Search

In the history of civil aircraft transportation, ice formation has been identified as a key factor in the safety of flight. Anti-icing and deicing systems have emerged through the years with the aim to prevent or to eliminate ice formation on wing airfoils, control surfaces and probes. Modern flying machines demand more efficiency in order to reduce the carbon footprint and increase the sustainability of flight transport. In order to achieve this goal, the need to have an efficient aircraft with an efficient and low power consuming system is fundamental. This paper proposes a new model for ice accretion using computational fluid dynamics (CFD). This model permits the simulation of the shape of the ice formed over a profile varying boundary condition (i.e., speed, liquid water content, and so on). The proposed model also takes into account the amount of heat transferred between the water and the surrounding environment and includes the effects of air turbulence on the ice formation process. The CFD simulations have been validated with NASA experimental outcome and show good agreement. The proposed model can be also used to investigate the effects of various parameters such as air speed, liquid water content, and air temperature on the ice formation process. The results evidence that the proposed model can accurately predict ice formation process and is suitable to optimize the design of anti-icing or deicing systems for aircraft and helicopters. This approach is not limited to aerospace but can also be exported to other applications such as transportation, wind turbine, energy management, and infrastructure.

Geological structures such as joints and faults in rock mass have significant influence on open pit mining. Hence, it is critical to develop a understanding of dynamic joint behaviour under blasting loading. This in turn can provide both theoretical and practical guidance to improve blasting rock fragmentation and associated bucket excavating efficiency. In this paper, delayed blasting on the highwall bench at Baiyunebo open-pit mine was used as an example, a nonlinear joint blasting model was also constructed. By simplifying the blasting wave propagation velocity, the P-wave normal incidence to the joint was obtained. The peak vibration velocity was 0.33m/s at 3.0s. The S-wave reflected by the joint interface was first reflected backward and then forward, which the peak vibration velocity was 0.027 m/s. By combining the relevant stress and displacement theories of type I and II cracks, the equipotential diagrams of the stress and displacement field with the vibration velocity of the particle were obtained. σx was positive in the direction of 0~330° and subjected to tensile stress, whereas σy was positive in the direction of 0~180° and under tensile stress. The longitudinal σy along joint was low in compressive stress distribution area and did not affect surrounding rock at the time. The stress concentration appeared in the lower right corner. Based on the continuous behavior of stress wave in joints, the asymmetry and continuous changes reflected in the whole process could not be analysed by the contour diagram. Hence, ANSYS used to analyse distribution of the stress field. The intensity of the shock wave after detonation was greater than that of the rock strength. Subsequently, the sub-layer shock wave supplemented the shock wave energy that was not enough to break the rock and induced further cracking. This was able to be visualized by the degree of color change post-processing. It was concluded that with the attenuation of the detonation wave energy, the stress exhibited a decreasing trend in this process. According to distribution of the peak effective stress, it was found that the peak value first increased to 10-12 MPa and then showed a downward trend. Overall, the results were validated against the finite element simulation and mathematical analysis.

This article presents six probability distributions from the gamma family with three parameters for the flood frequency analysis in hydrology. The choice of the gamma family of statistical distributions was driven by its frequent use in hydrology. In the Faculty of Hydrotechnics, the improvement of the estimation of maximum flows, including the methodological bases for the realization of a regionalization study with the linear moments method with the corrected parameters, was researched and is an element of novelty. The linear moments method performs better than the method of ordinary moments because it avoids the choice of skewness depending on the origin of the flows, and is the method practiced in Romania. The L-moments method conforms to the current trend for estimating the parameters of statistical distributions. Observed data from hydrometric stations are of relatively short length, so the statistical parameters that characterize them are of a sample that requires correction. The correction of the statistical parameters is proposed using the method of least squares based on the inverse functions of the statistical distributions expressed with the frequency factor for L-moments. All the necessary elements for their use are presented, such as quantile functions, the exact and approximate relations for estimating parameters, and frequency factors. A flood frequency analysis case study was carried out for the Ialomita river to verify the proposed methodology. The performance of this distributions is evaluated using Kling–Gupta and Nash–Sutcliff coefficients.

Intracortical microelectrodes that can be implanted have the ability to capture fast-changing neuron action potentials in the living brain. Despite this potential, many obstacles must still be overcome for reliable, long-term, high-quality recordings and accurate analysis of brain activity. These challenges include improving recording quality, enhancing recording stability, increasing recording capacity, and adding multi-functional features. One potential approach to enhance implantable microelectrodes involves the advancement of materials, refinement of implantation methodologies, and augmentation of the number of sites for recording. However, these challenges are still being actively researched, and advancements in microelectrode technology are underway. The difficulties with implantable microelectrodes are evaluated, and solutions are presented from different perspectives. The latest advancements in microelectrode technology are reviewed and condensed, and future possibilities are explored.

Groundwater plays a crucial role in triggering and reactivating deep-seated landslides. However, classical hydrogeological investigations have limitations in their applicability to deep-seated landslides due to anisotropic and heterogeneous media[16]. The Huangtupo landslide in the Three Gorges reservoir area has garnered significant attention in China due to its large volume, complex geological structure, and high hazard potential. Recent monitoring data has shown notable deformation in the NO.1 Riverside Sliding Mass (HTP-1), one of the four parts of the Huangtupo landslide, making it the primary focus of landslide research. This study aimed to investigate the water sources in the HTP-1 landslide using environmental stable isotopes as tracers. The isotopic analysis results indicated that the groundwater in the landslide area (LGW) is a mixture of remote karst groundwater (KGW) from the adjacent up-slope and local precipitation (LP). The karst groundwater is a major contributor to the recharge of the landslide groundwater system, causing a high slope groundwater level that can easily exceed the critical level during heavy rainfall events. Furthermore, based on the differential distribution of the LP fraction of LGW at different locations, the groundwater in the landslide can be classified into three groups. Groundwater at the sliding rupture surface has the highest fractions of local precipitation, followed by the groundwater at the rear of the landslide. Combined with the monitoring data of groundwater level in boreholes during the rainy season, this indicates that fissures and fractures in the sliding rupture zone and at the back of the landslide are well connected, which could be a critical factor in the mass movement of the creeping slope.

Owing to the advantages of cost-effectiveness, compactness, and the avoidance of complicated camera synchronization, single-camera three-dimensional (3D) digital image correlation (DIC) techniques have gained increasing attention for deformation measurement of materials and structures. In the traditional single-camera 3D-DIC system, the left and right view images can be recorded by a single camera using a diffraction grating, a bi-prism or a set of planar mirrors. To further improve the measurement accuracy of single-camera 3D-DIC, this paper introduces a single-camera four-view imaging technique by installing a pyramidal prism in front of the camera. The 3D reconstruction of the measured points before and after deformation is realized with eight governing equations induced by four views, and the strong geometric constraints of four views can help to improve the measurement accuracy. A static experiment, a rigid body translation experiment and a four-point bending experiment show that the proposed single-camera 3D-DIC method can achieve higher measurement accuracy than the dual-view single-camera 3D-DIC techniques and has advantages in reducing both random error and systematic error.

This paper is a review of the current state of IoT-based technologies in the healthcare field for the elderly population. The increasing population of elderly people has led to a greater demand for healthcare services to meet their unique needs. IoT-based technologies have emerged as a promising solution to address this demand. The paper analyses the current trends, challenges, and future opportunities for research in this area. It highlights how IoT-based technologies can be utilized to improve healthcare for older adults, from remote monitoring and telemedicine to fall detection and medication management. The paper also acknowledges that there are still challenges to be addressed, such as data privacy and security, but the potential benefits for older adults outweigh the challenges. The paper also examines the impact of the industrial revolution on the aging population and the corresponding increase in demand for geriatric healthcare. In summary, this paper delves into the current status of IoT-based technologies in geriatric healthcare and its future prospects, with a focus on addressing the needs of the aging population and improving their quality of life.

The globe and more particularly the economically developed regions of the world are currently in the era of the fourth Industrial revolution (4IR). Conversely; the economically developing regions in the world and more particularly the African continent have not yet even fully passed through the Third Industrial Revolution (3IR) wave and its economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth of the global human population which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet up with the steep food demand and stabilize the economy of the African continent and countries like India whose economy is mainly dependent on Agriculture. The tools we have at our disposal to utilize in the digitization of farming practices include space technology and Global Navigation and Satellite System (GNSS) in particular, Machine learning (ML), precision agriculture and communication systems such as the Internet of Things (IoT) and Information And Communication Technologies (ICT). The most pressing challenges in the farming field include the monitoring of diseases, pests, weeds and nutrient deficiencies in the crops as early detection translates to swift and timely correction actions and hence more yield at the end of a farming cycle. Vast opportunities in the field of precision agriculture still exist that can amount to further research studies such as the lack of real-time monitoring and real-time corrective action focus.

Definition and imagination of an ideal city can be traced back to the origin of garden city in UK about 100 years ago. Since then, many different names and topics have been proposed and smart city is the one most recently proposed. Starting from 2000, more and more countries have developed various demonstration projects for the promotion of smart city in order to provide total solution for the promotion of sustainable development and social welfare. In fact, some of them have been successfully carried out. Some researchers in Taiwan argue that the current mechanism by which government subsidies are allocated for smart city demonstration projects warrants improvement. A comprehensive literature review determined that the development potential of smart cities should be prioritized in site selection for such demonstration projects. This study developed an evaluation framework on the basis of multi-criteria evaluation methods to enable the identification of suitable smart city demonstration sites. Evaluation criteria were first identified through the Delphi method. Next, the weights of each criterion were derived through the analytic hierarchy process. Furthermore, the capability of the proposed evaluation model was determined through simulation testing. Four demonstration sites are simulated, they are: Taipower Smart Community, Yinlin Technology University campus, Taichung Creative Cultural Park, and Asian New Bay Area in Kaohsiung, It is expected that the research findings in this thesis can be helpful to the future decision for the demo site selection of smart city.

The cost to train a basic qualified U.S. Navy fighter aircraft pilot is nearly USD 10 M. The training includes primary, intermediate, and advanced stages, with the advanced stage involving extensive flight training, and, thus, is very expensive as a result. Despite the screening tests in place and early-stage attrition, 4.5% of aviators undergo attrition in this most expensive stage. Key reasons for aviator attrition include poor flight performance, voluntary withdrawals, and medical reasons. The reduction in late-stage attrition offers several financial and operational benefits to the U.S. Navy. To that end, this research leverages feature extraction and machine learning techniques on the very sparse flight test grades of student aviators to identify those with a high risk of attrition early in training. Using about 10 years of historical U.S. Navy pilot training data, trained models accurately predicted 50% of attrition with a 4% false positive rate. Such models could help the U.S. Navy save nearly USD 20 M a year in attrition costs. In addition, machine learning models were trained to recommend a suitable training aircraft type for each student aviator. These capabilities could help better answer the need for pilots and reduce the time and cost to train them.

Minimum miscibility pressure (MMP) and oil swelling are two important factors of carbondioxide (CO2) gas displacement mechanism occurred in the reservoir relate to application of CO2 injection in the field to enhance oil recovery. In this paper determination of minimum miscibility pressure (MMP) between crude oil samples with carbondioxide gas conducted using two methods, i.e. correlation methods and laboratory experiment using slimtube. While, determination of swelling factor was conducted using PVT cell, where recombined fluid injected and conditionally at the reservoir temperature. The results of MMP using empirical equation (2807 Psig) and Holm & Josendal correlation (2750 Psig) is more approximate to the result of laboratory analysis (2805 Psig). The result of swelling test during injection CO2 gas processes until 46.82% mole, shows that bubble point pressure is increasing gradually from 410 psig through 2200 psig, and swelling factor was also increase from 1.0 through 1.442. Based on the value of fracture pressure of Layer F in the KHL Field is 2200 Psig and MMP is 2805 Psig, hence the application of CO2 gas injection in the field only could be conducted as immiscible flooding.

In this study, the application of the main algorithms used for SDM, namely Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Multivariate Adaptive Regression Splines (MARS), Maximum Entropy (MAXENT) and Random Forest (RF), were considered in order to simulate the citrus probability of distribution in a Mediterranean area, based on presence data and a random background sample, and in relation to several predictors. The predictors were grouped under 19 bioclimatic variables, elevation described through a Digital terrain model, soil physical properties, and irrigation. Sensitivity analysis was carried out by: modifying the values of the main models’ parameters; and reducing the input presence points.

The concept of a metaverse, a shared virtual space that brings together the physical and digital worlds, has been a topic of interest for decades. With recent advancements in technology, particularly in virtual reality (VR), augmented reality (AR), and blockchain, the metaverse has become a rapidly evolving and promising ecosystem. This paper provides a review of the metaverse, exploring its history, evolution, and current state of development. The paper delves into the different types of the metaverse, including fully immersive VR metaverses, hybrid metaverses that blend the physical and virtual worlds, and decentralized metaverses that are powered by blockchain. Additionally, the paper examines the impact of web 3.0 on the metaverse, as well as the various technologies used to create and sustain the metaverse, including VR, AR, and blockchain. Furthermore, the paper explores the concept of digital ownership and the potential for marketing and commerce within the metaverse. This paper serves as a review of the metaverse and its various components, providing insights into its history, development, and prospects, and is intended to provide a foundation for further research into this exciting and rapidly evolving ecosystem.

The textile and clothing (T&C) value chain is one of the most polluting in the world and one that produces the most waste. It is, therefore, important to encourage the circular economy (CE) model in this sector to reduce pollution, mitigate the effects of waste production, and, consequently, increase environmental sustainability. Leveraging end-consumer engagement in a CE mindset in the T&C sector is crucial, as they are the last player in a typical linear value chain. Therefore, a platform that supports and promotes sustainable tasks to manage one’s fashion products, through the use of gamification techniques, can be of utmost importance. In this article, we identify impactful carbon footprint consumer actions and solutions for the T&C consumer phase. After that, we survey gamification frameworks for analyzing techniques, at the system design level, which enable the engagement of the final consumer in the CE process. Then, we select and use one of such frameworks, Gameful Design Heuristics (GDH), for defining the gamification structure needed to implement on a business-to-consumer-to-consumer (B2C2C) context of a circular economy process, linking it to the aforementioned actions and solutions. As result, we present a B2C2C circular business process model for the T&C value chain and propose the design model of a gamified platform for the final consumers, which allows them to register the consumer-to-business (C2B) and consumer-to-consumer (C2C) activities, from the circular value chain’s business process, and benefit from a game-like experience. All the model features have been mapped to the GDH framework heuristics, validating that it is possible to support a set of defined heuristics of applied gamification for promoting CE in the T&C value chain.

Over the years, researchers have been developing different ventilation strategies as a potential solution for controlling infectious airborne transmissions in healthcare facilities. The effects of ventilation systems on airflow characteristics have been extensively discussed among researchers since SARS-CoV-2 outbreak in 2019. Based on a bibliometric study of 154 publications from 1970 to 2021 retrieved from the Web of Science (WoS) database, this paper examines the research landscape on the effects of ventilation strategies on airflow analysis in healthcare facilities. Results show that China is the most productive country due to its significant contributions to the top prolific authors and funding agencies, which have promoted the research development of the topic. Several research hotspots were identified through keyword co-occurrence analysis. Cluster 1 highlights the ventilation studies involving ventilation layouts and ventilation rates in healthcare facilities. Cluster 2 underlines the computational analysis of ventilation performance for reducing the exposure risk of occupants, which promotes the development of infection control measures. Cluster 3 strengthens the knowledge of the emission and dispersion characteristics of respiratory droplets in healthcare facilities. Overall, this paper highlights the future direction of the current research field other than identifying strategic collaborators and suitable publication outlets for interested researchers.

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.

Soil erosion is one of the most important environmental problems which can have various negative consequences, such as land degradation affecting the sustainable development and the agricultural production, especially for developing countries like Tunisia. Moreover, soil erosion is a major problem around the world because of its effects on soil fertility by nutriment loss and siltation in water bodies. Apart from this, soil erosion by water is the most serious type of land loss in several regions both locally and globally. This study evaluated regional soil erosion risk through the derivation of appropriate factors, using the Revised Universal Soil Loss Equation (RUSLE), which was applied to establish a soil erosion risk map of the whole Tunisian territory and to identify the vulnerable areas of the country. RUSLE model take into account all the factors playing a major role in erosion processes, namely the erodibility of soils, topography, land use, rainfall erosivity and anti-erosion farming practices. The equation is thus implemented under Geographic Information System (GIS) “Arc GIS Desktop”. The results indicated that Tunisia has a serious risk of soil water erosion, showing that 6.43% of the total area of the country is affected by a very high soil loss rate estimated at more than 30 t/ha/year and 4.20% are affected by high mean annual soil loss ranging from 20 to 30 t/ha/year. The most eroded areas were identified in west southern, central and western parts of the country. The spatial erosion map can be used as a decision support document to guide decision-makers towards better land management and provide the opportunity to develop management strategies for soil erosion prevention and control in the global scale of Tunisia.

In this study, we demonstrate how the incorporation of appropriate feature engineering together with the selection of a Machine Learning (ML) algorithm that best suits the available dataset, leads to the development of a predictive model for transformation temperatures that can be applied to a wide range of shape memory alloys. We develop a gradient boosting ML surrogate model capable of predicting Martensite Start, Martensite Finish, Austenite Start, and Austenite Finish transformation temperatures with an average accuracy of more than 95% by explicitly taking care of potential distribution changes when modeling different alloy systems. We included heat treatment, rolling, extrusion processing parameters, and alloy system categorical features in the model input features to achieve more accurate and realistic results. In addition, using Shapley values, which are calculated based on the average marginal contribution of features to all possible coalitions, this study was able to gain insights into the governing features and their effect on predicted transformation temperatures, providing a unique opportunity to examine the critical parameters and features in martensite transformation temperatures.

The adoption of harmful postures due to a sedentary lifestyle, which involves long periods at work in offices, is a factor that exacerbates musculoskeletal risks. To examine these risks, there are numerous postures analysis methods, such as Rapid Upper Limb Assessment (RULA) and Rapid Office Strain Assessment (ROSA). RULA is a consolidated method to evaluate the exposure of individual workers to ergonomic risk factors associated with upper extremity musculoskeletal disorders. In turn, ROSA is a more recent method that quantifies exposure to risk factors in an office work environment. This research aims to identify whether there is harmony between these two methods, ROSA and RULA. Through applied research, from a qualitative perspective, the scientific procedures adopted were direct observation and ROSA and RULA postural assessment instruments. The study was conducted in the radiology service and focused on the outpatient reception workstation. Results indicated that further investigation must occur to make working conditions changes as soon as possible and that both methods are harmonic. As contributions of the analysis, we have discussed the working conditions and suggested improvements to reduce the risks of musculoskeletal morbidity symptoms. We recommend future studies aimed at measuring the levels of complementarity between these methods by quantitative approaches.

From the standpoint of Neoliberal Institutionalism, this study explores Malaysia's participation in activities that make energy the topic and/or object of foreign policies, whether through bilateral or multilateral engagements. This research, which aims to evaluate the relevance of energy in Malaysia's economy and diplomacy and to explain Malaysia's reason for its involvement in these cooperations, is necessary because of Malaysia's growing involvement in the global energy market. Information for this study was gathered from a variety of government sources, interviews, published statistical data, and previous studies. The study demonstrates how vital energy is to Malaysia's economy. The Lao PDR-Thailand-Malaysia-Singapore Power Integration Project (LTMS-PIP) initiative, in which Malaysia is participating, is evidence of the viability of multilateral power commerce in the area. The Peninsula Gas Utilisation (PGU) pipeline in Peninsular Malaysia and the Four-Fuel Diversification Policy 1981 (4FDP 1981), which increases natural gas utilisation, are related to Malaysia's participation in the Malaysia-Thailand Joint Development Area (MT-JDA). Malaysia would want to think about streamlining its energy administration.

So that the levels of water stress are not harmful to the development of the crop and affect its productivity, its detection and monitoring are necessary, and it can occur in different ways. One of them is through the Crop Water Stress Index (CWSI). This index quantifies water stress through the normalization of leaf temperature between the maximum and minimum plant temperatures as a function of evaporation conditions. The responses of a low-cost infrared (IR) sensor were crossed with image processing through segmentation by the Excess Green model to develop a water stress detection system using CWSI. A soil/plant temperature map was generated through a point-to-point scan of the IR sensor. And when it overlaid with a segmented image of the experimental area, only points identified as plants had their temperature values maintained. The Non-Water-Stressed Baseline (NWSB) equation was parameterized for the same conditions of the experiment and external environmental. The experimental area was divided into three different treatments, maintained under stable water conditions throughout the experiment and the system was able to identify stably different stress values between treatments. Although the relationship between crop and environment affected the results, this work showed that using an irrigation system based on CWSI is possible.

With recent developments in fusion engineering, interest has sparked in development of fusion devices for deterrent. Enormous amount of energy generated by combining two light nuclei could be contained and manipulated at will to trigger and accelerate micro explosions (from shock wave, x-rays or ion beam focusing) which finally result in full scale blast. Materials required to make such device are critical. They must possess high strength, high hardness, ductility, formability, drawability, and anisotropic properties. High entropy alloys (HEA) are new class of materials which nicely fulfils this requirement. Essentially, they are solid solutions of multi principal elements (usually > 5) eliminating the need of base metal as in conventional alloys. This gives them many unique properties which may be tailored at will (heat treatment, cold rolling, precipitation, irradiation). They also exhibit excellent directional properties with formation of distinct bands along certain preferred crystallographic planes even in hexagonal close packed structures. These anisotropic properties are strong function of rolling, working, or forging (swaging) direction and can be utilized to benefit. This study encompasses making outer shell of a typical fusion device selected on the basis of the weight, which is a function of area of pay load bay of carrier aircraft.

In this paper, we have investigated the impact of an early learning assessment on students' motivation for improving their performance throughout the semester. An observation analysis was conducted on an entry level mechanical engineering course in which students are enrolled in during their first semester of engineering work. This study analyzes the effect that a first exam, with an average below a passing grade, has on student's outcome in the course. It was hypothesized that students were motivated to achieve their desired grade outcomes following inadequate performance on the first exam. This was investigated by diving into the results of the course and referencing initial performance to the remaining exam and assessment outcomes. Students were placed into grade bands ranging from 0 to 100 in 20% increments. Their results were tracked and it was shown that for the second mechanics exam, averages jumped 43.333%, 35.35%, and 30.055% for grade bands of 0 to 20, 20 to 40, and 40 to 60 respectively. Assessment grades increased as well with the remaining assessments being averaged to a score of 91.095%. Variables contributing to student performance came from both with-in and outside the classroom. Learning communities, material differentiation, and student and professor adaptation all contributed to the rise in performance. It was concluded that the internal and external variables acted in combination with one another to increase student dedication to achieve success.

The assessment of an organization/product's environmental footprint is based on the protocols developed by the Joint Research Centre of the European Union, which take into account 16 impact categories. Among the categories covered are toxicity to freshwater ecosystems and to humans. Standard protocols use only chemical parameters as input data, preventing the true impact of entire complex mixtures, such as emissions discharged into the environment, from being determined. Biological assays allow us to bridge this gap: in the current study, assays were used to determine baseline toxicity towards aquatic organisms (green algae, luminescent bacteria, and crustaceans) as well as specific toxicity (mutagenicity and carcinogenicity). Expected impacts were compared using two approaches (standard and bioassay-centered results obtained are not always in line and, in general, the traditional method underestimates impacts). This demonstrates the importance of these assays and suggests that they be used in LCA (Life Cycle Assessment) protocols as well.

The emergence of unmanned aerial vehicles (UAVs) raised multiple concerns, given their potential for malicious misuse in unlawful acts Vision-based counter-UAV applications offer a reliable solution compared to acoustic and radio frequency-based solutions because of their high detection accuracy in diverse weather conditions. The existing solutions work well on trained datasets, but their accuracy is relatively low for real-time detection. In this paper, we model deep learning-empowered solutions to improve the multi-class UAV's classification performance using single-shot object detection algorithms YOLOv5 and YOLOv7. The transfer learning is employed for performance improvement and rapid training with improved results. We customized a multi-class dataset containing multi-rotor, fixed-wing, and single-rotor UAVs in challenging weather conditions. Experiments show that the integration of transfer learning has achieved good results, with an overall best average-classification precision of 94\%, an average recall of 93.1\%, a mAP$@$0.5 average of 95.3\%, and an average F1 score of 92.33\%. The dataset and code are available as an open source: https://github.com/ZeeshanKaleem/YOLOV5-Large-vs-YOLOV7.git

Bubble (point defect) – a precursor of fuzz or under dense nanostructure formation is crystal lattice defect. Suitable selection of crystal lattice which inhibit Frenkel pair generation and intrinsically promotes self- interstitial solid solution strengthening contributes effectively towards making plasma facing material. For this, interstitial sites, their size, amount / fraction, positions, tendency of occupation and diffusion parameters (e.g. activation energies (Q), activation volumes) are determined. Fcc iron carbon alloys (austenitic stainless steels AISI / SAE 321, fcc structure, Pearson code cF4, space group Fm3̅m) are proposed as suitable candidates. Along with their room temperature fcc structure having 12 interstitial positions (4 octahedral, 6 coordination sites and 8 tetrahedral, 4 coordination sites / unit cell) to allow insertion of self (iron) atoms, they have excellent corrosion resistance, thermal conductivity, and non- magnetic properties. After their melting, casting, and machining to required dimensions and geometry, stabilizing heat treatment is applied to precipitate all carbon as TiC and prevent formation of Cr23C6 (sensitization). This resist heat and surface degradation and yield excellent architecture which not onlyinhibit Frankel pair generation but will also allow bulk assimilation or surface annihilation (loop punching) of this lattice point defect. A superior thermal, fluid, and structural design augment above. A second choice is presented as Co base superalloys owing to same fcc crystal structure and excellent properties (such as strength, dimensional stability, oxidation resistance) at high temperature.

Despite being well established phenomena, solidification principles are experiencing tough challenges in their application to explain microscale transport phenomena in additive manufacturing melt pool. The problem becomes even more complicated when applied to multicomponent bulk metallic glass matrix composites (BMGMC) whose behavior is dubious and still not fully understood. The aim of the present study is to highlight pathways to overcome these challenges. A comprehensive nucleation and growth model based on original KGT theory and Rappaz modification is proposed encompassing actual transient thermophysical BMGMC data to predict evolving microstructure during additive manufacturing. The model is aimed at pictorial representation of in-situ ductile phase dendrite evolution from melt pool during solidification using two-dimensional cellular automaton (CA) methods. It is proposed to be coded in MATLAB® using commercial simulation code ABAQUS® at back end for macroscopic heat transfer model. The results will be compared with experimental values for validation.

Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues to enable and encourage practices with numerous unsustainable impacts affecting our environment, ingraining inequality, and degrading quality of life. There is an urgent need to identify such multifaceted impacts holistically. Impact assessment of digital interventions (DIs) leading to digitalization is essential specifically for Sustainable Development Goals (SDGs). Action is required to understand the pursuit of short-term gains toward achieving long-term value-driven sustainable development. We need to understand the impact of DIs on various actors and in diverse contexts. A holistic understanding of the impact will help us align the visions of sustainable development and identify potential measures to mitigate negative short and long-term impacts. The recently developed digitainability assessment framework (DAF) unveils the impact of DIs with an in-depth context-aware assessment and offers an evidence-based impact profile of SDGs at the indicator level. This paper demonstrates how DAF can be instrumental in guiding participatory action for the implementation of digitainability practices. This paper summarizes the insights developed during the Digitainable Spring School 2022 (DSS) on “Sustainability with Digitalization and Artificial Intelligence,” one of whose goals was to operationalize the DAF as a tool in the participatory action process with collaboration and active involvement of diverse professionals in the field of digitalization and sustainability. The DAF guides a holistic context-aware process formulation for a given DI. An evidence-based evaluation within the DAF protocol benchmarks a specific DI’s impact against the SDG indicators framework. The participating experts worked together to identify a DI and gather and analyze evidence by operationalizing the DAF. The four DIs identified in the process are as follows: smart home technology (SHT) for energy efficiency, the blockchain for food security, artificial intelligence (AI) for land use and cover change (LUCC), and Big Data for international law. Each of the four expert groups addresses different DIs for digitainability assessment using different techniques to gather and analyze data related to the criteria and indicators. The knowledge presented here could increase understanding of the challenges and opportunities related to digitainability and provide a structure for developing and implementing robust digitainability practices with data-driven insights.

Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of aerial remote sensing blurred images. Moreover, the average PSNR of this paper's algorithm tested on the publicly available dataset RealBlur-R reached 41.02dB, surpassing the latest SOTA algorithm.

The purpose of this paper is to develop an index system for measuring the compliance of high-rise residential buildings with fire requirements and also to develop an index system for measuring the fire response efficiency, which is linked to the index of compliance with fire requirements. The higher the compliance rate, the greater the chance of a successful response. The two systems de-pend on the automation of the firefighting system management processes using the techniques of the fourth industrial revolution and developed based on the consultation of subject matter experts in the field. The main elements of the indexes were identified, which were based mainly on several variables, including the fire administrative system, the firefighting system, the residents, the loca-tion of the high-rise residential buildings from the fire extinguishing station, and the height of the high-rise residential building. The two systems can contribute to improving emergency prepared-ness in high-rise residential buildings in the Emirate of Sharjah and are also considered as a measurement index for compliance with fire requirements in the Emirate of Sharjah.

Simulation-based education is a critical component of Intelligent Transportation Systems (ITS) education. This study discusses the experimental success of using Virtual Reality (VR) technology in simulation-based ITS education, which was found to improve the quality of education while increasing immersion and motivation. The study documents the application of VR technology to a microscopic simulation model within the ITS curriculum. Furthermore, the study also proposes using a standardized methodology to capture and evaluate students’ subjective experiences. The findings of the study are discussed, along with potential ways to improve the planned VR technology implementation in the ITS laboratory.

Vacuum pump wear is the most prevalent failure mode of the IBA Synthera® automated radiochemistry system. Rebuilding or replacing the pump causes equipment downtime and increases the radiation exposure of the service personnel. We built a dedicated test device to assess new or rebuilt pumps prior to installation, thus reducing downtime and radiation exposure during repairs. The Testbed incorporates a microprocessor that actuates the pump, valves, pressure sensor, and communicates with the user through lights, buttons, and an alphanumeric screen. The Testbed increases productivity and safety in the radiochemistry laboratory.

Valorization of excess sludge through the recovery of high-value products, such as biopolymers, could be a crucial step to implement circular economy principles in wastewater treatment plant (WWTP). In this frame, the present study was aimed at evaluating the simultaneous production of polyhydroxyalkanoates (PHA) and extracellular polymeric substances (EPS), obtainable from the treatment of an agro-industrial wastewater. Two biological systems, one implementing aerobic granular sludge (AGS) and the other a conventional activated sludge operating as a sequencing batch reactor (SBR) were operated for 204 and 186 days, respectively. Both the systems involved a three-stage process for mixed microbial culture enrichment and biopolymers accumulation. The maximum biopolymers accumulation capacity was close to 0.60 mg gVSS-1 in the AGS when the enrichment reactor was operated at 3 kgCODm-3d-1, whereas in the SBR it was slightly more than half (0.35 mg gVSS-1). Biopolymers extracted from the AGS were mainly constituted by EPS (>70%), which percentage increased with the organic loading rate applied in the enrichment reactor up to 95%. In contrast, SBR enabled to obtain a higher PHA production (50% of the biopolymers). Results suggested that organic carbon was mainly channeled toward metabolic pathways for extracellular storing in AGS, likely as a consequence of metabolic stressors (e.g., hydraulic selection pressure, shear forces) applied for promoting aerobic granulation.

The stick-free flight stability is an old-fashioned and non-progressive issue; nevertheless, it is still existent and of significant importance to the design of aircraft whose control system is reversible. The existence of the problem necessitates a deep assessment of stick-free flight stability throughout the aircraft design. Up to now, this problem has been addressed using either analytical approaches, which are only related to the static stability evaluation, or performing flight tests. In this study, the problem is handled in its entirety, from static and dynamic flight stability assessment to design criteria with a comprehensive perspective. Moreover, it is also exhibited that, contrary to what has been generally proposed in the literature, the limitation of the problem of stick-free flight stability through static stability assessment is far from being the main challenge. As a brief scope, the derivation of the control surface dynamics, a stick-free trim algorithm, and assessment rationale of the stick-free static and dynamic flight stability using a simulation approach are proposed. As a consequence, the aim is to set a broad understanding for designers related to this phenomenon and add adjunct design criteria in the design optimization process by approaching it from a modeling, simulation, and flight test perspective.

Several studies have highlighted the importance and evolution of process safety leadership and culture in various industries. However, none has focused on the palm oil milling industry yet. This paper critically reviews the latest developments in the palm oil milling process and unit operations leading to process safety concerns. It also discusses the Principle of 3C that is applied to explain repeat accidents and the four-level safety culture in the palm oil milling industry. For this purpose, the author presents case studies of two key palm oil companies in Malaysia. Overall, this paper offers guidelines to leaders in the palm oil milling industry about the required process safety leadership and culture to be understood in order to improve their safety outcomes.

In treatment wetlands (TWs), solids accumulation can result in hydraulic malfunction, reducing the operation life, and it could enhance biological activity by favoring biofilm development. It is still unknown whether the solids accumulation can affect greenhouse gas (GHG) emissions. This study aims to evaluate the solid concentration along a horizontal flow (HF) TW, and its role in GHG emissions, hydraulics, treatment performance, and vegetation development (Phragmites australis (Cav.) Trin. ex Steud.). The study was carried out in an eight-year-old full-scale HF-TW located in the Mediterranean region (Sicily, Italy). To collect data inside the HF unit, nine observation points (besides the inlet and the outlet) along three 8.5-m-long transects (T1, T2, and T3) were identified. The first transect (close to the inlet zone) showed a hydraulic conductivity (Ks) reduction approximately one order of magnitude higher than the other two. Results highlighted that GHG emissions increased during the summer, when the temperature and solar radiation were higher than in the rest of the year, matching the macrophyte growth rate. Theoretical methane (CH4) emissions followed the trend of volatile solids (VS), which was around 3.5 and 4 times in T1 to T2 and T3. Pore clogging affected carbon dioxide (CO2) emissions, which decreased from T1 to T3, with maximum monthly values in T1 (21.4 g CO2·m−2·d−1) being approximately double with respect to T2 (12.6 g CO2·m−2·d−1) and T3 (10.7 g CO2·m−2·d−1) observed in July. The same trend for chemical oxygen demand (COD) removal efficiency, decreasing from T1 to T3, was observed. Notwithstanding this behavior, the final effluent quality was very satisfactory, with an average value of COD removal efficiency above 90%.

A field experiment was conducted on the farm of Abi Jarash in Agricultural Collage, different rates of olive solid waste and cow manure were used, as follows: (control, fresh olive solid waste 100%, fresh olive solid waste 75%+ cow manure 25%, fresh olive solid waste 50%+ cow manure 50%, fresh olive solid waste 25+ cow manure 75%, Fermented Olive Solid Waste 100%, Fermented olive solid waste 75%+ cow manure 25%, Fermented olive solid waste 50%+ cow manure 50%, Fermented olive solid waste 25+ cow manure 75%), wheat was seeded. The impact of fresh and fermented olive solid waste and cow manure on some chemical and fertility soil properties was studied. the olive solid waste treatment 100% which added had more effect on the organic matter, exchange capacity, nitrogen, phosphorus and potassium in soil, plant and productivity, after that the fermented olive solid waste 75% + cow manure, fresh olive solid waste 100% comparing with the control, the values of organic matter were (25% 2.73, 2.39) and the cation exchange capacity (21.76, 20.44, 19.38) and for phosphorus (303.3, 277.8, 171.8) mg / kg, while the values of potassium ​​were (303.3, 277.8, 171.8) mg / kg. In terms of plant content, the plant content of nitrogen, phosphorus and potassium were the same as for grains (2.784, 2.606, 2.126), (0.4535, 0.4226, 0.2753), (1.876, 1.762, 1.234) respectively, (0.2875, 0.2016) respectively for the same previous treatments and elements. olive solid waste 100% was the highest productivity comparing with the other treatments, where the productivity of wheat was (9.513) ton. ha-1.

The emergence of Unmanned Aerial Vehicles (UAVs) raised multiple concerns, given their potentially malicious misuse in unlawful acts. Vision-based counter-UAV applications offer a reliable solution compared to acoustic and radio frequency-based solutions because of their high detection accuracy in diverse weather conditions. The existing solutions work well on trained datasets, but their accuracy is relatively low for real-time detection. In this paper, we model deep learning-empowered solutions to improve the multiclass UAV's classification performance using single-shot object detection algorithms (YOLOv5 and YOLOv7). They efficiently and correctly differentiate between multirotor, fixed-wing, and single-rotor UAVs in challenging weather conditions. Experiments show that the suggested technique is reliable with an overall best average-classification precision of 86.7\%, 88.5\% average recall, 91.8\% average mAP, and 58.4\% average-IoU.

The research aims to improve some of the physical and hydro-physical properties of some Vertisols in the southern region (part of the eastern Houran Plateau) in Sweda Governorate at the south of Syria. Using different quantities of volcanic ash, soil samples were collected from the Al-Thahallah village from a depth of (0-30) cm, The experiment was designed according to the complete random design with one factor that represents the ash quantity (1.25, 2.5, 5) %, with three replicates for each treatment in addition to the control treatment a0. The experiment was carried out within the plastic pots during agricultural season 2018/2019, in which the wheat of the Sham variety 3 were cultivated as a cover plant. The results showed that the addition of volcanic ash at the quantity of 5% led to a significant increase in the infiltration rate by (328.60) %,%, where the filtration rate increased from 0.42 cm/hr -1 to 1.80 cm.h-1, as well as for each of the air porosity by (89) % and the volume of infiltrate water by (40) %, compared with the control. The above-mentioned addition also resulted in a decrease in both dry bulk density, total soil porosity and volumetric swelling coefficient by (18.60, 5.80, 314) % Respectively, compared to the control. The addition also contributed to the reduce in the weighted moisture content when saturation and the field capacity, at the level of significance of 5%. The research recommends adding volcanic ash to the soil at a quantity of 5%, and adding enhancements with volcanic ash at various levels such as organic waste.

Coagulation is the most sensitive step in drinking water treatment. Underdosing may not yield the required water quality, whereas overdosing may result in higher costs and excess sludge. Traditionally, the coagulant dosage is set based on bath experiments performed manually. Therefore, this test does not allow real-time dosing control, and its accuracy is subject to operator experience. Alternatively, solutions based on machine-learning (ML) have been evaluated as a computer-aided alternative. Despite these advances, there is open debate on the most suitable ML method applied to the coagulation process, capable of the most highly accurate prediction. This study addresses this gap, where a comparative analysis between ML methods was performed. As a research hypothesis, a novel data-driven fuzzy inference system (FIS) should provide the best performance due to its ability to deal with uncertainties inherent to complex processes. Although ML methods have been widely investigated, only a few studies report hybrid neuro-fuzzy systems applied to coagulation. Thus, to the best of our knowledge, this is the first study thus far to address the accuracy of this novel data-driven FIS for such application. The novel FIS provided the smallest error (0.86), indicating a promising alternative tool for real-time and highly accurate coagulant dosing in drinking water treatment.

Globally, groundwater is the largest distributed storage of freshwater and plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often change as a result of land usage, abstraction, as well as variation in climate. To solve these challenges, many conventional solutions, such as certain numerical techniques, have been proffered for groundwater modelling. The global evolution of the Internet of Things (IoT) has enhanced the culture of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. At the moment, some studies in the literature reveal that groundwater managers lack an efficient and real-time groundwater management system that is needed to gather the required data. Additionally, the literature reveals that the existing methods of collecting data lack the required efficiency to meet computational model requirements and meet management objectives. Unlike previous surveys, which solely focussed on particular groundwater issues related to simulation and optimisation management methods, this paper seeks to highlight the current groundwater management models as well as the IoT contributions.

Motor imagery(MI)-based Brain-Computer Interfaces (BCI) have shown increased potential for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice has been restricted due to their low accuracy performance. To date, although a lot of research has been made in benchmarking and highlighting the most valuable classification algorithms in BCI configurations, most of them are using offline data and not from real BCI performance during the closed-loop (or online) sessions. Since rehabilitation training relies on the availability of an accurate feedback system, we surveyed articles of current and past EEG-based BCI frameworks who report the online classification of the movement of two upper limbs in both healthy volunteers and stroke patients. We found that the recently developed Deep Learning methods do not outperform the traditional machine learning algorithms. In addition, patients and healthy subjects exhibit similar classification accuracy in current BCI configurations. Lastly, in terms of neurofeedback modality, Functional Electrical Stimulation (FES) yielded the best performance compared to non-FES systems.

South Italy is characterised by a semi-arid climate with scarce rain and high evaporative demand. Since climate change could worsen this condition, the need to optimise water resources in this area is crucial. In citrus cultivation, which involves one of the most important crops bred in Southern Italy, and more generally in Mediterranean regions, deficit irrigation strategies are implemented in order to cope with limited resource availability. On this basis, knowledge on how the territorial distribution of citrus would change in relation to these strategies represents valuable information for stakeholders. Therefore, the objective of this study was to determine the probability of the presence of citrus in Sicily based on changes in the percentage of water deficit in order to identify and analyse change in the surface area as well as the location of the crop. The methodology was based on the application of species distribution models (SDM) and Geographic Information Systems (GIS) to the case study of the province of Syracuse in Sicily. Different geostatistical and machine learning models were applied based on bioclimatic variables measured over three decades, a Digital Terrain Model and irrigation. Assessment of the outcomes was carried out using classification evaluation metrics. The analysis of the outcomes showed that uncorrelated predictor layers mainly included water input that most affected the probability of the presence of citrus fruits. Moreover, GIS analyses showed that deficit irrigation strategies would generate an overall reduction of cultivation surfaces in the territory (e.g., for the Random Forest model the surface reduction was equal to 41.15%) and a decrease of citrus presence in southern areas of the considered territory. In this area, climate conditions are less favourable in terms of temperature and precipitation; thus, these analyses provide useful information for decision support tools in agriculture and land use policy.

The increasing regulatory pressure to monitor and reduce GHG emissions and air pollutants requires cost-effective methods for their surveillance. The most common technique used for scientific investigations on gas concentration monitoring in barns are accurate but expensive and with a complex maintenance. This research study analysed the potential use of a low-cost portable measurement devices for the measurement of ammonia (NH3) and carbon dioxide (CO2) concentrations in an open dairy barn. A comparison between gas concentrations acquired at different heights from the floor by using portable devices and those acquired by a photoacoustic infrared multigas spectroscope (i.e., reference measurement) in the same sampling locations was carried out to determine the precision of the low-cost portable devices. The performance of low-cost portable devices was statistically analysed by the application of the one-way analysis of variance, correlation analysis and regression analysis. The results showed a significant difference between gas concentration values at various heights from the floor for both NH3 and CO2. The correlations between concentrations acquired by the low-cost portable device and the INNOVA were statistically significant (r=0.83; P

The lattice reduction aided(LRA) algorithm has attracted widely attention of researchers in MIMO systems because of its low complexity to achieve full diversity. However, most lattice reduction auxiliary algorithms do not directly improve system performance. In this paper, a modified lattice reduction successive interference cancellation (SIC) algorithm is proposed for MIMO OFDM system mild changing the complexity of LRA algorithm. At present, the MMSE SQRD algorithm has the problem of regarding too much useful signals as the residual interference which cannot be cancelled by successive interference cancellation. Since the diagonal elements of useful signals are non-zero, the components of the symbols to be detected still present in the interference term, treating all the signals as interference will be resulted in performance loss. To solve this problem, the modified matrix is proposed.by moving this symbol component into the signal term, the proposed algorithm can provide the better performance than MMSE SQRD algorithm. The analysis shows that the LRA-MMSE-SIC algorithm of proposed in this paper significantly improves the system performance with mild increasing the complexity. The simulation results show that the performance of the proposed algorithm is improved by 1dB and 8dB compared with the comparison algorithm when the bit error rate is 10−3.

In the field of low power wireless networks, one of the techniques on which many researchers are putting their efforts is related to positioning methodologies such as fingerprinting in dense urban areas. This paper presents an experimental study aimed at quantifying the mean location estimation error in densely urbanized areas.Using a dataset made available by the University of Antwerp, a neural network was implemented with the aim of providing the position of the end-devices. In this way it was possible to measure the mean location estimation error in an area with high urban density. The results obtained show an accuracy in the localization of the end-device of less than 150 meters.This result would make it possible to use the fingerprint instead of alternative, energy consuming, methodologies such as GPS in IoT (Internet of Things) applications where battery life is the primary requirement to be met.

With the ongoing fifth-generation cellular network (5G) deployment, electromagnetic field exposure has become a critical concern. However, measurements are scarce, and accurate electromagnetic field reconstruction in a geographic region remains challenging. This work proposes a conditional Generative Adversarial Network to address this issue. The main objective is to reconstruct the electromagnetic field exposure map accurately according to the environment’s topology from a few sensors located in an outdoor urban environment. The model is trained to learn and estimate the propagation characteristics of the electromagnetic field according to the topology of a given environment. In addition, the conditional Generative Adversarial Network based electromagnetic field mapping is compared with simple kriging. Results show that the proposed method produces accurate estimates and is a promising solution for exposure map reconstruction.

Deep learning, specifically the supervised approach, has proved to be a breakthrough in depth prediction. However, the generalization ability of deep networks is still limited, and they cannot maintain a satisfactory performance on some inputs. Addressing a similar problem in the segmentation field, a scheme (f-BRS) has been proposed to refine predictions in the inference time. f-BRS adapts an intermediate activation function to each input by using user clicks as sparse labels. Given the similarity between user clicks and sparse depth maps, this paper aims at extending the application of f-BRS to depth prediction. Our experiments show that f-BRS, fused with a depth estimation baseline, is trapped in local optima, and fails to improve the network predictions. To resolve that, we propose a double-stage adaptive refinement scheme (DARS). In the first stage, a Delaunay-based correction module significantly improves the depth generated by a baseline network. In the second stage, a particle swarm optimizer (PSO) delineates the estimation through fine-tuning f-BRS parameters—that is, scales and biases. DARS is evaluated on an outdoor benchmark, KITTI, and an indoor benchmark, NYUv2 while for both the network is pre-trained on KITTI. The proposed scheme outperformed rival methods on both datasets.

Motor impairments are among the most relevant, evident, and disabling symptoms of Parkinson’s disease that adversely affect quality of life, resulting in limited autonomy, independence, and safety. Recent studies have demonstrated the benefits of physiotherapy and rehabilitation programs specifically targeted to the needs of Parkinsonian patients in supporting drug treatments and improving motor control and coordination. However, due to the expected increase in patients in the coming years, traditional rehabilitation pathways in healthcare facilities could become unsustainable. Consequently, new strategies are needed, in which technologies play a key role in enabling more frequent, comprehensive, and out-of-hospital follow-up. The paper proposes a vision-based solution using the new Azure Kinect DK sensor to implement an integrated approach for remote assessment, monitoring, and rehabilitation of Parkinsonian patients, exploiting non-invasive 3D tracking of body movements to objectively and automatically characterize both standard evaluative motor tasks and virtual exergames. An experimental test involving 20 parkinsonian subjects and 15 healthy controls was organized. Preliminary results show the system’s ability to quantify specific and statistically significant (p < 0.05) features of motor performance, easily monitor changes as the disease progresses over time, and at the same time permit the use of exergames in virtual reality both for training and as a support for motor condition assessment (for example, detecting an average reduction in arm swing asymmetry of about 14% after arm training). The main innovation relies precisely on the integration of evaluative and rehabilitative aspects, which could be used as a closed loop to design new protocols for remote management of patients tailored to their actual conditions.

This study looks at the application of delayed action Passive InfraRed (PIR) sensors in the control of water use for urinal flushing. In this we briefly review the literature on urinal controls before reviewing four different approaches to PIR urinal controls. Existing literature discusses some of the pros and cons of different types of urinal control. However, the literature doesn’t consider the marked differences that can occur within individual approaches, based on the way controls operate. This study was initiated at the University of Surrey during 2019 following a water saving audit, in an attempt to bring down what had been identified as one of the largest users of water. This paper therefore aims to identify the most effective way to reduce water consumption of urinal systems, through retro-fitting PIR control systems within the variety of settings found across university campuses. This paper also reviews the different reductions achieved over periods of different levels of use, linked to term time, holidays, Covid-19 related lockdowns, and the ‘new normal’. It found that grouped delayed action flushing was the most effective form of urinal control for reducing water use. The delayed action, grouped PIR sensors, achieved between, a 59-64% adjusted reduction against the control group during non-Covid19 periods, and a 35% reduction against the control group during lockdowns.