or greater than the CB are considered outliers (the null hypothesis is rejected).Outlier analysis allows the assessment of how effectively the suggested methodologies distinguish healthy wheels from defective ones for all feature extraction methods without human intervention. In general, the literature presupposes that a chi-squared distribution in n−dimensional space can approximate the Mahalanobis-squared distance. Therefore, a Gaussian distribution can roughly represent the Mahalanobis distance, and an outlier analysis based on a statistical threshold can be performed. The threshold’s significance level is established as equal to 1% . AccordiThe results of the automatic damage detection for the ARX-derived features are presented in From This study aimed to compare the accuracy of an unsupervised data-driven methodology, based on four distinct features , for the automatic detection of wheel flats and based on time–history accelerations on the track elements (rails and sleepers).The proposed methodology includes (i) feature extraction from acquired responses using dedicated feature extraction methods; (ii) feature normalization based on principal component analyses (PCA); (iii) data fusion to merge features derived from each sensor and (iv) damage detection by performing an outlier analysis using a specific confidence boundary. the AR and ARX methods are the most accurate feature extraction methods for wheel flat damage detection as they can robustly detect defects; these two methods are sensitive to the side of the damage being the most promising to automatically distinguish an existing defective wheel on the right side from the left side in future works;the CWT method is only capable of detecting damaged wheels and is not sensitive to the side of the defect;the accuracy of the PCA method to detect the defective wheel is low and damage detection using this method lacks reliability;the ARX method is the only method that can robustly detect the wheel flat with accelerometers placed in the sleepers.One of the novelties of this research in relation to previous works ,8 is theFrom the research presented herein, it is possible to draw the following conclusions:Such results clearly show the great potential of this innovative application of data mining in the railway industry, particularly for infrastructure managers. Future work includes a field trial to validate the proposed methodology based on on-site measurements. Furthermore, for the final development of the proposed methodology, it is imperative to develop a novel feature, or eventually upgrade the actual methodology, to additionally classify the severities of the wheel flats.