To verify the reliability of the proposed stitching linear scan method for roundness measurement of small cylindrical workpieces, measurement uncertainties of the workpieces with various dimension are conducted. The cross-sectional circle of a cylindrical workpiece is divided into several equal arcs to be measured. Thus, the arc profile can be characterized according to the measured arcs. The roundness profile of the cylindrical part can be obtained by stitching these arc profiles together. Firstly, the measurement uncertainty of the workpiece with a diameter 1.5 mm and length 5.8 mm is evaluated to be 0.095 μm which can meet the uncertainty target of 0.1 μm. Considering the uncertainty of θx and θz which are the alignment around X-axis and Z-axis respectively changing with workpiece dimension changing, relevant uncertainties are carried out. As a result, there is no limitation with respect to workpiece diameter theoretically If the alignment can reach θz = θx = ±0.1° without considering the measurable range of the machine.

Side-scan sonar (SSS) detection is a key method in applications such as underwater environmental security and subsea resource development. The use of acoustic images for seabed target detection has gradually become a mainstream underwater detection method. However, many existing detection approaches primarily concentrate on tracking the evolution path of optical image object detection tasks, resulting in complex structures and limited versatility. To tackle this issue, we introduce a pioneering Dual-Domain Multi-Frequency Network (D2MFNet) meticulously crafted to harness the distinct characteristics of SSS image detection. In D2MFNet, aiming at the underwater detection requirements of small scenes, we introduce a novel method for optimize and improve the detection sensitivity of different frequency ranges and propose a Multi-Frequency Combined Attention Mechanism (MFCAM). This mechanism amplifies the relevance of dual-domain features across different channels and space. Moreover, recognizing that SSS images can provide richer insights after frequency domain conversion, we introduce a Dual-Domain Feature Pyramid Network (D2FPN). By incorporating frequency domain information representation, D2FPN significantly augments the depth and breadth of feature information in underwater small datasets. Our methods are seamlessly designed for integration into existing networks, offering plug-and-play functionality with substantial performance enhancements. We have conducted extensive experiments to validate the efficacy of our proposed techniques, and the results showcase their state-of-the-art performance. MFCAM improves the mAP by 16.9% in the KLSG dataset and 15.5% in the SCTD dataset. The mAP of D2FPN was improved by 8.4% in the KLSG dataset and by 9.8% in the SCTD dataset. We will make our code and models publicly available at https://dagshub.com/estrellaww00/D2MFNet.

Human microbiome data from genomic sequencing technologies is fast accumulating, giving us insights into bacterial taxa that contribute to health and disease. Predictive modeling of such microbiota count data for classification of human infection from parasitic worms such as helminths can help in detection and management across global populations. Real-world datasets of microbiome experiments are typically sparse containing hundreds of measurements for bacterial species, of which only a few are detected in the bio-specimens that are analyzed. This feature of microbiome data produces the challenge of needing more observations for accurate predictive modeling and has been dealt with previously using different methods of feature reduction. To our knowledge, integrative methods such as transfer learning has not been explored in the microbiome domain yet, as a way to deal with data sparsity by incorporating knowledge of different but related datasets. One way of incorporating this knowledge is by using a meaningful mapping among features of these datasets. In this paper, we claim that this mapping would exist among members of each individual cluster grouped based on phylogenetic dependency among taxa and their association to the phenotype. We validate our claim by showing that models incorporating associations in such a grouped feature space result in no performance deterioration for the given classification task. In this paper, we test our hypothesis using classification models that detect helminth infection in microbiota of human fecal samples obtained from Indonesia and Liberia countries. In our experiments, we first learn binary classifiers for helminth infection detection by using Naive Bayes, Support Vector Machines, Multilayer Perceptrons, and Random Forest methods. In the next step, we add taxonomic modeling using the SMART-scan module to group the data, and learn classifiers using the same four methods, to test the validity of the achieved groupings. We observed a 6% to 23% and 7% to 26% performance improvement based on the Area Under ROC Curve (AUC) and Balanced Accuracy(Bacc) measures, respectively, over 10 runs of 10-fold cross-validation. These results show that using phylogenetic dependency for grouping our microbiota data actually results in a noticeable improvement in classification performance for helminth infection detection. These promising results from this feasibility study demonstrate that methods such as SMART-scan can be utilized in the future for knowledge transfer from different but related microbiome datasets by phylogenetically-related functional mapping, to enable novel integrative biomarker discovery.

Driving factors of smart city construction are exploratively studied by grounded theory method based on text sources from journal papers in SCI and CSSCI databases. Initial scale of driving factors about smart city construction is obtained on the basis of above analyses. This paper modifies measuring items of the initial scale with a small sample pretest and reliability test, then forms final items by exploratory factor analysis. According to the above scale, questionnaires are designed to obtain empirical data, and confirmatory factor analysis is used to verify further validity and reliability of the scale. The results show that driving factors of smart city construction include three main dimensions: problem-oriented factors, business- or technology-driven factors and endogenous development requirements.

The unpredictable increase in electrical demand affects the quality of theenergy throughout the network. A solution to the problem is the increase of distributedgeneration units which burn fossil fuels.While this is an immediate solution to theproblem the ecosystem gets affected by the emission of CO2.A promising solutionis the integration of Distributed Renewable Energy Sources (DRES) to the conventionalelectrical system, thus, introducing the concept of smart microgrids (SMG) that requirea safe, reliable and technically planned two-way communication system. This documentpresents a heuristic based on planning capable of providing a bidirectional communicationnear optimal route map, following the structure of an hybrid Fiber-Wireless (FiWi) with thepurpose of obtaining information of electrical parameters that help us to manage the useof energy by integrating conventional electrical system to SMG. A FiWi network is basedon the integration of wireless access and optical networks. This integration increases thecoverage and reliability at a lower cost. The optimization model is based on clusteringtechniques, through the construction of balanced conglomerates. The method is used forthe development of the clusters along with the Nearest-Neighbor Spanning Tree Algorithm(N-NST). Additionally, Optimal Delay Balancing (ODB) model will be used to minimizethe end to end delay of each grouping. In addition, the heuristic observes real designparameters such as: capacity and coverage. Using the Dijkstra algorithm, the routes arebuilt following the minimum shorter path. Therefore, this paper presents a heuristic able to plan the deployment of smart meters (SMs) through a tree-like hierarchical topology for theintegration of SMG at the lowest cost.

Automatic generation control (AGC) plays a vital role in creating an equilibrium between generated output power and load demand, in order to maintain frequency at desired value. This study focuses on performance analysis of simple-multi attribute rating technique (SMART) assisted proportional-integral-derivative (PID) controller design for AGC of two interconnected power systems. PID controller is designed by minimizing frequency variations, area control errors for area-1 and area-2, and tie-line power deviation. An objective function is framed considering error minimization of aforementioned factors as sub-objectives. The construction of objective function involves using the integral of time-multiplied absolute error (ITAE) for frequency deviations for area-1 and area-2, ITAE for deviation in tie-line power, and ITAEs for area control errors for area-1 and area-2 as sub-objective functions. By assigning appropriate weights to these sub-objective functions, an overall objective function is formed. This study determines these weights in an organized manner using SMART method, rather than randomly/equally assigning them. Overall objective function is minimized using Jaya algorithm. To demonstrate effectiveness of the proposed Jaya-based PID controller, its performance is analysed and compared with controllers tuned using other optimization algorithms, including sine cosine, Luus-Jaakola, teacher-learner based optimization, Nelder-Mead simplex, and elephant herding optimization. Considering six different case studies that consider a range of load variations, responses for fluctuations in frequency and tie-line exchange are plotted. Statistical and non-parametric analysis performed further provide additional insights into the performance of Jaya-based PID controller.

The research work hereby presented, emerges from the urge to answer the well-known question of how the uncertainty of intermittent renewable sources affects the performance of a microgrid and how could we deal with it. More specifically, we want to evaluate what could be the impact in performance of a microgrid intended to serve a smart-building (powered by photovoltaic panels and with battery energy storage), when the uncertainty of the photovoltaic-production forecasts is considered in the energy management process. For this, several objectives (or services) are targeted based in a two-step (double-objective) energy management framework, that combines optimization-based and rule-based algorithms. The performance is evaluated based on some particular services proposed as performance indicators. Simulations are performed using data of a study-case microgrid (Drahi-Xnovation center, Ecole Polytechnique, France). The use of quantile forecasts (obtained with an analog-ensemble method) is tested as a mean to deal with (i.e. decrease) the uncertainty of the solar PV production. The proposed energy management framework is compared with basic reference strategies and the results show the superior performance of the former in almost all the services and forecasting scenarios proposed. The contrasting nature among some of the target services is one of the main conclusions of this work, as well as the different requirements in terms of forecasts when optimizing for different services and seasons of the year. This fact highlights the usefulness of the quantile forecasting approach, as a tool to deal with the intrinsic uncertainty of PV power production

Introduction: CT scan of the skull in patients with head trauma play an important role in identifying patients' injuries; In moderate to severe brain injury; CT scans are arguably the best way to get information about the intracranial complications of a head injury. Researchers have cited a number of reasons for why doctors are increasingly asking for CT scans of patients. In this study; pathological findings in CT scan of patients with mild cranial trauma and level of consciousness 13-15 are examined. Method: In this study, the treatment process of 445 patients who had referred to the emergency department due to skull trauma was examined as research units; and samples were taken by simple random sampling among patients for whom CT scan of the brain was requested. In this study; the reasons for requesting a CT scan of the skull from the requesting physician were questioned through an interview at the time of request. Results: According to the tables; it can be seen that most of the patients referred to the hospital due to skull trauma for CT scan were men and were discharged in less than the first 24 hours. The age group of 21 to 30 years had the highest and the age group over 70 years had the lowest frequency and the highest percentage of request for CT scan was by doctor and the biggest injury that caused patients with mild cranial trauma to go to hospital was fall. Conclusion Based on the findings of this study, cranial trauma is one of the life-threatening factors that can cause minor or severe injuries Its range varies from superficial injuries to hematoma and intracranial hemorrhage, which is a reason to perform CT scans of patients with skull trauma. In order to carefully examine the extent of the damage to the patient and to avoid possible risks and to take the necessary measures for treatment. On the other hand, the lack of clear protocols as well as the lack of clarity and certainty of physicians in their diagnoses to perform graphs of qualified people has increased the number of unnecessary CT scans

Infectious disease like COVID-19 will have a great impact on human society. An effective prevention and control measure is to quickly isolate the source and reduce the population’s exposure to the infection. Therefore, it is very important to quickly trace and identify the virus carrier and its close contacts in order to effectively suppressing transmission. This article provides a method, system and storage medium for locating the source of infection based on big data. We adopt the method of combining Bluetooth and GPS positioning to track the action trajectory of each individual. The method stores the close contact and location information locally and update on the cloud platform for an appropriate period of time. The method and system can provide personal risk levels and regional risk levels through related algorithms. When entering a high-risk area or if the personal risk level within the Bluetooth interconnection range is greater than the set threshold, an alarm function is provided. We can also generate reports on the spread of the epidemic over a period of time. As a result, the system can quickly locate the source of infection and close contacts, providing a reference for quick and highly efficient epidemic prevention and control.

Information and communication technologies play an increasingly important role in the process of knowledge and management of places at different scales. ICTs allow a rapid diffusion of data not only through institutional channels but also through social networks where the smart community share experiences and perceptions. In this sense, ICTs become strategic tools to support the promotion of sustainable tourism development of territories, especially if the digital data are organised within a circular smart dashboard. This research focuses on the case study of the Santa Barbara Walk (SBW), an ancient mining route in the Sulcis Iglesiente region (Sardinia, Italy), where the authors have recognized a state of disorganization in slow tourism promotion activities. In fact, if the SBW represents a network - material infrastructure - which connects the main points of interest along the Walk, its digital network - intangible infrastructure - is fragmented in terms of policies and contents. The goal of this study is to provide a comprehensive set of data and to propose the architecture and design for a circular dashboard of the SBW, capable of organizing information concerning the main features of the walk, in order to facilitate a shared governance for an effective tourism promotion.

Introduction:ASPECTS (Alberta Stroke Program Early CT Score) help in detecting early ischemic changes in stroke patients. This scoring system helps in predicting the stroke outcome, treatment options like thrombolysis and prognosis. It is a 10-point scaling system based on anatomical regions supplied by the middle cerebral artery and each point is subtracted for areas with ischemic changes. Aims and objectives:To study the effectiveness of the ASPECTS scoring in the assessment of the anterior and posterior cerebral circulation stroke. Materials and methods:A retrospective study was performed on stroke patients referred to our institute for an NCCT scan. 40 stroke patients with a mean age of 59+/-3 years were selected and patients with intracranial hemorrhage or hemorrhagic transformation of infarct were excluded. Scans were performed on a 128-slice multidetector CT PHILIPS Perspective scanner. The ASPECTS score was determined using standardised axial CT cuts. Result: In this case series study of 40 patients, a total of 28 patients (70%) had a score of 7 or more and these patients had better prognoses with proper treatment and follow-up.The remaining 12 patients (30%) had a score of less than 7 and even with treatment and sequential follow-up, they had no neurological recovery. Conclusion: Thus, it can be concluded that the ASPECTS can be used as a tool for predicting treatment outcomes in stroke patients. A score of 7 or more is associated with a good prognosis.

Pneumatosis intestinalis is the presence of air within the two layers of the wall of the gastrointestinal tract, specifically in the small and large intestines. This accumulation of gas can cause thicker walls of the intestines in X-ray or CT scans. Superior Mesenteric Artery (SMA) Thrombosis occurs due to the formation of blood clots within the SMA. As it supplies from the ampulla in the second part of the duodenum to the splenic flexure of the large intestine its thrombosis can cause ischemia in any part depending upon its branch. A 67-year-old male patient presented to the hospital with the complaint of generalized abdominal pain for 5 days along with fever, nausea, vomiting, obstipation, and postprandial fullness. On further investigation with the help of an MDCT scan and X-ray, proximal jejunal loops showed circumferential air within the walls giving the diagnosis of Pneumatosis Intestinalis along with Superior Mesenteric Artery Thrombosis causing jejunal infarct.

Adequate serum vitamin D and iron levels are thought to positively influence physical training adaptations and mood. The purpose of this prospective, observational, study was to investigate relationships between serum 25-OH vitamin D and serum ferritin levels with body composition and athlete burnout symptoms. Seventy-three collegiate athletes (49 female) from 7 indoor and outdoor sports were tested pre-season and post-season for: nutrient biomarkers (serum 25-OH vitamin D and serum ferritin) via venipuncture; body composition (total lean mass, bone mineral densi-ty/BMD, and % body fat) via dual energy x-ray absorptiometry (DXA) scans; and athlete burnout symptoms (post-season) via the athlete burnout questionnaire (ABQ). When male and female co-horts were combined, significant relationships were noted between pre-season serum 25-OH vit-amin D versus the change (∆: post-season minus pre-season) in both BMD (r=-0.34;p=0.0003) and % body fat (r=-0.28;p=0.015). Serum ferritin ∆ was significantly associated with lean mass ∆ (r=-0.34;p=0.003). For burnout symptoms, serum 25-OH vitamin D ∆ significantly explained 20.6% of the variance for devaluation of sport in the male cohort only. Across time, serum 25-OH vitamin D levels increased while serum ferritin levels decreased, non-significantly, in both males and fe-males. Relationships between nutrient biomarkers and body composition were opposite of physio-logical expectations.

Background The acknowledgment of computed tomography (CT) defined diagnosis in high prevalence northern Italy may identify more patients with Coronavirus Disease-2019 (COVID 19) infection, than RT-PCR alone. Methods We retrospectively reviewed 148 chest CT scans of oncological patients who were referred to the Radiological Unit of Policlinico S. Marco from 1st of February 2020 to 30th of April 2020, during the Covid-19 outbreak in Bergamo area. Therefore, we analyzed RT-PCR tests of these 148 patients. Results Among 32 patients with diagnosis of COVID-19 infection: 17 patients were asymptomatic or had mild symptoms (53.1%), while 15 developed severe disease (46.8%). The incidence of COVID-19 infection is 22.9%, the mortality rate is 18.8%. Severe COVID-19 disease is associated with higher median age. We did not find any correlation between disease severity and sex, smoke or cardiovascular comorbidities. Remarkably, patients who were on treatment developed milder disease than cancer patients who were not on treatment. Conclusions The acceptance of CT-defined diagnosis in high prevalence area like Bergamo highlighted a larger number of COVID-19 oncological population than RT-PCR alone, in particular asymptomatic and mild symptomatic patients. We observed that actively treated patients had milder disease, according to previous studies that suggested a protective role of immunosuppression.

The hard bottom layer of paddy field has a great influence on the driving stability and operation quality and efficiency of intelligent farm machinery, and the continuous improvement of unmanned precision operation accuracy and operation efficiency of paddy field operation machin-ery is the support to realize unmanned rice farm. In this paper, in view of the complicated hard bottom layer situation of unmanned operation farm machinery driving is difficult to realize to quantify the local characteristics of hard bottom layer of paddy field, the unmanned rice direct seeding machine chassis is used to operate the operation field and collect the hard bottom layer information simultaneously, and the data processing method of automatic calibration of sensor installation error, abnormal value rejection and 3D sample curve denoising of contour trajectory is designed; a hard bottom layer surface profile evaluation method based on the local sliding surface roughness is proposed. The local characteristics of the hard bottom layer were quantified, and the quantified results of the local characteristics of the hard bottom layer in the test plots showed that the mean value of the local roughness was 0.0065, 68.27% was distributed in the variation range of 0.0042~0.0087, and 99.73% was distributed in the variation range of 0~0.0133. Based on the test field data, the surface roughness features are verified to describe the variability of representative working conditions such as transport, downfield, operation and trapping of unmanned operation of intelligent farm machinery. The method of quantifying the hard-bottom local features of farm machinery driving can provide feedback on the local environmental features of intelligent farm machinery driving at the current position, and provide a reference basis for the design optimization of unmanned system for improving the quality of intelligent farm machinery operation.

Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.

Backround: FNH is a common benign liver lesion, mostly solitary, and with no evidence of malignant transformation over time. The diagnosis can usually be made by imaging modalities with distinct features in MRI- and CT-scans. Case series: We present a case series of three patients with solitary liver tumors which were highly suspicious to be large FNH, based on radiological features. The patients were sent to our center for second opinion and probable treatment. Indication for resection was given because of nonconclusive radiologic imaging in the first, substantial growth progression in the second and elevated AFP in the third case. Histology revealed well to poor differentiated HCCs. Discussion: FNH tends to show a distinct pattern in imaging modalities, but HCC can mimic FNH. The MRI-scan using hepatotropic contrast agent is the most sensitive imaging modality to diagnose FNH. AFP is usually not elevated in FNH. Asymptomatic FNH does not require surgery but a reliable diagnosis is crucial. In case of an uncertain diagnosis surgery should be offered to patients. Conclusion: In cases with a progression in size, an elevated level of AFP should be suspicious. A second opinion at a center for hepato-biliary surgery with HCC/FNH-experienced radiologists and surgeons is reasonable. Generally, an over-therapy via surgery should be avoided and follow-ups of a newly diagnosed FNH including measurement of AFP are advisable to detect alterations.

Presenting a case report about an occurrence of duodeno-duodenal intussusception in a 17-year-old female patient. Intussusception is typically observed in children. The patient has a history of recurrent pain and vomiting for four days. During the examination tenderness in the epigastric area worsened when the legs were elevated. An initial X-ray of the abdomen was done and the findings were normal. Subsequent ultrasound and abdominal CECT scans of the abdomen revealed intussusception involving the 2^nd, 3^rd, and 4^th part of the duodenum ampulla of Vater and pancreas without any identifiable cause for this condition. There was no abnormal thickening of the wall. This case shows how using radiological imaging and surgical management helps to prevent severe complications in cases like duodeno-duodenal intussusception in adults.

In this presented case, a 77 years old woman suffering from ongoing knee pain underwent a bone scan using 99mTc-hydroxydiphosphonate (HDP) in suspicious for bone infection. An incidental finding from this scan revealed diffuse cardiac uptake, necessitating further diagnostic procedures to exclude the possibility of cardiac amyloidosis. In the subsequent 99mTc-3,3-diphosphono-1,2-propanodicarboxylic acid (DPD) scan and SPECT images, no perceptible cardiac uptake was observed at all. Upon retrospective review of the patient’s medical records, she had received 1000mg of ferric carboxymaltose for iron-deficient anemia, the day before the 99mTc-HDP bone scan. Therefore, it is assumed that the diffuse and temporary cardiac activity was due to the transient iron overload. We present and share this bone scan images in order to avoid possible future misinterpretation of cardiac amyloidosis.

As a rapidly expanding family of two-dimensional (2D) materials, MXenes have recently gained considerable attention due to their appealing properties. Here, by developing a solution-based coating method that enables transfer-free and layer-by-layer film coating, we investigate the layer-dependent nonlinear optical absorption of Ti₃C₂T_x films ‒ an important member of the MXene family. By using the Z-scan technique, we characterize the nonlinear absorption of the prepared MXene films consisting of different numbers of monolayers. The results show that there is a strong and layer-dependent nonlinear absorption behavior, transitioning from revisable saturable absorption (RSA) to saturable absorption (SA) as the layer number increases from 5 to 30. Notably, the nonlinear absorption coefficient β varies significantly within this range, changing from ~7.13 × 10² cm/GW to ~-2.69 × 10² cm/GW. We also characterize the power-dependent nonlinear absorption of the MXene films at various incident laser intensities, and a decreasing trend in β is observed for increasing laser intensity. These results reveal the intriguing layer-dependent nonlinear optical properties of 2D MXene films, highlighting their versatility and potential for implementing high-performance nonlinear photonic devices.

Some COVID-19 patients develop interstitial pneumonia that can evolve into Acute Respiratory Distress Syndrome (ARDS). This is accompanied by an inflammatory cytokine storm. SarS-CoV has proteins capable of promoting cytokine storm, especially in patients with comorbidities, including obesity. Since there is currently no resolutive therapy for ARDS and given the scientific literature regarding the use of adenosine, its application has been hypothesized. Adenosine through its receptors is able to inhibit the acute inflammatory process, increase the protection capacity of the epithelial barrier and reduce the damage due to an overactivation of the immune system, such as in cytokine storms. These features are known in ischemia / reperfusion models and could also be exploited in acute lung injury, with hypoxia. In light of these hypotheses, for compassionate use, a COVID-19 patient, with unresponsive respiratory failure, was treated with adenosine. The results showed a rapid and clear improvement in clinical conditions, with the negative effect of detection of SarS-CoV2.

Cell division control 37 (CDC37) increases the stability of HSP90 client proteins and is thus essential for numerous intracellular oncogenic signaling pathways, playing a key role in prostate oncogenesis. Notably, elevated expression of CDC37 was found in prostate cancer cells, although the regulatory mechanisms through which CDC37 expression becomes increased are unknown. Here we show both positive and negative regulation of CDC37 gene transcription by two members of the SCAN transcription factor family- MZF1 and SCAND1, respectively. Consensus DNA-binding motifs for myeloid zinc finger 1 (MZF1 / ZSCAN6) were abundant in the CDC37 promoter region. MZF1 became bound to these regulatory sites and trans-activated the CDC37 gene whereas MZF1 depletion decreased CDC37 transcription and reduced tumorigenesis of prostate cancer cells. On the other hand, SCAND1, a zinc-fingerless SCAN box protein that potentially inhibits MZF1, accumulated at MZF1-binding sites in CDC37 gene, negatively regulated CDC37 gene and inhibited tumorigenesis. SCAND1 was abundantly expressed in normal prostate cells but was reduced in prostate cancer cells, suggesting a potential tumor suppressor role of SCAND1 in prostate cancer. These findings indicate that CDC37, a crucial protein in prostate cancer progression, is regulated reciprocally by MZF1 and SCAND1.

Sleep deficiency impacts the quality of life and may have serious health consequences in the long run. Questionnaire-based subjective assessment of sleep deficiency has many limitations. On the other hand, objective assessment of sleep deficiency is challenging. In this study, we propose a polysomnography-based mathematical model for computing baseline sleep deficiency severity score and then investigated the estimation of sleep deficiency severity using features available only from wearable sensor data including heart rate variability and single-channel electroen-cephalography for a dataset of 500 subjects. We used Monte-Carlo Feature Selection (MCFS) and inter-dependency discovery for selecting the best features and removing multi-collinearity. For developing the Regression model we investigated both the frequentist and the Bayesian ap-proaches. An Artificial Neural Network achieved the best performance of RMSE = 5.47 and an R-squared value of 0.67 for sleep deficiency severity estimation. The developed method is com-parable to conventional methods of Functional Outcome of Sleep Questionnaire and Epworth Sleepiness Scale for assessing the impact of sleep apnea on sleep deficiency. Moreover, the results pave the way for reliable and interpretable sleep deficiency severity estimation using a wearable device in Smart Health.

COVID-19 is a severe global problem, and AI can play a significant role in preventing losses by monitoring and detecting infected persons in early-stage. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan images. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel method for increasing the classification accuracy of convolutional networks. We implemented our method using the ResNet50V2 network and a modified feature pyramid network alongside our designed architecture for classifying the selected CT images into COVID-19 or normal with higher accuracy than other models. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient identification phase, the system correctly identified almost 234 of 245 patients with high speed. We also investigate the classified images with the Grad-CAM algorithm to indicate the area of infections in images and evaluate our model classification correctness.

As digital technology has become an integral part of urban’s daily operations; the urban landscape is constantly evolving with the needs of its society. This new reality allowed Municipalities to exert a higher influence in the national and local economy. Through deliberate planning and executing initiatives, they can ignite the local financial ecosystem to growth. In line with this, the paper aims to understand the possibilities available to the Greek Municipalities to exploit the smart city benefits to foster economic development. It is essential to understand the role of different factors including the strategy during the planning and implementing phases of initiatives concerning economy and innovation in a smart city. To achieve this, a tailor-made questionnaire and advanced statistical techniques are applied. The main findings highlight the importance to plan initiatives aligned with the needs of the municipality and the business environment. The existence of a smart city strategy has a catalytic effect on the final impact of the implemented initiatives on the urban environment. A systematic analysis of the smart cities’ dynamics and the new state of the urban environment can help the local actors focus on the value creation and public service provision, fostering innovation and profitability.

The COVID-19 pandemic caused an unprecedented effect on national radiological investigations. Since the World Health Organization officially declared the COVID-19 as a global pandemic, health policies have been rapidly organized to limit the spread of the virus and decrease the risk of exposure. These restrictions, in combination with home-stay arrangements and the onset of economic recession. As a result of public policies, financial difficulties and patient fear, many radiology departments have suffered a significant reduction in diagnostic examinations with important implications for their economic stability. The aim of this work is to evaluate the economic impact of the COVID-19 pandemic in the Radiology Department of an infectious disease hospital.

Universities, like cities, have embraced novel technologies and data-based solutions to improve their campuses with ‘smart’ becoming a welcomed concept. Campuses in many ways are small-scale cities. They increasingly seek to address similar challenges and to deliver improved experiences to their users. How can data be used in making this vision a reality? What can we learn from smart campuses that can be scale up to smart cities? A short research study was conducted over a three-month period at a public university in the United Kingdom employing stakeholder interviews and user surveys, aiming at gaining insight into these questions. Based on the study, the authors suggest that making data publicly available could bring many benefits to different groups of stakeholders and campus users. These benefits come with risks and challenges such as data privacy and protection and infrastructure hurdles. However, if these challenges can be overcome, open data could contribute significantly to improving campuses and user experiences, and potentially set an example for smart cities.

The introduction of non-conventional renewable energies (photovoltaic and wind, in the residential case) demand new proposals and opportunities to obtain a domestic energy management system (HEMS), which allows reducing the use of electrical energy. The HEMS incorporates artificial intelligence (AI) techniques to respond to energy demand (DR), which can control, switch, turn on and off, modifying the consumption profile, reducing monthly billing, which as a consequence brings improvement. of the quality of life at home. Based on what has been described, a model for identifying electrical appliances and determining consumption patterns for the home is proposed, using intrusive measurement/actuator equipment called Smart Socket. The Smart Socket measures the electrical variables of voltage and current in real time, calculating the powers and active-reactive-apparent energies of each household appliance (or the most relevant ones). The information provided by the Smart Socket is used as a basis to configure the HEMS, with the aim of optimizing energy use using artificial intelligence techniques and tools, thus reducing energy consumption, CO2 emissions and the user's monthly billing. residential.

The complexity and interconnection of smart cities provide huge political, technical, and socioeconomic challenges for the designers, integrators, and organisations who are responsible for the administration of these new entities. A growing number of studies are concentrating their attention on the safety, privacy, and potential dangers that exist within smart cities. These studies are drawing attention to the dangers that are associated with information security as well as the challenges that smart city infrastructure faces in the management and processing of personal data. This state of the art review of the literature analyses a number of issues pertaining to smart homes, offers a helpful synthesis of the important information found in the primary research, and creates a model for the interaction between smart cities. An overview of smart home research incorporating machine learning, including everything from definition to current research state. The state of art begins with a smart home definition, followed by descriptions of smart home elements, typical research projects, smart home network research status, smart home appliances, and difficulties. The term "smart home" refers to a subcategory of "everyday computing" that includes "smart technology" in order to improve a person's level of convenience, health, safety, and security while also reducing their When controlled by artificial intelligence, the applications for the smart home provide users with context-aware settings, services, and remote control, which significantly increases the level of user satisfaction.

Over the recent decades people’s (rural and urban) communities are facing numerous social and economic changes and challenges. Some of those challenges have been increasingly addressed through the lenses of technological developments and digitalization. In this paper we have made a review of already existing practices while focusing on the existing implementations of the smart village concept and the importance of digital transformation for rural areas. We give special attention to EU policies which we are using as an already existing framework for understanding our own forthcoming examples. We have shown the parallels between the findings and insights from different regions and made an evaluation of presented practices. Our main argument stems from our own previous experiences and experiences of other research approaches, and is grounded on the argument that rural areas are not uniform, and that smart rural development has to be applied in combination with place-based approach. We present the cases of Slovenian pilot practices and support our argument by proposing the FabVillage concept.

The article describes the application of the Smart City concept and the economic opportunities it creates, infrastructure and services, and opportunities to improve governance. The main features of the Smart City concept, development directions and evolution, standards and solutions, factors and obstacles to its implementation have been analyzed by the author. The experience of different countries in the application of digital technologies is discussed. The article provides the scope and structure of the "smart" market, application stages and scenarios. International experience in this field was widely analyzed and examples were given, comparisons were made and suggestions were made. The article talks about smart cities, the construction of which has already begun in Azerbaijan. The application of the smart city concept in Azerbaijan has been studied. It is stressed that the spread of digital technologies for the construction of a smart city in Azerbaijan is a prerequisite. The Network Readiness Index (NRI) identifies the indicators that are holding back Azerbaijan in the ranking for 2021. In our study, based on the growth dynamics for 2017-2020, the forecast was calculated by analyzing the trend in the ICT development index in Azerbaijan for 2022-2025.

Background: A careful work-up before Transcatheter aortic valve replacement (TAVR) consists not only of measuring anatomical conditions but also assessment of frailty. Previous studies have evaluated body composition parameters, primarily fat and muscle mass. Few data are available on other frailty parameters, which, however, can be easily determined by pre-TAVR computed tomography (CT). Objectives: The primary objective was to investigate the impact of total muscle area (TMA) and visceral adipose tissue (VAT), as frailty parameters, on 5-year all-cause mortality in patients undergoing TAVR. Methods: Between 01/2017 and 12/2018, consecutive TAVR patients undergoing CT-angiography scan enabling TMA and VAT measurement were included. A single axial image was acquired at the level of the middle third lumbar vertebra to perform TMA and VAT measurements using dedicated slicing software (Sliceomatic, TomoVision). Results: A total of 500 patients qualified for TMA analysis. Age was not associated with a higher risk of 5-year mortality HR 1.02 (95% CI: 0.998 - 1.049; p=0.069). Body surface area normalized TMA (nTMA) was significantly associated with 5-year all-cause mortality (HR 0.927, 95% CI: 0.927-0.997; p= 0.033), while VAT had no effect (HR 1.002, 95% CI: 0.99-1.015; p= 0.7). The effect of nTMA on 5- year all-cause mortality was gender-dependent: the protective effect of higher nTMA was found in male patients (pinteraction: sex * nTMA = 0.007). The combination of normalized TMA and VAT was not associated with higher mortality rates. Conclusion: Normalized TMA derived from routine CT before TAVR complements frailty assessment in patients undergoing TAVR.

This study aimed to investigate the atherogenicity (quality) of LDL particles in patients with acute and recovered from COVID-19 infection. The participants were adults, aged 18 years or older of both sexes. Those with positive RT-PCR results at baseline were included in the Acute COVID-19 group (n=33), and those with negative RT-PCR six months after acute infection, were included in the Recovered COVID-19 group (n=30). The LDL quality was evaluated using three validated methods: Z-scan, UV-visible spectroscopy, and Lipoprint. The Recovered COVID-19 group showed significantly higher numbers of large LDL particles (less atherogenic) than the Acute COVID-19 group (P<0.05). We also found that COVID-19 infection was associated with the oxidative modification of LDL particles. D-dimer and CRP levels were correlated with Z-scan results and antioxidant-amount estimate. Moreover, we noticed that the infection left a sequel in LDL quality, even after six months of recovery. These findings highlight the importance of monitoring lipids during and after recovery from COVID-19 infection, and their potential deleterious effect on the LDL profile might correlate with the progression of atherosclerosis and poor clinical outcomes.

Broadband reverse saturable absorption is systematically investigated via Z-scan, transient absorption spectrum (TAS). The excited state absorption and negative refraction of Orange IV are observed in the Z-scan experiment at 532 nm. Meanwhile, two-photon induced excited-state absorption and pure two-photon absorption are observed at 600 nm and 700 nm with the pulse width of 190 fs, respectively. An ultrafast broadband absorption in visible wavelength region is observed via TAS. The different nonlinear absorption mechanism at multiple wavelengths is discussed and interpreted from the results of TAS. In addition, the ultrafast dynamics of negative refraction in the excited state of Orange IV is investigated via degenerate phase object pump probe, from which the weak long-lived excited state is extracted. All studies indicate that Orange IV has the potential to be further optimized into a superior broadband reverse saturable absorption material and also has certain reference significance for the study of optical nonlinearity in organic molecules containing azobenzene groups.

A continuous spatio-temporal database of accurate soil moisture (SM) measurements is an important asset for agricultural activities, hydrologic studies, and environmental monitoring. The Advanced Microwave Scanning Radiometer 2 (AMSR2), launched in May 2012, has been providing SM data globally with a revisit period of two days. It is imperative to assess the quality of this data before performing any application. Since resources of accurate SM measurements are very limited in Puerto Rico, this research will assess the quality of the AMSR2 data by comparing with ground-based measurements and perform a downscaling technique to provide a better description of how the sensor perceives the surface soil moisture as it passes over the island. The comparison consisted of the evaluation of the mean error, root mean squared error, and the correlation coefficient. Two downscaling techniques were used and their performances were studied. The results revealed that AMSR2 products tend to underestimate. This is due to the extreme heterogeneous distributions of elevations, vegetation densities, soil types, and weather events on the island. This research provides a comprehensive study on the accuracy and potential of the AMSR2 products over Puerto Rico. Further studies are recommended to improve the AMSR2 products.

Surface processes such as coatings, corrosion, photocatalysis, and tribology are greatly diversified by acid-base interaction at the surface overlayer. This study focuses on the action of metallic copper surface as an electron donor/acceptor related to inactivation of viruses; it was found that regarding Cu2O or Cu materials electrostatic interaction plays a major role in virus inactivation. We applied TPPE method to clarify the mechanism of electron transfer (ET) occurring at light irradiated copper surfaces. The TPPE characteristics was strongly influenced by the environments, which correspond to the temperature and environment dependence of the total count of emitted electrons in the incident light wavelength scan (PE total count, NT), the photothreshold, and further the activation energy (E) analyzed from Arrhenius plot of NT values obtained in the temperature-increase and subsequent temperature-decrease processes. In this study we reexamined the dependence of the TPPE data from two types of Cu metal surfaces: Sample A, which was mechanically abraded in alcohols, water, and air, and Sample C, which was only ultrasonically cleaned in these liquids. The NT for both Samples slowly increased with increasing temperature, reached a maximum (NTmax) at 250C (maximum temperature, Tmax), and after that decreased. For Sample A the NTmax value decreased in the order H2O > CH3OH > C2H5OH > (CH3)2CHOH > C3H7OH, although the last alcohol gave Tmax = 100C, while with Sample C the NTmax value decreased in the order: C3H7OH > (CH3)2CHOH > C2H5OH > CH3OH > H2O. Interestingly, both orders of the liquids were completely opposite: this means that Cu surface can possess two-way character. The NT intensity was found to be strongly associated with the change of the hydroxyl group (Cu-OH) to the oxide oxygen (O2) in the O1s spectra in the XPS measurement. The difference between the above orders was explained by the acid-base interaction mode of the Cu-OH group with the adsorbed molecule on the surfaces. The H2O adsorbed on Sample A produces the electric dipole Cu-OH+---:OH2 (--- hydrogen bond), while the C3H7OH and (CH3)2CHOH adsorbed on Sample C produce ROH+---:O(H)-Cu (R = alkyl groups). Gutmann’s acceptor number (AN) representing the basicity of the liquid molecules was found to be related to the TPPE characteristics: (CH3)2CHOH (33.5), C2H5OH (37.1), CH3OH (41.3), H2O (54.8) (the AN of C3H7OH could not be confirmed). With Sample A the values of NTmaxa and EaUp1 both increased with increasing AN (Up1 means the 1st temperature-increase process). On the other hand, with Sample C the values of NTmaxc and EcUp1 both decreased with increasing AN. These findings suggest that Sample A acts as an acid, while Sample C functions as a base. However, in the case of both types of Samples A and C the NTmax values were found to increase with increasing EUp1. It was explained that the EUp1 values depending on the liquids originate from the difference in the energy level of the hydroxyl group radical at the surface denoted. This is able to attract electrons in the neighborhood of the Fermi level of the base metal by tunnelling. After that due to Auger emission electrons are released, leading to the ET in the overlayer. These electrons are considered to have a strong ability of reducibility.

This article introduces a new way of using a Fibre Bragg Grating (FBG) sensor for detecting the presence and number of occupants in the monitored space in a Smart Home (SH). CO₂ sensors are used to determine the CO₂ concentration of the monitored rooms in an SH. CO₂ sensors can also be used for occupancy recognition of the monitored spaces in SH. To determine the presence of occupants in the monitored rooms of the SH, the newly devised method of CO₂ prediction, by means of an Artificial Neural Network (ANN) with a Scaled Conjugate Gradient (SCG) algorithm using measurements of typical operational technical quantities (indoor temperature, relative humidity indoor and CO₂ concentration in the SH) is used. The goal of the experiments is to verify the possibility of using the FBG sensor in order to unambiguously detect the number of occupants in the selected room (R104) and, at the same time, to harness the newly proposed method of CO₂ prediction with ANN SCG for recognition of the SH occupancy status and the SH spatial location (rooms R104, R203, and R204) of an occupant. The designed experiments will verify the possibility of using a minimum number of sensors for measuring the non-electric quantities of indoor temperature and indoor relative humidity and the possibility of monitoring the presence of occupants in the SH using CO₂ prediction by means of the ANN SCG method with ANN learning for the data obtained from only one room (R203). The prediction accuracy exceeded 90% in certain experiments. The uniqueness and innovativeness of the described solution lie in the integrated multidisciplinary application of technological procedures (the BACnet technology control SH, FBG sensors) and mathematical methods (ANN prediction with SCG algorithm, the Adaptive Filtration with of LMS algorithm) employed for the recognition of number persons and occupancy recognition of selected monitored rooms of SH.

Background: Smart transport plays a crucial role in urban planning and the transition from traditional to smart cities in developing countries with sustainability requirements.Traditional cities in developing countries often suffer from traffic congestion,air pollution, and inadequate public transportation systems,which can impede economic growth and negatively impact the quality of life for residents. Smart transport solutions can address these challenges by providing more efficient, sustainable.Here is a possible business model for an ITS system in a high-potential city in Iran,Based on IoT analysis, Mashhad city in Iran selected as a high-potential city to be changed to a smart city in the transportation sector.In the localization business model,the Uber smart transportation business model as a successful world experience is considered the base model. Methods: Based on the literature review,various criteria and sub-criteria were identified and determined in designing a new localized transportation business model.A questionnaire was prepared and filled with the help of 11 experts,method IAHP technique; Results:The present study showed the possibility of monthly payments for customers,software development, and the addition of luxury cars are among the most important sub-criteria with high weights;Conclusions: Working with transport companies,schools and universities as key partners solves reaching a strong and sustainable smart transportation system.

This study assessed stakeholders’ perception on the prospects and challenges of practising Climate–Smart Agroforestry in the Asunafo North Municipal Assembly in the Ahafo Region in Ghana. Interviewer administered questionnaires were used to collect cross-sectional data from 250 cocoa, food and cash crop farmers drawn from four (4) communities using simple random sampling. And nine (9) in-depth interviews were also conducted to elicit key stakeholder perspectives. Stakeholders also held the view that Climate-Smart Agroforestry encourages the use of stress-tolerant crop varieties, increase income, improve soil productivity, diversify farm produce, urges the reduction of input supply and recommends the timely usage of the inputs, ensures better market system for farm produce, cost of production decreases during the practice of Agroforestry, agro-ecological functions and Climate-Smart Agroforestry can best help adapt to the threats of climate change in the agriculture sector. The challenges included insecure tree ownership right, fast-declining soil fertility and long maturity period of Agroforestry trees, land tenure issues, illegal tree logging (chainsaw operators) on farms, lack of practical understanding of the approach, inadequate knowledge and information and inadequate supportive facilities in the study area. The study recommends further studies on the existing forest policy and legislations and its implications on the practice and adoption of Climate-Smart Agroforestry in respond to climate change in the area.

In the smart city, information and communications technologies (ICTs) are proposed as solutions to urban challenges, including sustainability concerns. While sustainability commonly refers to economic and environmental dimensions, the concept also contains a social component. Our study asked how smartphone applications (apps) address social sustainability challenges in urban transport, if at all. We focused on transport disadvantages experienced due to low income, physical disability, and language barriers. A review of 60 apps showed that transport apps respond to these equity and inclusion issues in two ways: (a) by employing universal design in general-use apps; including cost-conscious features; and providing language options (b) by specifically developing smartphone apps for persons with disabilities. The article discusses the study by positioning it in the literature of smart cities as well as socially sustainable transport.

The construction industry has witnessed the increasing use of digital tools and smart solutions, particularly, in the realm of buildings energy automation within the concept of smart cities. While to truly realize the potential benefits of smart cities, a broader scope of smart initiatives is required to support the transition from smart buildings towards smart neighbourhoods, appreciated as the critical unit of urban development. To support this interplay of smart solutions between buildings and neighbourhoods, this article aims to collect and review all smart solutions presented in existing scientific articles, technical literature, and realized European projects. These solutions are classified under two main sections of buildings and neighbourhoods, which are investigated through five domains: buildings energy-related uses, renewable energy sources, water, waste, and open space management. Showcasing the quantitative outcomes demonstrates the potential benefits of implementing smart solutions from buildings to neighbourhoods. Moreover, this research concludes that the true enhancement of energy conservation goes beyond the buildings’ energy component and can be genuinely achieved by integrating intelligent elements of the neighbourhood, due to their strong interdependencies. Future research is recommended to focus on assessing the effectiveness of these solutions towards resource conservation.

During the last decade, smart materials have increasingly impacted on several niches, among which that of one-off/limited edition experimental fashion. Thanks to their performativity, due to the implementation of Smart Materials Systems, they have reached indeed catwalks as well as museums and galleries. As boundaries between what-is-art and what traditionally was not supposed to be art are now turning into osmotic membranes, zooming on how smart materials are highly contributing to outline the new creative landscape can provide with interesting and compelling issues. Introducing three different areas of experimental fashion, named Multi-sensory dresses, Empathic dresses, and Bio-smart dresses and accessories, respectively covering the world of in-Lab experiments and design collaborations in relation to the application of advanced smart materials systems, the article discuss some of the implications in term of Design Thinking and Design Aesthetics.

Despite the available literature on traumatic brain injury (TBI) biomarkers elsewhere, data are limited or non-existent in sub-Saharan Africa (SSA). The aim of the study was to analyse associations in acute TBI between admission serum biomarker concentrations and TBI severity, CT-scan findings and outcome as well as explore the influence of concurrent Toxoplasma gondii infection. Concentrations of serum biomarkers (GFAP, NFL Tau, UCH-L1, and S100B) were measured and Toxoplasma gondii detected in samples obtained <24 hours post-injury. The GOSE was used to evaluate 6 months outcome. All biomarkers levels increased with severity of TBI, but this increase was significant only for NFL (P=0.01). GFAP values significantly increased (P=0.026) in those with unfavorable outcome. Tau levels were higher in those who died (P=0.017). GFAP and NFL were sensitive to CT-scan pathology (p values respectively 0.004 and 0.002). S100B levels were higher (P<0.001) in TBI patients seropositive to Toxoplasma gondii. NFL was found to be sensitive to TBI severity while NFL and GFAP were predictive of CT intracranial abnormalities. Increased levels of GFAP and Tau were associated with poorer outcomes 6 months after TBI. S100B levels were significantly higher in T. gondii seropositive TBI patients.

Deterioration of concrete subjected to freezing and thawing climatic conditions is one of most important factors affecting the durability of concrete infrastructure in cold climates. The freeze-thaw resistance of cementitious materials like concrete and mortar can be determined by the CDF test (Capillary Suction of De-icing chemicals and Freeze-Thaw Test). Here, concrete specimens are subjected to repeated freeze-thaw cycles with simultaneous addition of de-icing salt and the amount of material weathered near the surface is determined. For concretes with adequate freeze-thaw resistance, this test method works very well. However, specimens with inadequate or unknown performance often experience increased edge weathering, which is caused by the detachment of the lateral isolation tape. The increasing edge influence thus leads to a falsification of the results and consequently to an underestimation of the actual freeze-thaw resistance of the material. In materials research in particular, however, concretes with high levels of weathering are studied in order to be able to investigate various factors of influence on the freeze-thaw resistance of concretes in a targeted manner. This paper presents a novel methodology that delivers new information regarding the weathering of CDF test samples and the associated distribution function of the height decrease using high resolution 3D scan data. The results indicate a correlation between the progression of the distribution function and the sample's maximum aggregate size. The change of the sample volume can be used to support the weathering results of the standard CDF methodology. The increase of the surface area is used to estimate the tortuosity of the sample surface. It indicates an asymptotic curve approaching a specific maximum value, which is dependent on the the weathering depth of the sample.

The current paper aims to study the impact properties of additively manufactured Maraging steel (1.2709) using laser powder bed fusion (PBF-L) processing. The specimens were manufactured using 3Dsystems ProX 300 equipment under constant specific power input, or Andrew Number. The interactions between the build strategy and parameters, such as Hatch spacing and Scan speed was, and the impact strength and fracture were investigated. The Impact energy anisotropy was also investigated parallel and perpendicular to the build direction. Instrumented impact testing was performed, and the fractography supported that the fusion zone geometry dictated the fracture behaviour. The influence from gaseous elements such as Nitrogen, Oxygen and Hydrogen was found insignificant at the levels found in the printed material.

The paper is a follow-up of a previous investigation and effort to develop the ontology of the smart city (Komninos, N., Bratsas, C., Kakderi, C., and Tsarchopoulos, P. "Smart city ontologies: Improving the effectiveness of smart city applications". Journal of Smart Cities, vol. 1(1), 1-17. https://www.komninos.eu/wp-content/uploads/2015/07/2015-Smart-City-Ontologies-Published.pdf). Since the publication of this article in 2015, research and literature on smart cities have evolved significantly, as have the technologies for digital spaces and applications that support city functions. These developments are reflected in the present form of the smart city ontology 2.0 we propose. It depicts the building blocks of the smart city ontology (technologies, structure, function, planning), and the object properties and data properties that connect structural blocks and classes. The aim of the SCO 2.0 is to provide a better understanding and description of the smart/intelligent city landscape; identify the main components and processes, the terms used to describe them, their definition and meaning; clarify key processes related to the integration of the different dimensions of the smart city, mainly the physical, social, and digital dimensions. The paper is accompanied by an owl file, developing the ontology through the editor Protégé.

The present work proposed a low-cost portable device as an enabling technology for Smart Farms using multispectral imaging and Machine Learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed an good prediction performance with R2 of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences.

The goal of the paper is to investigate the expected participation and mentality of smart citizens in smart cities. The key question is the role of the human factor in smart environments globally studied through a research corpus of the mainstream summaries, trend reports, white papers and visions of business – governmental – university research co- operations. Foremost, a short review of the changing scholarly trends is presented as a theoretical framework. Concerning its key ideas, the corpus based findings are recapped and analysed by content networks and the most referred city strategies. Besides, a critical approach reveal further required factors and risks to investigate. The ultimate goal is to understand how the smart city landscape is shaped by citizen-based strategies, open data, empowerment and responsibility. Accordingly, the paper closes with theoretical, practical and metaphor-based recommendations to support the business and political decision making, and also, the emerging scholarly trends in the context of upcoming technological-structural changes.

This publication aims to create 4K UHD video sequences with a specific focus on smart cities and transport. The created database will be accessible to all experts in the field and can be further used for research purposes. All reference video sequences will be transcoded into different quality settings. The resolution, compression standard, and bitrate will be changed. The work will aim to find out the best combination of video parameters from the measured values. Thanks to the overall evaluation, we will ensure for each single video sequence the quality with the best experience for the providers that will offer the service. The database will also be available with its objective and subjective ratings, with dynamicity information characterized by spatial and temporal information. After, It will be possible to compare the results of the subjective evaluation of a selected sample of our respondents with the work of other researchers who can evaluate with a different sample of evaluators.

There are limited research articles focusing on Smart City Assessment (SCA) applications since it is a relatively new field of research and practice. However, numerous studies have been conducted and published to date, particularly in developing countries, with the broad objective of building theoretical frameworks that are centered on smart city assessments. This scoping review systematically provides an examination on the available literature on SCA, with a goal of synthesizing smart city assessments in developing economies. In order to improve the quality and transparency of the reviews and meta-analysis, as well as to reduce the risk of bias, this paper adopted the PRISMA scoping review research design to analyze 25 journal articles. Results showed that conceptual modeling appears to be the most common method identified while industrial development emerged as the most common objective identified in the MFO Model. On the other hand, ISO 37122:2019 was the most prevalent framework used in the collected sample size with 6 journal articles followed by IoT-Enabled Smart City Framework with 5 journal articles while Smart Cities Index Framework obtained 3. Meanwhile, India emerged as a leader in the global Smart City movement followed by Malaysia and Africa. The Qualitative Research Design approach was the most common among the literatures while social science was the most common subject area among the 25 journals being studied. More so, sustainability and renewable energy are the two most important assessment categories in SCA tools. By collating and evaluating different criteria and metrics in existing SCA, cities can learn from their successes and failures, adjust their strategies, and share best practices with other cities. This can foster a culture of continuous improvement and innovation in urban governance, and ultimately lead to more livable, resilient, and prosperous cities for all.

This research seeks to understand the impact of digital platforms on the governance of smart cities. It uses the Scaffold of Popular Participation to classify digital platforms and better understand participatory governance in cities, its benefits and complications, correlating them with the SDGs of the 2030 Agenda. It was also possible to list points of attention for the development of digitization in the public sector, including in Brazil. It concluded that the digitization of public services is in progress, pressured by the civil body for greater transparency and participation. In addition, the platforms must be guided by civil inclusion in the discussion of public policies, by the training of intelligent citizens and by sustainable guidelines that guide the themes addressed. In contrast, platforms can also facilitate deception and popular manipulation, which represents a high risk if used for this purpose.

In this paper we designed system for smart university building based on artificial intelligence (AI) and internet of things (IOT). Our idea can be summarized in smart security system that has different sensors to detect the surrounding environment of the class room in campus which keep everyone and everything on campus safer. By using (IOT), (AI) technologies and applications and by using microcontroller programming we can make the university building safer, secure and more energy saves.

Objects recognition is a necessary task in smart city environments. This recognition can be used in processes such as the reconstruction of the environment map or the intelligent navigation of vehicles. This paper proposes an architecture that integrates heterogeneous distributed information to recognize objects in intelligent environments. The architecture is based on the IoT / Industry 4.0 model to interconnect the devices, called Smart Resources. Smart Resources can process local sensor data and send information to other devices. These other devices can be located in the same operating range, the Edge, in the same intranet, the Fog, or on the Internet, the Cloud. Smart Resources must have an intelligent layer in order to be able to process the information. A system with two Smart Resources equipped with different image sensors has been implemented to validate the architecture. Experiments show that the integration of information increases the certainty in the recognition of objects between 2\% and 4\%. Consequently, in the field of intelligent environments, it seems appropriate to provide the devices with intelligence, but also capabilities to collaborate closely with other devices.

Can the smart city provide a new perspective for public and academic libraries? How does the smart city impact the libraries as cultural and scientific assets? And how can libraries contribute to the development of the smart city? An overview of recent library models, like the learning center or the green library, reveals affinities with the concept of the smart city, especially regarding the central role of information and the integration of technology, people and institution. From this observation, the paper develops the outline of a new concept of the smart library which can be described on four dimensions, i.e. smart services, smart people, smart place and smart governance. However, the smart library concept does not constitute a unique model or project but a process, a way how to get things done, less linear, less structured, more creative and innovative. Also, smartness may not be a solution for all library problems.

Purpose: Localization and mapping with LiDAR data is a fundamental building block for autonomous vehicles. Though LiDAR point clouds can often encode the scene depth more accurate and steadier compared with visual information, laser-based Simultaneous Localization And Mapping (SLAM) remains challengeable as the data is usually sparse, density variable and less discriminative. The purpose of this paper is to propose an accurate and reliable laser-based SLAM solution. Design/methodology/approach: The method starts with constructing voxel grids based on the 3D input point cloud. These voxels are then classified into three types to indicate different physical objects according to the spatial distribution of the points contained in each voxel. A global environment model with Partition of Unity (POU) implicit surface is maintained along the process and located voxels are merged into it from stage to stage, through scan-to-model matching implemented by Levenberg-Marquardt method. Findings: We find a laser-based SLAM method. The method uses POU implicit surface representation to build the model and is evaluated on the KITTI odometry benchmark without loop closure. Experimental results indicate that the method achieves accuracy comparable to the state-of-the-art methods. Originality/value: We propose a novel, low-drift SLAM method, which falls into a scan-to-model matching paradigm, operates on point clouds obtained from Velodyne HDL64. The method is of value to researchers developing SLAM systems for autonomous vehicles.

1) Background: We analyzed, by PET-SCAN, how growth hormone (GH) could act on the brain of an older woman, not GH-deficient, which was beginning to show some cognitive deficiencies and presented an ApoE genotype 4/3; 2) Methods: After performing a first psychometric study (TAVEC verbal learning test), the metabolic activity of brain structures related to knowledge, memory, and behavior was analyzed using 18-F Fluorodeoxyglucose PET-SCAN. The patient was then treated with GH (0.4 mg/day) for three weeks and on the last day under this treatment, a new PET-SCAN was performed. One month after beginning treatment with GH, a new TAVEC test was performed; 3) Results: GH administration normalized the cognitive deficits observed in the first psychometric test and increased significantly (P < 0.025) the metabolic activity in practically all brain cortical areas, specifically in the left hippocampus and left amygdala, although not in the left parahippocampus; and 4) Conclusions: This is the first study in which the positive effects of GH on cerebral metabolism have been visualized in a human patient. Our data confirm the positive effects of this hormone on cognition, memory and behavior in patients affected by mild cognitive impairments.

Our planet faces several serious and urgent challenges to sustainability, not just climate change. Most researchers argue that technological solutions can solve these problems. This review paper first examines the prospects for decoupling environmental damages in general from economic growth, considered at the global level, then looks at whether the recent advances in Information and Communication Technology (ICT) can help. It is argued that although absolute decoupling might have occurred in some countries, even after accounting for energy-intensive imports, it has not occurred at the global level, which is the relevant level for global sustainability problems. This conclusion is strengthened by the very high correlation over the past three decades found between GDP and several parameters relevant for sustainability, particularly for atmospheric CO2 ppm and ecological footprint as a function of Gross Domestic Product (GDP). ICT innovations relevant to energy use include smart grids and smart cities, especially smart urban transport. A review of recently published papers shows no definite findings of energy or carbon reduction, although some innovations show energy/carbon reduction potential, if given strong policy support. However, the needed policies could well give marked reductions even without ICT approaches. Overall, it is concluded that Earth’s sustainability challenges will necessitate deep energy reductions, which in turn require profound sociopolitical changes.

Ever-increasing need for improving the livability of a city and improve outcomes for its residents, over the last decade, the adoption of technology to develop urbanised societies around the world has given rise to the need for developing smart cities. The speed at which the world population is growing, the use of Internet of Things in smart cities have really advanced the quality of life. One significant area of concern within the smart city framework is waste management. If the waste within a city is not adequately managed, then it leads to issues in the health of the citizens. Additionally, the waste management has such a high impact on the environmental footprint, hence the need to have a smart way of managing waste is of critical importance. Through our research, we analyse the challenges of waste management within a city to understand the impact of the problem on to the citizens and overall city operations. We then investigate ways in which we can solve these problems using the emerging technologies, such as the Internet of Things, to collect valuable data of large volumes arriving at an astronomical rate, then apply multi-agent deep reinforcement learning algorithms to harness the power of big data to extract meaningful information and actionable insights. We ingest data generated by our Internet of Things into our algorithm for three main purposes including providing the notifications to an external system, for example, a map navigation engine out of the scope for this project but a future extension for route optimisation and waste vehicle tracking; extracting and reporting the actionable insights from the underlying data; and consuming the extracted data for predictive forecasting to draw out the unknown patterns of waste fill levels within various geographical locations and again send out triggers and notification to external systems for example a waste collection authority who can efficiently schedule the waste collection vehicles and optimise the route. To achieve the above mentioned outcomes, we propose a framework that is agnostic of the hardware that it connects to and can effectively interface with a wide variety of hardware keeping a level of abstraction in the architecture.

The final assembly of appliances and automotive industries is a rigorous process, that has limited capabilities of full traceability associated with: (1) the parts installed, (2) their fabrication processes, and (3) the assembly work. This is also the case for each of its sub-assemblies The many parts and sub-assemblies that compose the final assembly, make full traceability a challenging fit, that is almost unsurmountable. Such full traceability along the entire supply-chain is not existent today and must be based on documentation of most assembled parts, assembly tasks, and inspection tasks that compose the full assembled product. In addition, security measures are needed to prevent hostile hacking and unauthorized approach to the assembly documentation throughout the entire supply chain. The related documentation and repeated verifications require considerable effort and have many chances for human errors. So, automating these processes has a great value. This article expounds a framework that harnesses block-chain and smart-contract technology to offer traceable and protected documentation of the assembly process. We expand the concept of a Bill-Of-Assembly (BOA) to incorporate data from the bill of materials (BOM), the associated assembly activities, the associated activities’ specification parameters and materials, and the associated assembly resources (machines and/or operators). The paper defines the operation of the BOA with blockchain and smart-contract technology, for attaining full traceability, safety, and security, for the entire assembled product. Future research could extend the proposed approach to facilitate the usage of the BOA data structure in constructing a digital twin of the entire simulated system.

Smart Living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for Smart Living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in Smart Living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of Smart Living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in Smart Living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of Context Awareness, Data Availability, Personalization, and Privacy in HAR, this paper serves as a valuable resource for researchers and practitioners striving to advance Smart Living services and applications.

Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains: Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to explore further and advance the field of human action recognition in smart living.

Infrastructure and urban network operators, city users and industrialists are faced with complex issues to ensure the sustainability of the service, maintain, operate and develop their urban sys-tems, while integrating environmental, economic and societal impacts and being resilient to un-expected geopolitical and climatic upheavals. Urban Digital Twins (UDTs), i.e., digital twins for smart cities and territories are considered as promising tools that can enable all stakeholders to collaborate across disciplinary silos and foster digital transformation in urban and territorial pro-jects to ensure sustainability, resilience and increased inventiveness. However, a widely acknowledged challenge to address in the application of UDTs is the lack of interoperability. One significant barrier is the difficulty that stakeholders have in understanding the level of complexity hidden behind this challenge, and thus the adoption of an adequate interoperability technique. This paper proposes a conceptual framework, which is unique for understanding the various lev-els of interoperability required in deploying and using the full potentials of UDTs. We show how such a high-level modeling approach supports the symbolic manipulation of UDTs interoperabil-ity notions and requirements, and thus eases the design of appropriate solutions for large-scale in-teroperable UDTs.

Due to the irruption of new technologies in cities such as mobile applications, geographic infor-mation systems, Big Data, Internet of Things (IoT) or Artificial Intelligence (AI), new approaches to citizen management are being developed with the aim of adapting citizen services to this new en-vironment. These new services can enable city governments and businesses to offer their citizens a truly immersive experience that facilitates their day-to-day lives and ultimately improves their quality of life. In this sense, it is important to emphasize that all investments in infrastructure and technological developments in smart cities will be wasted if the citizens for whom they have been created eventually do not use them for whatever reason. To avoid these kinds of problems, the citizen's level of adaptation to the technologies should be evaluated. However, although much has been studied about new technological developments, studies to validate the actual impact and user acceptance of these technological models are much more limited. This paper tries to fill this gap presenting a new model of recommender system based in the most cited and used model in the scientific and academic literature: the Technology Acceptance Model (TAM) and using the most cited and agreed upon criteria in the existing literature. To accomplish the objective, this study in-troduces a novel recommender system that utilizes a fuzzy 2-tuple linguistic model in conjunction with the analytic hierarchy process (AHP) method to prioritize and personalize the relationship between tourists and smart cities. The methodology proposed in this paper was tested and validated in a case of study through different clusters to derive global recommendations tailored to each specific cluster. The main findings reveal that the use of technology is closely linked to the ability to enjoy personalized experiences in the realm of Smart Cities and Smart Tourism. As future works, authors recommend extending the recommender system model to any cluster of tourists using the proposed methodology and evaluate also other kind of disruptive technologies such as artificial intelligence (AI) in supporting the citizens.

Developing a low-cost wireless energy meter with power quality measurement for smart grid applications represents a significant advancement in efficient and accurate electrical energy monitoring.In increasingly complex and interconnected electrical systems, this device is essential in various applications, such as smart grids, enabling real-time energy usage monitoring. In such a context, smart meters expand the possibilities for sustainable and efficient energy use and promote better utilization of energy sources, especially non-renewable ones. According to the 2020 International Energy Agency (IEA) research, non-renewable energy sources represent 65% of the global total. The smart meter developed in this work is built based on the ESP32 microcontroller and easily accessible components, using a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of the smart meter development was to enhance the software and simplify the hardware. Unlike traditional meters that perform electrical parameter calculations using complex hardware circuits, this project performed the calculations directly on the microcontroller. This approach reduced the complexity of the hardware, simplifying the meter’s design. With the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters were achieved without the need for additional circuits. This software-driven approach with simplified hardware brought benefits such as reduced production costs, lower energy consumption, and increased meter development and updates flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the transmission of collected data to remote monitoring systems for later analysis. The developed smart meter offers cost, energy efficiency, and flexibility benefits. It represents an innovative, efficient, and versatile solution for smart grid applications.

Climate change and global warming are becoming important problems around the globe. To prevent these environmental problems, many countries try to reduce the emissions of greenhouse gases (GHG) and manage the consumption of energy. In Korea, Korea Electric Power Corporation (KEPCO) has introduced Smart Grid (SG) technologies to its branch office in 2014. This was the first demonstration of smart grid on a building called Smart Grid Station. This paper treats the achievements of the Smart Grid Station (SGS) by its early target in three aspects. The things are peak reduction, power consumption reduction and electricity fee saving. The authors analyzed the achievements by comparing the data of 2015 with the data of 2014. Through the evaluation, the authors studied the case, proved the advantages of SGS, and suggested the requirements to improve and the direction to go of the system.

.Background: Neither radiological phenotypic characteristics nor reconstruction CT scan to study the cranial contour in children with the so called the idiopathic type of West syndrome has been implemented to reach the etiology understanding. Material and Methods: The basic diagnostic measures and the traditional antiepileptic treatments have been applied to these children in accordance with the conventional protocol of investigations and treatment for children with West syndrome. Boys from three unrelated families were given the diagnosis of idiopathic type of West syndrome. Three boys aged 6,7,9 and three parents (age range of 28-41 year) were included in this study. These children and their parents were referred to our orthopedic departments because of variable skeletal deformities. All showed the history of intellectual disabilities, cryptogenic epileptic spasms and fragmented hypsarrhythmia. Recently, variable forms skeletal deformities of flat foot, torticollis and early onset osteoarthritis. We performed detailed clinical and radiological phenotypic characterization of every affected child, siblings and parents. All affected children underwent whole exome sequence analysis Results: The craniofacial phenotype of all children revealed apparent disruption of development of the cranial bones. Palpation of the skull bones showed unusual palpable boney ridges along different sutural locations. A-7-year-old child showed abnormal bulging over the sagittal suture associated with bilateral boney ridges over the sequamosal sutures. Examining his parents, we noticed a similar skull radiographic abnormality in his mother radiographs. A-10-year-old boy showed an extremely narrow frontal area, facial asymmetry and a well palpable ridge over the lambdoid sutures. 3D reconstruction CT scan confirmed the diagnosis of early closure . His mother showed typical radiographic skull phenotype with a history of postadulthood scoliosis. A- 12-year-old boy showed brachycephaly. Investigating his parents revealed a brachycephalic mother with border line intellegence. We affirm that the pattern of inheritance in the three boys was compatible with X-linked recessive pattern of inheritance . Whole exome sequencing showed non definite phenotype/genotype correlation. Conclusion: The aim of this study was fivefold. First, to refute the common usage of the term idiopathic and we affirm that our findings are Novel. Second, it could be possible that West syndrome is a symptom complex rather than a separate diagnostic entity. Third, to further detect the genetic carrier , we explored the connection between the cranial bones in the children with West syndrome with what is seen in parents.. Fourth, the differences in cranial bones abnormalities among every child and another seems to be heterogeneous. Fifth, it seems that the progressive deceleration in the development of this group of children is highly connected to the progressive closure of the cranial sutures.

Diffusion-controlled cyclic voltammograms at fast scan rates show peak shifts as well as decreases in the peak currents from the predicted diffusion-controlled currents especially when currents are large in low concentration of supporting electrolyte. This has been conventionally recognized as an IR-drop effect by solution resistance on the peaks as well as heterogeneous kinetics. It is also brought about by negatively capacitive currents associated with charge transfer reactions. The reaction product generates dipoles with counterions to yield a capacitance, of which current flows oppositely to that of the double layer capacitance. The three effects are specified here in the oxidation of a ferrocenyl derivative by fast scan voltammetry. The expression for voltammograms complicated with IR-drop is derived analytically, and yields deformed voltammograms. The peak shift is approximately linear with the IR-term, but exhibits a convex variation. Dependence of some parameters on the peaks by the IR-drop is compared with those by the negative capacitance. The latter is more conspicuous than the former under conventional conditions. The two effects cannot be distinguished specifically except for variations of conductance of the solution.

Current commitments by European governments seek to improve energy consumption as a means to reduce carbon emissions from building stock by 2050. Within such context, retrieving reliable three-dimensional contours from point clouds becomes an important step in developing facade retrofitting solutions since facade retrofitting projects often make use of as-built 3D models to help reduce inaccuracies by narrowing interpretation and measurement errors. This work aims to provide a method that uses topology-based parametric modelling for reconstructing building envelopes from point clouds. Through a semi-automated process that gives permanent visual feedback, the user adjusts parameters to custom standards of acceptability. A solution under the form of a Grasshopper definition delivers building envelope 3D contours in various file formats as a means for increasing interoperability. The main contributions of this work consist of a parametric reconstruction workflow capable of solving building topology for retrieving 3D contours, a strategy to bypass point cloud occlusion, and a strategy for converting those contours into an IFC model directly from the parametric modelling environment.

The thorough description of the peculiarities of additively manufactured structures represents a current challenge for aspiring freeform fabrication methods, such as the selective laser melting (SLM). All of which have an immense advantage in the fast fabrication (no special tooling or moulds required), the geometrical flexibility in the design of components, and their efficiency when only low quantities are required. However, designs demand the precise knowledge of the material properties, which in case of additively manufactured structures are anisotropic and, under certain circumstances, in addition of an inhomogeneous nature. Furthermore, these characteristics are highly dependent on the fabrication settings. Within this study, the anisotropic tensile properties of selective laser melted stainless steel (1.4404, 316L) are investigated: The Young’s modulus ranged from 148 GPa to 227 GPa, the ultimate tensile strength from 512 MPa to 699 MPa and the breaking elongation ranged, respectively, from 12% to 43%. The results were compared to related studies, in order to classify the influence of the fabrication settings. Furthermore, the influence of the chosen raw material was addressed by comparing deviations on the directional dependencies reasoned by differing microstructural developments during manufacture. Stainless steel was found to possess its maximum strength at a 45° layer versus loading offset, which is precisely where AlSi10Mg was previously reported to be at its weakest.

Smart Grid systems have become popular and necessary for the development of a sustainable power grid. These systems use different technologies to provide optimized services to the users of the network. Regarding computing, these systems optimize electrical services by processing a large amount of data generated. However, privacy and security are essential in this kind of system. With a large amount of data generated, it is necessary to protect the privacy of users, because this data may reveal users’ personal information. Today, blockchain technology has proven to be an efficient architecture for solving privacy and security problems in different scenarios. Over the years, different blockchain platforms have emerged, attempting to solve specific problems in different areas. However, the use of different platforms fragmented the market, which was no different in the smart grid scenario. This work proposes a blockchain architecture that uses sidechains to make the system scalable and adaptable. We used three blockchains to ensure privacy, security, and trust in the system. To universalize the proposed solution, we used the OSGP protocol and smart contracts. The results show that architecture security and privacy are guaranteed, making it feasible for implementation in real systems. Although scalability issues regarding the storage of data generated still exists.

Electricity sector is among the main carbon emissions contributors. This sector has the potential to reduce its carbon emissions through producing electric energy from zero emitting facilities and through the optimization of the consumption in a way to better accommodate low carbon emissions. Hydrogen society per se combined with smart grids, as this is analyzed in this manuscript, can present substantial contribution towards climate change mitigation.

The mHealth is a term that is used for mobile health supported by smart devices such as mobile phones, tablets, and wearable smart devices, etc. The smart devices strengthen the efficiency and effectiveness of interaction with patients, physicians, and specialists. Patients nowadays would like to be intimately involved in their diagnosis as well as to make more informed decisions concerning their care. It has begun to measure the success of the quality of treatment. This was a reason that patients trust mHealth to provide them with consistency in their communications with the physicians. Most wireless strategies do not measure up to this standard so that patient engagement ultimately ended up decreasing. The blockchain can boost mHealth through storing and sharing electronic data securely and transparently. It can enhance the accessibility of patient information in real-time. The Internet of Things (IoT) provides a unique identification number to every connected device such as mobile devices, medical devices, and wearable devices. This framework uses the blockchain and IoT technologies together to provide quick help to the patients, monitor remotely, reduce the cost and unnecessarily hospitalization physically and find the real diagnosis. In order to increase patient involvement, mHealth framework with blockchain and IoT technologies has built with the key objective of providing patients with full information on their treatment and diagnosis.

Because of the significant enabling role smart meters can play in securing the transition towards sustainable energy distribution, the paper provides insights to support smart meters implementation projects. Energy utilities must propose adequate solutions to manage grid-upgrading projects and, in the meantime, increase efficiency levels. Based on empirical data analysis the paper provides insights aimed at maximize probability of success of smart meters projects. Results show common patterns of variables that may support project undertakers, policymakers and scholar when it comes to analyze projects to predict to maximize opportunities. For smart meters projects to succeed, regulatory stability is essential as long-period investments grids produce benefits for energy utilities, and for society.

Background Total serum bile acids (TSBA), liver-spleen scan and dynamic liver functional tests, in our case the antipyrine clearance (Ap Cl), have been long adopted. The aim of our study was that of assessing whether some diagnostic tests, scarcely performed due to the novel techniques implementation, were reliable diagnostic tools. Methods Data extracted from records of two well-matched for age and gender populations was retrospectively analysed. Specifically, 17 patients with biopsy-proven chronic hepatitis were confronted with 17 subjects suffering from liver cirrhosis. Clinical, laboratory and instrumental findings, such as the Child-Pugh classification, the number connection test for evaluating hepatic encephalopathy, prothrombin time, serum albumin levels, TSBA concentration, Ap Cl determination, abdominal ultrasound and liver-spleen scan imaging as well as endoscopy features were evaluated. Results Cirrhotics showed a median concentration of TSBA increased respect to that of patients with chronic hepatitis, independently from gender, 20.1 versus 12.24 micromol/L, P= 0.0054. The median AP Cl value of the patients with liver cirrhosis was reduced confronted with that of patients with chronic hepatitis, specifically, 13.92 opposed to 18.3 mcg Ap/dL, P= 0.045. Furthermore, the median liver-spleen scan score was higher in cirrhotics than in chronic hepatitis patients, i.e., 3.47 versus 1.47, P= 0.000. The AUROCs of TSBA levels and of the liver-spleen scan scores for differentiating patients with liver cirrhosis from those with chronic hepatitis were 0.82 and 0.96, respectively. When was applied a new predictive model, combining the previous ones, the AUROC to discriminate patients belonging to the two populations was 0.98. The best cut-off of the new index was 26, with a sensitivity and specificity of 100.00% and 82.35%, respectively, correctly classifying 91.18% of the patients. Discussion Both TSBA and liver-spleen scan were high discriminant as well as their combination, while Ap Cl showed some limitations as reliable diagnostic tool. TSBA and liver-spleen scan have been too soon and unnecessarily overlooked.

The current paradigm shift in orthodontic treatment planning is based on facially driven diagnostics. This requires an affordable, convenient, and non-invasive solution for face scanning. Therefore, utilization of smartphones` TrueDepth sensors is very tempting. TrueDepth refers to front-facing cameras with a dot projector in Apple devices that provide real-time depth data in addition to visual information. There are several applications that tout themselves as accurate solutions for 3D scanning of the face in dentistry. Their clinical accuracy has been uncertain. This study focuses on evaluating the accuracy of the Bellus3D Dental Pro app, which uses Apple's TrueDepth sensor. The app reconstructs a virtual, high-resolution version of the face, which is available for download as a 3D object. In this paper, sixty TrueDepth scans of the face were compared to sixty corresponding facial surfaces segmented from CBCT. Difference maps were created for each pair and evaluated in specific facial regions. The results confirmed statistically significant differences in some facial regions in amplitudes greater than 3 mm, suggesting that current technology has limited applicability for clinical use. The clinical utilization of facial scanning for orthodontic evaluation, which does not require accuracy in the lip region below 3 mm, can be considered.

In this article, we analyse the computer aid screening method of COVID19 using Xray and CT scan images. The main objective is to set an analytical closure about the computer aid screening of COVID19 among the X-ray image and CT scan image. The computer aid screening method includes deep feature extraction, transfer learning and traditional machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN model. The machine learning approach includes three sets of features and three classifiers. The pre-trained CNN models are alexnet, googlenet, vgg16, vgg19, densenet201, resnet18, resnet50, resnet101, inceptionv3, inceptionresnetv2, xception, mobilenetv2 and shufflenet. The features and classifiers in machine learning approaches are GLCM, LBP, HOG and KNN, SVM, Naive bay’s respectively. In addition, we also analyse the different paradigms of classifiers. In total, the comparative analysis is carried out in 65 classification models, i.e. 13 in deep feature extraction, 13 in transfer learning and 39 in machine learning approaches. Finally, all the classification models perform better in X-ray image set compare to CT scan image set.

The Coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared as a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a Machine Learning based pipeline to detect the COVID-19 infection using the lung Computed Tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19 affected CTI using Social-Group-Optimization and Kapur’s Entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection and fusion to classify the infection. PCA based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test and validate four different classifiers namely Random Forest, k-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task and for the classification task the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose the ongoing COVID-19 infection.

The background of this work is related to the scheduling of household appliances, taking into account variations in energy costs during the day, due to official Brazilian domestic tariffs: constant and white. The white tariff can reach an average price of around 17\% lower than the constant, but charges twice its value at peak hours. In addition to cost reduction, we propose a methodology to reduce user discomfort due to time shifting of controllable devices, presenting a balanced solution through the analytical analysis of a new method here called tariff space, derived from white tariff posts. To achieve this goal, we explore the geometric properties of the movement of devices through the tariff space (geometric locus of the load), over which we can define a limited region in which the cost of a load under the white tariff will be equal to or less than the constant tariff. As a trial for the efficiency of this new methodology, we collected some benchmarks (such as execution time and memory usage) against a classic multi-objective algorithm (Hierarchical) available in the language portfolio in which the project has been executed (Julia language). As a result, both methodologies achieve similar results, but the one presented in this article shows a significant reduction in processing time and memory usage, which could lead to the future implementation of the solution in a simple, low-cost embedded system like an ARM cortex M.

- Power grids of the future will likely incorporate more renewable energy distributed generation (REDG), also known as alternative energy systems. REDG units are increasingly being used in electrical transmission networks because to the overall positive effects they have on power networks. REDG systems are the backbone of smart electric networks and are essential to the operation of the smart grid. These REDG systems can additionally improve system reliability by providing some customers with a backup generator in the event of power interruptions. The report offers a thorough evaluation of the existing body of information on the topic of electric vehicles' (EVs') future interactions with the smart grid. The combination of the EVs' potential deployment and the smart grid's conceptual goal presents challenges in electric grid infra-structure, communication, and control. The proposal for connecting EVs to the grid is based on research into cutting-edge smart metering and communication systems. In the context of the vehicle-to-grid (V2G) phenomenon, the possibilities, benefits, and limitations of various EV smart charging systems are also fully examined. There is a fast growing percentage of distributed energy that is wind and solar photovoltaic. Their variable power output, however, introduces fresh challenges for those responsible for organizing, operating, and controlling the power grid. While fluctuations in the electric grid are problematic, they may be mitigated by the entry of EVs into the energy market. As such, we do a comprehensive literature search to learn more about the exciting research gap that needs to be filled and the most recent possible answer that involves EVs. Additionally, we take a close look at the practicality of the sophisticated V2G technology. The smart grid is a developing concept for the world's energy infrastructure, and this study analyzes in depth how EVs interact with it.

The COVID-19 pandemic has put lifestyles in question, changed daily routines and limited citizen freedoms that seemed inalienable before. A human activity that was greatly affected since the beginning of the health crisis is mobility. Focusing on mobility, we aim to discuss the transformational impact that the pandemic brought on this specific urban domain, especially with regards to the promotion of the smart growth agenda and the acceleration towards the smart city paradigm. We collect 60 initial policy responses related to urban mobility from 86 cities around the world and analyse them based on the challenge they aim to address, the exact principles of smart growth and sustainable mobility that they encapsulate and the level of ICT penetration. Our findings suggest that emerging strategies, although mainly temporary, are transformational, in line with the principles of smart growth. As a result the pandemic becomes an opportunity for shifting towards more sustainable urban planning and mobility practices. However, most policy responses adopted during the first months of the pandemic fail to leverage advancements made in the field of smart cities, and to adopt off-the-shelf solutions such as in monitoring, alerting and operations management.

Information and communication technologies play an increasingly important role in the process of acquiring knowledge from a territory and managing it at different scales. ICTs allow a rapid diffusion of data not only through institutional channels but also through social networks where the smart community share experiences and perceptions. In this sense, ICTs become strategic to support the promotion of sustainable tourism development of territories. An important tool to implement it, can be a circular smart dashboard, a decision support system in which the digital data are organized and processed to produce an information output, to be used, after the evaluation by the decision makers, as a new input for the system. The present paper deals with a wider research the authors are involved in, related to the reconversion and valorization of a former mining area towards slow tourism, as the Santa Barbara Walk (SBW), an ancient mining route in the Sulcis Iglesiente area (Sardinia, Italy) . In particular, we here focus on the design proposal of a dashboard, capable of organizing information concerning the main features of the walk, in order to facilitate a shared governance for an effective tourism promotion. The paper is based on a thorough recognition of the main characteristics of the Walk, both the material ones and the digital, immaterial ones. The SBW represents in fact a network connecting the main points of interest along the Walk. On the contrary, its digital network – consisting of intangible infrastructure and flows - is however fragmented in terms of policies and contents Also a state of disorganization in slow tourism promotion activities can be observed. The goal of this paper is to present and analysis of the area, and to propose an evaluation and planning tool as the design of a circular dashboard of the SBW.

Internet of things security will be a big challenge for the enterprises working behind the build-up of the internet of things, and it’s application. With IoT, another buzzword is blockchain-based cryptocurrency bitcoin. Blockchain technology has proven itself as one of the most secured existing technology. In this paper, we have discussed the significant challenges that will come up in identity management due to the heterogeneity of devices. We have proposed a solution for privacy preservation using secure identity management and possible communication methodology by using public key-based cryptography used in the blockchain. We have taken the ecosystem of smart home management and smart health management. At last, we have concluded with the discussion of futuristic applications of blockchain in other applications of the internet of things.

The definition of a Smart-complex of educational discipline and its technology is given. Attention is focused on the form of the Smart-complex and constructive elements: the creative educational environment, the author's environment, the non-verbal environment, the encyclopedia, the creative / self-realization environment, the monitoring / self-evaluation environment.A feature of the Smart-complex of the academic discipline is the integration into it of the structural equalization block. Constructive alignment (alignment) is based on the design of the student's own learning participation in educational projects. Alignment refers to creating an appropriate learning environment and involves selecting the most appropriate training activities and evaluating each learning outcome. If the result of the training is to develop analytical skills, then for the assessment it is necessary to build questions and scenarios for their development.Two main components of the structural alignment are considered: 1) the first component is based on cognitive psychology and constructivist theory, recognizes the importance of linking new material with the concepts and experiences of students and extrapolation to possible future scenarios in the process of mission activity - they comprehend what they are doing during training; 2) the second component is based on the declaration by the teacher of the correspondence between the planned educational activities and the results of the training of students. This makes planning of educational activities and development of self-assessment criteria for organizing feedback. It is necessary to take into account the impact of Smart-complexes of educational disciplines on three think-tank training networks: 1) effective networks (why learn) - several ways of presenting to give students different ways of obtaining information and knowledge; 2) recognition networks (learning outcomes) - several ways of expressing them to provide them with an alternative for demonstrating what they know; 3) strategic exercises (how to learn) - several ways to increase the motivation for learning to attract their attention to both their own learning project and its technical and historical decision. Smart-complexes of educational disciplines should be: 1) scientific; 2) with user-friendly interface; 3) have a connection with LMS; 4) structured; 5) with visual material; 6) working in Off-line mode; 7) used on various devices in an educational institution, as well as at home.

A smart city is a futuristic approach that evolves with new technologies. It can start as a simple community and evolve into future cities. In most developing countries, where the population growth is faster, and where the organization and the urbanization are slower, there is a serious need for a framework for progressive change towards sustainable cities. This article discusses the level of intelligence of smart city components and proposes indicators as well as a methodology for measuring intelligence based on the objectives to be achieved as well as a certain number of technical requirements and some cognitive processes implemented.

The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of all users in system. In this paper, we employ the popular computer vision techniques of AI to design a non-intrusive load monitoring method for smart electric energy management. First of all, we utilize both signal transforms (including wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods to map one-dimensional current signals onto two-dimensional color feature images. Second, we propose to recognize all electric loads from color feature images using a deep neural network with multi-scale feature extraction and attention mechanism. Third, we design our method as a cloud-based, non-intrusive monitoring of all users, thereby saving energy cost during electric power system control. Experimental results on both public and our private datasets have demonstrated our method achieves superior performances than its peers, and thus supports efficient energy management over large-scale Internet of Things.

The smart grid system, an improvised form of the traditional grid, is considered a critical cyber-physical system. Due to its complex combination of smart and legacy technologies, this technology is characterized by a wide range of features such as full duplex communication, advanced metering infrastructure, remote monitoring, and control. However, despite this system’s substantial environmental and socio-economic impact on modern life, it poses severe security vulnerabilities due to its integration and interconnected and interdependent cyber-physical components. Based on the rapid development of cyber-physical systems (CPS), academics and industries have investigated relevant techniques to enhance and strengthen the security measures of the SG system. The intrusion detection system is a countermeasure, a powerful safeguard against various cyber-attacks and threats. The contribution of the IDSs to the SG paradigm is examined in this study. A comprehensive review has been presented in this study, highlighting different techniques, basic ideas, and analyses. Besides discussing the contemporary pertinent research studies, the shortcomings of the existing IDS for the SG system are also identified with a recommendation for future research direction.

This study identified, analysed, and prioritised significant factors for standardising the Korean smart city project evaluation system. We analysed the efficiency and productivity of companies currently providing smart city services to consider both policy and practical aspects. The prioritisation of smart city planning reflects the latest trends in South Korea, where urban planning is moving towards smart city planning. Furthermore, the capacity building of public-private/private-private partnerships indicate the importance of business scalability. This indicates that smart city services are only stabilised when the private sector is involved and leads the project, rather than focusing on public development. The feasibility of building intelligent facilities indicates that smart city projects should be implemented after securing cost-benefit feasibility. The results were used as the basis for building an evaluation system, showing that in smart city convergence alliances, small- and medium-sized enterprises achieved the highest efficiency by reducing inputs to 81% and 86%, under the assumptions of constant and variable returns to scales, respectively. As the operational aspect is more problematic than the technical aspect, policy alternatives are necessary for smooth business progress, including increased flexibility of laws and institutions and the activation of policies that temporarily relieve regulations to demonstrate smart city projects.

The paper is based on the scientific outcome of a PhD Thesis. It introduces the generic, model-based, reusable, and extensible conceptual framework to incorporate Facility Management data based within the three-dimensional model-based design and construction of an asset to enable smart applications, which are introduced. The conceptual framework is composed of empirical data from expert interviews, questionnaires, and factual analysis from 13 projects of varied sizes of public and private clients. It shows which phases need which data, who needs them, and which added value can be generated if intelligent data structuring is used at the beginning of the construction project and bridges the gap between requirement and practice. The term “smart application” is introduced.

We applied evolutionary game theory to extend a resource constrained security game model for confidentiality attacks in an Advanced Metering Infrastructure (AMI), which is a component of IoT-enabled Smart Grids. The AMI is modelled as a tree structure where each node aggregates the information of its children before encrypting it and passing it on to its parent. As a part of the model, we developed a discretization scheme for solving the replicator equations. The aim of this work is to explore the space of possible behaviours of attackers and to develop a framework where the AMI nodes adaptively select the most profitable strategies. Using this model, we simulated the evolution of a population of attackers and defenders on various cases resembling the real life implementation of AMI. We discuss in depth how to enhance security in AMI using evolutionary game theory either by a priori analysis or as a tool to run dynamic and adaptive infrastructure defence.

The performance of laser beams in tracking Lissajous scan trajectories is severely limited by beam jitter. To enhance the performance of fast steering mirror (FSM) control in tracking Lissajous scan trajectories, this paper proposed a fractional order active disturbance rejection controller (FOADRC) and verified its effectiveness in improving system scanning tracking accuracy. A dynamic mathematical model of fast steering mirror was studied and the design of parameters for the control mode of the closed-loop system was determined. A reduced order linear active disturbance rejection controller suitable for FSM systems was designed and the corresponding fractional order proportional differentiation (FOPD) controller were determined according to the mathematical model. The use of the designed controller enabled high-performance tracking of high-frequency Lissajous scanning curves (X-axis 500 Hz, Y-axis 350 Hz) and met the need for high-frequency repetitive scanning. The controller has the characteristics of simple implementation and low computational complexity and is suitable for closed-loop control applications in engineering.

The upgrade of energy infrastructure to those of smart grids, necessarily goes through the provision of integrated technological solutions that ensure the interoperability of their business capabilities and reduce the risk of devaluation of the systems used. The heterogeneity of the infrastructures and the dynamics of their operating environment, requires the continuous reduction of the complexity, the faster execution of the processes and the easy addition of innovative counterparts. Also, the integrated management of the overall ecosystem demands the provision of end-to-end interconnection, quality assurance, the definition of strict security policies, collaborative integration and correlation of events. In this respect, every design detail can be critical to the success or failure of a costly and ambitious project, such as that of smart energy networks. This work presents communication operating standards specific to the smart electricity networks applications, which should be taken into account in the process of planning and implementation of new infrastructures.

The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an on-going theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT), Cognitive Internet of Things, Internet of Medical Things, and Cloud Computing are expanding smart home concepts and solutions, and their applications. The primary objective of the present research work was to design and develop IoT and cloud computing based smart home solutions. In addition, we also propose a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision facility. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time.

Digital health has added numerous promising solutions to enhance the health and wellness of people with neurocognitive disorders (NCDs) and their informal caregivers: (1) Background: It is important to get a comprehensive view of currently available technologies, their outcomes, and conditions of success to inform recommendations regarding digital health solutions for people with NCDs and their caregivers. This environmental scan was performed to identify the features of existing digital health solutions relevant to the targeted population. This work reviews currently available digital health solutions and their related characteristics to develop a decision support tool for older adults living with mild or major neurocognitive disorders and their informal caregivers. This knowledge will inform the development of a decision support tool to assist older adults and their informal caregivers in their search for adequate digital health solutions according to their needs and preferences, based on trustable information.; (2) Methods: We conducted an environmental scan to identify digital health solutions from a systematic review and targeted searches in grey literature covering the regions of Canada and Europe. Technological tools were scanned based on a preformatted extraction grid. We assessed their relevance based on selected attributes and summarized findings; (3) Results: We identified 100 available digital health solutions. The majority (56%) were not specific to NCDs. Only 28% provided scientific evidence of their effectiveness. Remote patient care, movement tracking, and cognitive exercises were the most common purposes of digital health solutions. Most solutions were presented as decision aid tools, pill dispensers, apps, web, or a combination of these platforms. (4) Conclusions: This environmental scan allowed the identification of current digital health solutions for older adults with mild or major neurocognitive disorders and their informal caregivers. Findings from the environmental scan highlight the need for additional approaches to strengthen digital health interventions for the well-being of older adults with mild and major NCDs and their informal and formal healthcare providers.

Bone marrow mononuclear cells (BMMNCs) have emerged as a potential therapeutic strategy for various incurable conditions including autism spectrum disorder (ASD). To study its safety and efficacy, we analyzed 1011 patients who underwent intrathecal administration of autologous BMMNCs along with neurorehabilitation. At mean follow-up of 19.3 months after intervention, 90.6% patients showed overall improvement. Improvements were observed in attention concentration, command following, eye contact, sitting tolerance, social interaction, hyperactivity, communication, speech, aggressiveness, stereotypical and self-injurious behavior. Statistically significant changes were observed in scores of Indian Scale of Autism Assessment and Childhood Autism Rating Scale (p<0.05). On subgroup analysis, improvements were recorded irrespective of age and disease severity and better outcome was observed in patients who took multiple doses of cell therapy compared to single dose. Comparative Brain PET-CT scan of 401 patients showed improved brain metabolism after the intervention. No major adverse events were reported. Only 9 out of 1011 patients experienced seizures which were managed using medications. 4 out of 9 had history of seizures while 5 had normal EEG with no history. However, improvements were still observed in these 9 patients. Thus, intrathecal transplantation of autologous BMMNCs along with neurorehabilitation is safe and efficacious in patients with ASD.

The use of additive manufacturing in fabricating composite components has been gaining traction in the past decade. However, some issues with mechanical perfor-mance still need to be resolved. The issue of material porosity remains a pertinent one that needs more understanding to be able to determine viable solutions. Different re-searchers have examined the subject of porosity issues in AM-fabricated CFRP compo-sites. However, more research to quantitatively determine the effects of fabrication temperatures at the micro-scale is still needed. This study employed micro CT scan analysis to quantitatively compare the effects of fabrication temperatures at 230°C, 250°C, 270°C, and 290°C for CF-PA and CF-ABS composites. This followed an SEM evaluation which was used to determine the effects of the temperatures on interlayer and intralayer porosity generation. The porosity volume was related to the mechanical properties results in which it was determined how deposition temperatures affect the porosity volumes. It was also determined that semicrystalline composites are generally more affected by fabrication temperatures than amorphous composites, with the rela-tionship between porosity and mechanical properties also established. The overall po-rosity volume from the interlayer and intralayer voids was also determined, with the interlayer voids found to play a more determinant role in influencing the mechanical properties.

This article summarizes the results of a research conducted to create an intelligent platform supporting small farmers growing vegetables. In Bulgaria, many farmers use relatively small areas for their production. Considering this situation, the platform is designed for regional use, trying to make maximum use of the specific climatic features of the monitored microdistrict. Consequently, special attention is paid to the possibilities of adapting the platform to the specific environment in which the observed plants grow. Thus, the platform is implemented as an agent-oriented system providing needed flexibility. The core of the platform is a personal assistant, which is responsible for tracking the vegetation cycle of the monitored crop and, upon detection of anomalies, preparing warnings to the farmer. The general architecture of the platform is also presented. In a distributed knowledge base, agriculture-specific knowledge are stored. Furthermore, a standardized interface for receiving data from the sensor networks located in the production areas is briefly described. Currently, the platform is being tested for monitoring four varieties of tomatoes grown on the territory of the national research center for potato breeding. Special attention is paid to the measurements carried out. Conclusions have been drawn that will help improve the platform.

In the realm of smart manufacturing, equipment plays a pivotal role, impacting the efficiency and quality of production. Ensuring equipment availability and optimal performance is crucial, necessitating effective maintenance strategies. This paper addresses the complexities of smart device maintenance, which often involves collaboration between multiple entities like device owners, manufacturers, suppliers, and external maintenance services. To enhance this cooperative maintenance, we propose a novel maintenance system based on permissioned blockchain technology, offering superior security and reliability compared to conventional cloud-based platforms. Our contributions include developing an integrated maintenance system framework for smart manufacturing, which facilitates interactions among diverse stakeholders. Additionally, we have developed a prototype of this maintenance system, serving as a practical model for real-world application. Our research primarily demonstrates the practicality and advantages of using permissioned blockchain for maintenance systems in the manufacturing sector, paving the way for more secure, efficient, and collaborative maintenance practices.

This study aimed to assess the feasibility of employing audio and vision technologies as an integrated monitoring system to monitor the health and behavior of pigs under different environmental conditions and to associate these factors. A total of 81 growing pigs ((Landrace x Largewhite) x Duroc) with distinct weight categories (10.47 ± 2.57 kg and 28.81 ± 5.03 kg) were distributed into two distinct housing conditions: Control (characterized by low NH3 levels and low stocking density) and Treatment (marked by elevated NH3 levels and varying stocking densities). Each house was installed with a SoundTalks monitor for automated daily evaluation of respiratory health status (ReHS), and a camera with a microphone to facilitate manual tracking of respiratory symptoms and behavioral patterns. Results showed that the Treatment group encountered significantly high room temperature, NH3 concentration, and carbon dioxide levels, resulting in compromised growth performance—a phenomenon further exacerbated in high stocking density conditions. Pigs within the treatment exhibited increases (p &lt; 0.05) in lateral and total lying behaviors, and incidences of ear and tail injuries, increases screaming frequency, conjunctivitis incidences, coughing frequency, elevated respiration rates, and a decrease in ReHS. The environmental conditions affect the behavior and health of the pigs. Moreover, the behavioral patterns of the pigs are associated with their health conditions. The SoundTalks system did not trigger any warnings during the experimental period. Nevertheless, trend analysis indicated a significant reduction in respiratory health in the treatment. In conclusion, this study underscores the efficacy of merging audio and visual technologies to holistically monitor pig health and behavior, enabling enhanced management strategies. Findings emphasize that monitoring the ReHS trend serves as a pivotal marker for identifying respiratory health problems, complementing the system's innate alarm functions.

With the development of information technology and the wide application of building information modelling technology, the construction industry continues to make digital changes. Managers are trying to apply smart construction site management to promote the upgrading of production modes. We used CiteSpace software to analyze 1707 articles from China National Knowledge Infrastructure, Web of Science and Scopus to understand the current research hotspots of global scholars in this field. Results show that: (1) The number of studies on smart construction sites increases rapidly by year. Researchers from China, America and the UK have the most influential studies. (2) The cooperation between researchers and institutions is not close enough. In addition, the directions of research in the field are still scattered; (3) Chinese scholars are good at building intelligent platforms and evaluating intelligent systems from multiple perspectives. International scholars are willing to pursue technological innovation, allowing the continuous development of intelligent construction technology. (4) The applications in this field have not been popularized despite relatively perfect technology and typical cases. Future scholars should gradually improve the theoretical basis and industry standards of smart construction sites, promote the development of intelligent construction technology and establish the evaluation standards of qualified smart sites. This study will provide scholars in this field with the theoretical basis and research directions for further in-depth research, help construction companies to understand the development status and trend of smart construction sites and accelerate the intelligent transformation of construction companies.

The global agricultural sector is increasingly pressured to adopt sustainable practices and reduce its environmental impact. In this context, greenhouses play a crucial role in enabling year-round crop production, ensuring food security, and minimizing reliance on traditional open-field farming. However, the energy consumption associated with greenhouse operations poses a significant challenge to achieving sustainability goals. As a result, there is a growing emphasis on transitioning greenhouses towards near-zero energy consumption. Near-zero energy consumption in greenhouses refers to the ambitious objective of minimizing energy usage to the greatest extent possible while maintaining optimal growing conditions for crops. This goal encompasses reducing energy consumption for heating, cooling, lighting, and other operational needs, as well as exploring renewable energy sources to power greenhouse operations. This review article offers a comprehensive overview of greenhouse energy consumption, with the main goal of analyzing the present situation, identifying key challenges, exploring potential opportunities, and proposing future perspectives for decreasing energy usage in greenhouse environments. As the focus on sustainable agricultural practices grows, the need to reduce energy consumption in greenhouses becomes increasingly important. The review critically examines current technological models and strategies applied in smart greenhouse applications, as well as the monitoring of microclimatic conditions inside the greenhouse, encompassing factors such as temperature, humidity, CO2 levels, soil quality, and crop cultivation. Moreover, it aims to present existing literature that investigates the advancement of greenhouses toward achieving significant reductions in energy consumption.