Taking into account the increase in the emission of greenhouse gases produced by ships, during navigation and maneuvering in the port, a direct consequence of the increase in maritime traffic, the international community has developed a broad set of regulations to limit such emissions. The installation in commercial ports of automatic mooring systems by means of vacuum suction cups (AMS), thus reducing considerably the time required to carry out the mooring and unmooring maneuvers of ships, is a factor that is considerably influencing the decrease in Emissions of polluting gases in commercial ports with high traffic. The objective of the present work is to verify the influence of the use of the AMS on the emissions of polluting gases produced in the facilities destined to the traffic of container ships. It examines the CO2 emissions of container ships that call in the only three container ports equipped with AMS: Salalah (Oman), Beirut (Lebanon) and Ngqura (South Africa). Between them, these three ports supported the transit of 6 million TEUS in 2017. The calculation of emissions is made taking into account the time saved when performing the mooring maneuvers using the new AMS system compared with when it is not used. To do this, two different calculation methods are used: EPA and ENTEC to then compare the results of the two and thus obtain the reduction in emissions per TEU in these terminals during the mooring maneuvers. The paper concludes with a discussion on the values of the reductions in emissions obtained and the advantages of the installation of AMS in commercial ports located near population centres.

The aim of the instant study was to evaluate relative inline progesterone dynamic changes according to parity and status of reproduction and to estimate the relationship with productivity in dairy cows by inline milk analysis system (IMAS) Herd Navigator. According to a progesterone assay, cows were divided into three periods: postpartum, after insemination, and pregnancy. In the first stage of the postpartum period (0-29 days), the progesterone level in milk was monitored every 6 days. The second stage of the postpartum period (30-65 days) lasted until cows were inseminated. In the third period (0-45 days) after cows were inseminated, progesterone scores were distributed according to whether or not cows became pregnant. The stability of progesterone dynamics was monitored in the last study period (45-90 days). For milk progesterone detection, the fully automated real-time progesterone analyzer Herd Navigator (Lattec I/S. Hillerød. Denmark) was used in combination with a DeLaval milking robot (DeLaval Inc., Tumba, Sweden). The highest progesterone concentration in multiparous cows ranged from 1.08% (11-17 days postpartum) to 34.89% higher than that in cows of the first parity. The lowest progesterone concentrations in the milk of all cows were estimated during the first 5 postpartum days and between 18 and 23 days after calving. Peak milk progesterone concentrations were evaluated in the first stage of the experiment on days 24-29 after calving. In the 30-65-day period after calving, the level of milk progesterone was 2.02-2.08 times higher than that in the 24-29-day postpartum period. After insemination, the level of progesterone in milk increased by 10.77-22.54% compared with the level from cows on days 30-65 after calving. A higher (12.88%) concentration of progesterone in milk was evaluated in multiparous cows compared with that from cows of the first parity. In pregnant cows, milk progesterone within 0-45 days after insemination was 23.88% (in multiparous cows) and 32.54% (in primiparous cows) higher than that in non-pregnant cows. On days 31–35 after insemination, pregnant cows had higher milk progesterone levels, which can predict pregnancy success. According to our study results, we can suggest that an inline progesterone concentration determined by inline milk analysis system Herd Navigator and changes in its dynamics correlate with different reproductive statuses and milk yield of cows. Pregnant cows 11–15 days after insemination have higher milk progesterone levels, what positively, associated with a successful pregnancy.

This case study investigates the heart rate (HR) and heart rate variability (HRV) in a patient with coronavirus disease 2019 (COVID-19). We report the case of a 58-year old male who contracted COVID-19. During his disease, 24-hour Holter electrocardiography (ECG) was performed continuously. For comparison, his 24-hour Holter ECGs from the previous 10 years were available. In this patient, COVID-19 was associated with a decrease in HR and a paradoxical decline in HRV. An abrupt decline in HRV and a decrease in HR may signal the onset of COVID-19 before common symptoms such as dry cough or fever appear. In addition, HRV and HR measurements may help to evaluate the course of the disease.

An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurred in China towards the end of 2019, and has spread rapidly ever since. Previous studies showed that some virus could affect the reproductive system and cause long-term complications. Recent studies exploring the source of SARS-CoV-2 using genomic sequencing have revealed that SARS-CoV-2 enters the host cells via the angiotensin-converting enzyme II (ACE2), the receptor that recognizes SARS-CoV. To investigate the expression of ACE2 and to explore the potential risk of infection in the reproductive system, we performed a thorough bioinformatic analysis on data from public databases involving RNA expression, protein expression, and single-cell RNA expression studies. The analyzed data showed high levels of ACE2 mRNA and protein expression in the testis and spermatids and equal levels of ACE2 expression in the uterus and lung. Comprehensive single-cell analysis identified ACE2 expression in the lung, testis, spermatids, and uterus. In conclusion, this study revealed the potential risk associated with the SARS-CoV-2 infection in the reproductive system and predicted that long-term complications might have a significant impact on the prevention and management of COVID-19, the disease caused upon infection with SARS-CoV-2.

Osteoimmunology was coined about twenty years ago to identify a strict cross talk between bone niche and immune system both in physiological and pathological activities, including cancer. Several molecules are involved in the complex interaction between bone niche, immune and cancer cells. The Receptor Activator ok NF-kB (RANK)/RANK Ligand (RANKL/Osteoprotegerin (OPG) pathway plays a crucial role in bone cells/cancer interactions with subsequently immune system control failure, bone destruction, inhibition of effect and metastasis outcome. The bidirectional cross talk between bone and immune system could became a potential target for anticancer drugs. Several studies evidenced a direct anticancer role with improved survival of bone-targeted therapies such as bisphosphonates and RANKL antagonist Denosumab. Conversely, initial data evidenced a possible anti-bone resorption effect of systemic anticancer drugs through and immunomodulation activity, i.e. new generation antiandrogens (Abiraterone) in prostate cancer. All data could open a future rationale of combined bone, immunologic and targeted therapies in cancer treatment.

In this study, the decision-making process management of forest tending in the forestry business is decentralized, and forest tending decision-making activities at different points in time are integrated by decision makers at different geographical locations. The decision-making process was analyzed and optimized from a system perspective. Based on the optimized decision-making process, a forest tending business group decision support system (FTGDSS) was established. We first reviewed and discussed the characteristics and development of the forest tending business and forestry decision support system. Business Process Modeling Notation was used to draw a current state flow chart of the forest tending business, to identify and discover important decision points in the process of tending decision-making. We also analyzed the content and attributes of each decision point, and described the system structure, functional framework, knowledge base structure, and reasoning algorithm of FTGDSS in detail. Finally, FTGDSS was evaluated from the two dimensions of the technology adoption model. FTGDSS integrates different levels of time-space decision-making activities, historical tending data, business plans, decision-makers' management tendencies into the decision-making process and automatically extracts decision-making data from the forest business process management enterprise resource planning system (Smartforest) that improves the ease of use of the decision support system (DSS). It also improves the quality of forest tending decisions, and enables the DSS to better support multi-target management strategies.

Requirements for cloud services include security and privacy. Although many security patterns, privacy patterns, and non-pattern-based knowledge have been reported, knowing which pattern or combination of patterns to use in a specific scenario is challenging due to the sheer volume of options and the layered cloud stack. To deal with security and privacy in cloud services, this study proposes the Cloud Security and Privacy Metamodel (CSPM). CSPM uses a consistent approach to classify and support existing security and privacy patterns. In addition, CSPM is used to develop a security and privacy awareness process to develop cloud systems. The effectiveness and practicality of CSPM is demonstrated via several case studies.

The paper is devoted to fundamental issues concerning to the ideology of the future sustainable development. Its implementation has available in the first place basing on the recognized framework concept of sustainable development (SD). At the same time, this concept requires serious improvement. As follows from the argumentation, a realistic design of institutional changes in connection with resource and organizational/behavioral changes needed for SD is called for to realize in the line of overall social system transformation. Its adequate understanding presupposes an exhaustive study of interconnected transformations in the main social fields as system processes taking into account the influence of relatively exogenous factors (technological, demographic and climate changes).According to the author, the transition to global sustainable social transformation is achievable. Main attention focuses on a number of possible fundamental shifts, the synergetic implementation of which will make such a transition possible.Proceeding from the arguments, the sustainable reproduction of the economy of a mature post-industrial type, including the green sectors, implies its stable and quiet growth, although the rapid growth of individual markets, especially innovative ones, may take place. The main findings also include the substantiation in favor of the choice of the national middle way model as the most appropriate for the corresponding sustainable system transformation of society.

The link between language processing and motor systems has been the focus of increasing interest to Cognitive Neuroscience. Some classical papers studying Event Related Potentials (ERPs) induced by linguistic stimuli have found differences in electrophysiological activity when comparing action and non-action words; more specifically, a bigger p200 for action words. On the other hand, a series of studies have validated the use of a grip force sensor (GFS) to measure language-induced motor activity during both isolated words and sentence listening, finding that action words induce an augmentation in the grip force around 250-300 ms after the onset of the stimulus. The purpose of the present study is to combine both techniques to assess if the p200 is related to the augmentation of the grip force measured by a GFS. We measured ERP and GFS changes elicited by listening to action and non-action words while maintaining an active grasping task in 10 healthy subjects. Our results show that the amplitude of the p200 in central electrodes is correlated to the augmentation in the GFS around 300 ms induced by linguistic stimuli. To our knowledge, this is the first study where the electrophysiological activity and the changes in the grip force induced by auditory language processing are put together, opening new venues of interpretation for the sensorimotor interaction in language processing.

This study describes the design and implementation of an electronic nose, which was applied to classify and identify hazardous gases generated in underground coal mines. For this purpose, an electrochemical sensor array was used to detect a set of toxic gases. This work illustrates the electronic components of a wireless multisensory system for the toxic gases detection on indoor environments (i.e., underground mines), which was performed with reduced size, low cost and low electrical consumption, in order to detect different compounds using the basic principle of operation of each component to be applied to the target gas. Furthermore, the sample collection, data communication and data processing in real time obtained an excellent performance for gas sensing and even to measure the concentration level of the chemical volatile compounds transmitted from different points of the detection zone. The results demonstrated that using a wireless electronic nose for toxic gases detection was possible to reach a success rate of discrimination of 97%, using principal components analysis (PCA) and Linear Discriminant Analysis (LDA).

In this study, we examine the relationship of bank level lending and borrowing decisions and the risk preferences on the dynamics of the interbank lending We develop an agent-based model that incorporates individual bank decisions using the temporal difference reinforcement learning algorithm with empirical data of 6600 S. banks. The model can successfully replicate the key characteristics of interbank lending and borrowing relationships documented in the recent literatur A key finding of this study is that risk preferences at individual bank level can lead to unique interbank market structures which are suggestive of the capacity that the market responds to surprising

The anti-money laundering (AML) process has failed both in identifying suspicious cases in due time as in assisting the AML analysts in decision making. Starting from a new generic anti-fraud approach, this article presents the main aspects related to the development of a multi-agent system that goes beyond the capture of suspicious transactions, seeking to assist the human expert in the analysis of suspicious behaviour. First, a transactional behavioural profile of clients is obtained in a data mining process. A set of rules, obtained through data mining over a real database, in conjunction with specific rules based on legal aspects and in the expertise of the AML analysts make up the agents' knowledge base. The cases for which the system was unable to suggest a decision are flagged as requiring more detailed analysis. The system analysed 6 months of real transactions and indicated several suspicious profiles, a set of these suspects was investigated by the AML analysts who proved the suspicion of several cases, including some that had not been identified by the systems in execution.

After decades of research, oncolytic virotherapy has recently advanced to clinical application, and currently a multitude of novel agents and combination treatments are being evaluated for cancer therapy. Oncolytic agents preferentially replicate in tumor cells, inducing tumor cell lysis and complex anti-tumor effects, such as innate and adaptive immune responses and the destruction of tumor vasculature. With the availability of different vector platforms and the potential of both genetic engineering and combination regimens to enhance particular aspects of safety and efficacy, the identification of optimal treatments for patient subpopulations or even individual patients becomes a top priority. Mathematical modeling can provide support in this arena by making use of experimental and clinical data to generate hypotheses about the mechanisms underlying complex biology and, ultimately, predict optimal treatment protocols. Increasingly complex models can be applied to account for therapeutically relevant parameters such as components of the immune system. In this review, we describe current developments in oncolytic virotherapy and mathematical modeling to discuss the benefit of integrating different modeling approaches into biological and clinical experimentation. Conclusively, we propose a mutual combination of these fields of research for more efficient development and effective treatments.

Introduction: This empirical study investigates the impact of two distinct mathematical problem-solving methods – the Algebraic Formula Method and the Newton Sum Method – on enhancing ChatGPT's competence in effectively solving quadratic root functions. The integration of Artificial Intelligence (AI) into mathematical problem-solving has paved the way for innovative approaches. In this study, we delve into the Algebraic Formula Method and the Newton Sum Method, essential techniques for solving quadratic root functions. We aim to showcase the profound influence of these methods on ChatGPT's capacity to excel in solving quadratic equations. Evidence Through concrete evidence, we demonstrate ChatGPT's adept utilization of the Newton Sum Method for quadratic root function calculations. While ChatGPT can compute quadratic root functions of the form α^15 + β^15 using this method, its proficiency in using algebraic formula methods typically extends only up to α^4 + β^4. This marked discrepancy underscores the pivotal role that different methods play in amplifying the AI system's mathematical capabilities Result The results of this study provide concrete evidence of ChatGPT's superior utilization of the Newton Sum Method for calculating quadratic root functions. The model adeptly computes expressions of the form α^15 + β^15 using this method, while its proficiency using algebraic formula methods is generally limited to α^4 + β^4. This striking discrepancy underscores the transformative impact that different methods can have on elevating the AI system's mathematical prowess. Conclusion :Pushing Boundaries: Pioneering Novel Maths Approaches for Overcoming Limitations in AIThis study serves as an illuminating testament to the significance of pioneering innovative methodologies, rules, theorems, or formulas to surmount the current limitations in AI systems like ChatGPT. These innovative pursuits hold the key to unlocking the untapped potential that lies within, propelling AI systems to greater heights of proficiency. In essence, they offer a strategic pathway towards expanding the capabilities of AI and pushing the boundaries of what can be achieved. Discussion The outcomes derived from this study underscore the significant influence wielded by the method selection in augmenting the mathematical competencies of ChatGPT. Particularly noteworthy is the application of the Newton Sum Method, which surfaces as a compelling exemplar. This method serves as a pivotal conduit through which the model surpasses its prior constraints, allowing it to venture into the realm of calculations entailing higher exponents. Implications and Future Research: These findings not only contribute to AI's mathematical competencies but also emphasize the need for pioneering new methods, rules, theorems, or formulas to further enhance AI systems like ChatGPT. Future research could explore the development of novel mathematical techniques tailored to AI systems, thus expanding their capabilities across diverse problem-solving domains.

Pre-Western contact Hawaiʻi stands as a quintessential sustainability example of a large human population that practiced intensive agriculture, yet minimally displaced native habitats that comprised the foundation of its vitality. An explicit geospatial footprint of human-transformed areas across the pre-contact Hawaiian archipelago comprised less than 15% of total land area, yet provided 100% of human needs, supporting a thriving Polynesian society. A post-contact history of disruption of traditional Hawaiian land-use and its supplanting by Western land tenure and agriculture based on ranching, sugarcane, and pineapple, culminated in a landscape, in which over 50% of native habitats have been lost, while self-sufficiency has plummeted to 15% or less. Recapturing the ʻāina momona (productive lands) of ancient times can be accomplished through study of pre-contact agriculture, assessment of biological and ecological changes imposed on Hawaiian social-ecological systems, and conscious planned efforts to increase self-sufficiency and reduce importation. Impediments include the current tourism-based economy, competition from habitat-modifying introduced species, a suite of agricultural pests severely limiting traditional agriculture, and changes in climate rendering some pre-contact agricultural centers suboptimal. Modified agricultural methods will be required to counteract these limitations, and diversified agriculture to broaden the production base, without contributing to further degradation of native habitats.

Background: Multiple system atrophy (MSA) is a rapidly progressive neurodegenerative disorder that has no curative treatment. Diagnosis is based on a set of criteria established by Gilman (1998 and 2008) and recently updated by Wenning (2022). We aim to determine the effectiveness of [123I]Ioflupane SPECT in MSA, especially at the initial clinical suspicion. Methods: Cross-sectional study of patients at initial clinical suspicion of MSA, referred for [123I]Ioflupane SPECT. Results: 139 patients (68 men, 71 women) were included, 104 being MSA-probable and 35 MSA-possible. MRI was normal in 89.2% while SPECT was positive in 78.45%. SPECT showed high sensitivity (82.46%) and positive predictive value (86.24), reaching maximum sensitivity in MSA-P (97.26%). Significant differences were found when relating both SPECT assessments in the healthy-sick and inconclusive-sick groups. We also found an association when relating SPECT to the subtype (MSA-C or MSA-P), as well as to the presence of parkinsonian symptoms. Lateralization of striatal involvement was detected (left side). Conclusions: [123I]Ioflupane SPECT is a useful and reliable tool for diagnosing MSA, with good effectiveness and accuracy. Qualitative assessment shows a clear superiority when distinguishing between the healthy-sick categories, as well as between the parkinsonian (MSA-P) and cerebellar (MSA-C) subtypes at initial clinical suspicion.

Mutual survival among different species of living organisms is quite common in our living world. That mutual survival can produce symbiotic or parasitic relationship among different living organisms. But at the same time, some relationships are harmful to the living organisms creating pathogenic relationships. Why some mutual survivals are beneficial, whereas some relationships are harmful creating different diseases in the living world? That harmful or pathological relationship producing different diseases in both the animal and plant kingdom has been extensively studied by the scientific community several times under the heading of ‘Host-pathogen interaction’ and ‘Disease pathogenesis’. But it is still not clear why some mutual survivals are beneficial or non-harmful, whereas some co-survivals are harmful producing different disease conditions in the living world mainly due to different immune mediated reactions or direct toxic effect of substances produced by an organism. To find the answer to this question, we have to search retrospectively to the evolutionary pattern of our diverse living world. If it is assumed that we have originated from Last Universal Common Ancestor (LUCA) by different cumulative mutations, horizontal gene transfer, mobile genetic elements (MGE), transposition and natural selection, then it would be quite pragmatic to consider that two things were moving side by side in our ancient living world. On one hand it’s purpose was to create the diversification of both unicellular and multi-cellular living world and on the other hand it’s another purpose was to maintain the specific identity of the living organisms. It is the second purpose or the maintenance of specific identity that ultimately led to the development of Immune system.

As people grow a custom to effortless outdoor navigation there is a rising demand for similar possibility indoors as well. Unfortunately, indoor localization, being one of the necessary requirements for navigation, continues to be problem without a clear solution. In this article we are proposing a method for an indoor positioning system using a single image. This is made possible using small preprocessed database of images with known control points as the only preprocessing needed. Using feature detection with SIFT algorithm we can look through the database and find image which is the most similar to the image taken by user. Pair of images is then used to find coordinates of database image using PnP problem. Furthermore, projection and essential matrices are determined allowing for the user image localization ~ determining the position of the user in indoor environment. Benefits of this approach lies in the single image being the only input from user and no requirements for new onsite infrastructure and thus enables a simpler realization for the building management.

From June 18 to 19, 2013, the Ésera river in the Pyrenees, Northern Spain, caused widespread damage due to flooding as a result of torrential rains and sustained snowmelt. We estimate the contribution of snow melt to total discharge applying a snow energy balance to the catchment. Precipitation is derived from sparse local measurements and the WRF-ARW model over three nested domains, down to a grid cell size of 2 km. Temperature profiles, precipitation and precipitation gradient are well simulated, although with a possible displacement regarding the observations. Snowpack melting was correctly reproduced and verified in three instrumented sites, and according to satellite images. We found that the hydrological simulations agree well with measured discharge. Snowmelt represented 33% of total runoff during the main flood event and 23% at peak flow. The snow energy balance model indicates that most of the energy for snow melt during the day of maximum precipitation came from turbulent fluxes. This approach forecast correctly peak flow and discharge during normal conditions at least 24h in advance and could give an early warning of the extreme event 2.5 days before.

This work aims to produce a cartographic model that identifies areas with a greater probability of finding karstic caves - the endokarst potential - in the northern sector of Santo António Plateau (Estremadura Limestone Massif, Central Portugal). Geological, topographic, hydrogeological, and vegetation cover data were collected, processed, and integrated into a spatial database using a geographic information system (GIS). The location of known cave entrances in the study area was also identified from the records of local public institutions and speleological teams. Four conditioning factors were extracted from the collected data, including lithostratigraphic units, fracture density, relief energy, and land cover. In a multi-criteria decision-making analysis framework, each previously chosen conditioning factor and respective classes/categories were weighted using the analytic hierarchy process (AHP). With an agreement of 81.9%, the results of the cartographic model constructed in GIS seem to be promising, considering that the entrances of the known caves are mainly located in areas classified as having high to very high endokarst potential. This prototype of the endokarst potential map for the study area can be used in strategic and operational environmental planning (at least on a local scale), as it can assist decision-makers, competent authorities, and local speleological teams in a more accurate and thoughtful definition of the areas that should be investigated, providing a substantial reduction in times and costs field prospecting.

The ejido system in Mexico based on communal land was transformed for private ownership due to neoliberal trends during 1990. This research describes the evolution of Mexican land policies that changed the ejido system into private development to answer why land tenure change is shaping urban growth. To demonstrate this, municipalities of San Andrés Cholula and Ocoyucan were selected as a case study. Within this context, we evaluated how much ejido land is being urbanized due to real estate market forces and what type of urbanization model is created. These two areas represent different development scales: S.A. Cholula where its ejidos were expropriated as part of a regional urban development plan; and Ocoyucan where its ejidos and rural land were reached by private developers without local planning. To analyze both municipalities, historical satellite images from Google Earth were used with GRASS GIS 7.4 and corrected with QGIS 2.18. We found that privatization of ejidos fragmented and segregated the rural world for the construction of massive gated-communities. Therefore, a disturbing land tenure change occurred during the last 30 years, hence this research questions the role of local authorities in permitting land use change without regulations or local planning. The resulting urbanization model is a private sector development that isolates rural communities in their own territories, for which we provide recommendations.

The Wind Energy Conversion System (WECS) plays an inevitable role across the world. In particular, the attention for Permanent Magnet Synchronous Generators (PMSGs) connected with wind farm is popular. This paper deals with the literature review that describes the recent advances, progresses and innovatory trends on PMSGs for WECS. Comparison between geared and direct-driven conversion systems and the classification of electrical machines used in WECS are discussed. A detailed analysis on the design aspects considering various topologies of PMSGs are encompassed in the literature. The PMSG design and optimization problems are solved by field computation techniques and optimized by using Soft Computing (SC) techniques .The three-dimensional, finite element software platform for the analysis and design of PMSGs is discussed. This paper also deals with the interdisciplinary modeling, analysis, and optimization of PMSG using Finite Element Analysis (FEM) and SC techniques. Finally, PMSGs are reviewed and compared for further exploration.

A growing number of studies suggest that SARS-CoV-2 could interfere with homeostatic mechanisms in the lung but the implications of this possible interference have not been fully explored in the literature. In this work, we examine the consequences that can be drawn from this hypothesis according to currently available knowledge. We suggest that one such consequence is the potential disruption of normal ventilation and perfusion of lung regions that may be distant from the infection sites. Loss of ventilation might result in local alveolar hypoxia and contribute to hypoxemia, which in turn could trigger homeostatic responses that enhance blood oxygenation by redistributing pulmonary blood circulation. Sudden changes in perfusion might then lead to the development of hydrostatic edema and eventually to vascular remodeling and inflammation. Therefore, the immune response might not be the only source of the substantial inflammation observed in lung tissues of patients with severe COVID-19, as is often assumed in the literature. The balance between the homeostatic and the immune reaction in each patient could account for the observed heterogeneity of the clinical manifestations of COVID-19.

This study presents application of demand response strategy in a standalone wind-solar-battery hybrid energy system (HES). Inputs for the designed HES are wind speed, solar radiation, temperature and load demand which is variable with time. In this study, hourly values of meteorological data and hourly load demand are considered in one year. An improved gravitational search algorithm (IGSA) is used to optimize the configuration of the standalone wind-solar-battery hybrid power system. The optimal objectives of the system are cost of the system in life cycle, the loss of power supply probability（LPSP）and the energy excess percentage（EXC）.The effect of demand response on economic benefit and energy storage allocation of the standalone wind-solar-battery system is studied. The obtained optimal configuration of the proposed HES can provide minimal energy cost with excellent performance and reduced waste and unmet load.

Vectorial optical fields have sparked considerable interest with potential applications such as optical nano-fabrication, optical micromachining, quantum information processing, optical imaging, and so on. Traditional compact vectorial optical generators with amplitude modulation perform poorly in terms of diffraction effect reduction. In order to address this issue, the refractive 4f system in amplitude modulation is longitudinally aligned using an optimization approach presented in this research. The phase images used for longitudinal alignment are loaded into the liquid crystal spatial light modulator (SLM), and the distance between the lens and the mirror in the reflective 4f system is adjusted for longitudinal alignment by compensating for the neglected phase in the integrated module of the compact vectorial optical field generator. The spot images collected by the CCD are processed using the improved eight-direction Sobel operator, and the longitudinal alignment in the reflective 4f system is determined by the sharpness of the image. The sharpness of the lines' edges and the overall image are both enhanced after optimization compared to before optimization. The results show that the proposed method can significantly reduce the longitudinal alignment error of the reflective 4f system in the amplitude modulation of the compact vectorial optical field generator, lessen the diffraction effect, and improve the performance of the system.

The age of information (AoI) metric was proposed to measure the freshness of messages obtained at the terminal node of a status updating system. In this paper, the AoI of discrete time status updating system with probabilistic packet preemption is investigated by analyzing the steady state of a three-dimensional discrete stochastic process. Assuming the queue used in system is Ber/Geo/1/2*/η, which represents that the system size is 2 and the packet in buffer can be preempted by fresher packet with probability η. Instead of considering system’s AoI separately, we use a three-dimensional state vector (n,m,l) to simultaneously track the real time changes of the AoI, the age of packet in server, and the age of packet waiting in buffer. We give the explicit expression of system’s average AoI, and show that the average AoI of system without packet preemption is obtained by letting η=0. When η is set to 1, the mean of AoI of system having Ber/Geo/1/2* queue is obtained as well. Combining the results we have obtained and comparing them with corresponding average continuous AoIs, we propose a possible relationship between average discrete AoI with Ber/Geo/1/c queue and the average continuous AoI with M/M/1/c queue. For each of two extreme cases where η=0 and η=1, we also determine the stationary distribution of AoI using the probability generation function (PGF) method. The relations between average AoI and the packet preemption probability η, as well as AoI’s distribution curves in two extreme cases are illustrated by numerical simulations. Notice that the probabilistic packet preemption may occur, for example, in an energy harvest (EH) node of wireless sensor network, where the packet in buffer can be replaced only when the node collects enough energy. In particular, to exhibit the usefulness of our idea and methods and highlight the merits of considering discrete time systems, in this paper we give much more explanations showing that how the results about continuous AoI is derived by analyzing the corresponding discrete time system, and how the discrete age analysis is generalized to the system with multiple sources. In terms of packet service process, we also propose our idea to analyze system’s AoI when the service time distribution is relaxed to be arbitrary.

With the advent of real-time MRI, the motion and passage of cerebrospinal fluid can be visualized without gating and exclusion of low-frequency waves. This imaging modality gives insights into low-volume, rapidly oscillating cardiac-driven movement as well as sustained, high-volume, slowly oscillating inspiration-driven movement.Inspiration means a spontaneous or artificial increase in the intrathoracic dimensions independent of body position. Alterations in thoracic diameter enable the thoracic and spinal epidural venous compartments to be emptied and filled, producing an upward surge of cerebrospinal fluid inside the spine during inspiration; this surge counterbalances the downward pooling of venous blood toward the heart.Real-time MRI, as a macroscale in vivo observation method, could expand our knowledge of neurofluid dynamics, including how astrocytic fluid preloading is adjusted and how brain buoyancy and turgor are maintained in different postures and zero gravity.Along with these macroscale findings, new microscale insights into aquaporin-mediated fluid transfer, its sensing by cilia and its tuning by nitric oxide will be reviewed. By incorporating clinical knowledge spanning several disciplines, certain disorders—congenital hydrocephalus with Chiari malformation, idiopathic intracranial hypertension and adult idiopathic hydrocephalus—are interpreted and reviewed according to current concepts, from the basics of the interrelated systems to their pathology.

Decision tables have been used for many years in data processing and business applications to simulate complex rule sets. Several computer languages have been developed based on rule systems and they are easily programmed in several current languages. Land management and river-reservoir models simulate complex land management operations and reservoir management in highly regulated river systems. Decision tables are a precise yet compact way to model the rule sets and corresponding actions found in these models. In this study, we discuss the suitability of decision tables to simulate management in the river basin scale Soil and Water Assessment Tool (SWAT+) model. Decision tables are developed to simulate automated irrigation and reservoir releases. A simple auto irrigation application of decision tables was developed using plant water stress as a condition for irrigating corn in Texas. Sensitivity of the water stress trigger and irrigation application amounts were shown on soil moisture and corn yields. In addition, the Grapevine Reservoir near Dallas, Texas was used to illustrate the use of decision tables to simulate reservoir releases. The releases were conditioned on reservoir volumes and flood season. The release rules as implemented by the decision table realistically simulated flood releases as evidenced by a daily NSE (Nash-Sutcliffe Efficiency) of 0.52 and a percent bias of -1.1%. Using decision tables to simulate management in land, river and reservoir models was shown to have several advantages over current approaches including: 1) mature technology with considerable literature and applications, 2) ability to accurately represent complex, real world decision making, 3) code that is efficient, modular and easy to maintain, and 4) tables that are easy to maintain, support, and modify.

Nowadays, energy decarbonization due to the integration of renewable energy sources presents important challenges to overcome. The intermittent nature of photovoltaic systems reduces power quality by producing voltage variations and frequency deviations in electrical systems networks, especially, in weak and isolated distribution systems in developing countries. This paper presents a power smoothing method improving the low pass filter and moving average for grid-connected photovoltaic system, the novel method includes state-of-charge monitoring control of the supercapacitors energy storage system to reduce the fluctuations of photovoltaic power at the point of common coupling. A case study for a microgrid in a high-altitude city in Ecuador is presented with exhaustive laboratory tests using real data. The ultimate goal of this research is to improve energy power quality in electrical distribution systems to cope with the growth of renewable penetration.

Objective: This study investigated the effect of the hierarchical medical system under the national health insurance program on resident’s healthcare-seeking behavior in Taiwan. Background: Healthcare authorities in Taiwan initiated the allowance reduction of outpatient visits at regional hospitals and higher hierarchical hospitals from 2018. The ultimate goal is to implement a hierarchical medical system and provide the residents accessible as well as consistent medical services. Methods: This research was conducted through the questionnaire survey method and data were collected between August and December 2018 from the records of subjects who had recently sought medical attention. A total of 1,340 valid questionnaires were returned. Results: Regarding the effect on healthcare-seeking behavior, the following factors were significant: being aged between 40 to 49 (p＜.1), subjects with an educational background of junior high school (p＜.05), those who were not aware of the policy (p＜.001), and an awareness about both the hierarchical medical system and the policy to reduce outpatient visits to large hospitals (p＜.001). Conclusion: The public should be made aware about the hierarchical medical system to improve healthcare.

Space and water heating for residential and commercial buildings amount to a third of the European Union’s total final energy consumption. Approximately 75% of the primary energy is still produced by burning fossil fuels, leading to high greenhouse gas emissions in the heating sector. Therefore, policymakers increasingly strive to trigger investments in sustainable and low-emission heating systems. This study forms part of the “Roll-out of Deep Geothermal Energy in North-West-Europe”-project and aims at quantifying the spatial heat demand distribution in the Interreg North-West-Europe region. An open-source geographic information system and selected Python packages for advanced geospatial processing, analysis, and visualization are utilized to constrain the maps. These were combined, streamlined, and optimized within the open-source Python package PyHeatDemand. Based on national and regional heat demand input data, three maps are developed to better constrain heat demand at a high spatial resolution of 100*100m2 for the residential and commercial sectors, and for both together (in total). The developed methodology cannot only be applied to transnational heat demand mapping but also on various scales ranging from city district level to states and countries. In addition, the workflow is highly flexible working with raster data, vector data, and tabular data. Results reveal a total heat demand of the Interreg North-West-Europe region of about 1,700TWh. The spatial distribution of the heat demand follows specific patterns, where heat demand peaks are usually in metropolitan regions like for the city of Paris (1,400MWh/ha), the city of Brussels (1,300MWh/ha), the London metropolitan area (520 MWh/ha), and the Rhine-Ruhr region (500 MWh/ha). The developed maps are compared with two international projects, Hotmaps and Heat Roadmap Europe’s Pan European Thermal Atlas. The average total heat demand difference from values obtained in this study to Hotmaps and Heat Roadmap Europe is 24 MWh/ha and 84 MWh/ha, respectively. It is assumed that the implementation of real consumption data is an improvement in spatial predictability. The heat demand maps are therefore predestined to provide a conceptual first overview for decision-makers and market investors. The developed methods will further allow for anticipated mandatory municipal heat demand analyses.

A block-diagonal fuzzy neural network for short-term load forecasting is proposed. DBD-FELF consists of fuzzy rules with consequent parts that are neural networks with internal recurrence. These networks have a hidden layer which consists of pairs of neurons with feedback connections between them. The overall fuzzy model partitions the input space in partially overlapping fuzzy regions, where the recurrent neural networks of the respective rules operate. The partition of the input space and determination of the fuzzy rule base is performed by use of Fuzzy C-Means clustering algorithm and the RENNCOM constrained optimization method is applied for consequent parameter tuning. The electric load time-series of the Greek power system is examined, and hourly-based forecasting for the whole year is performed. The performance of DBD-FELF is tested via extensive experimental analysis and the results are promising, since an average percentage error of 1.18% is attained, along with an average yearly absolute error of 76.2 MW. Moreover, DBD-FELF is compared with Deep Learning, fuzzy and neurofuzzy rivals, such that its particular attributes are highlighted.

This paper presents the process of designing, fabrication, assembling, programming and optimizing a prototype of a nonlinear mechatronic Ball-Plate System (BPS) as a laboratory platform for STEM engineer education. Due to the nonlinearity and complexity of BPS, task presents challenging issues, such as: 1) difficulties in controlling the stabilization of a given position point known as steady state error, 2) position resolution known as specific distance error and 3) adverse environmental effects - light shadow error, also discussed in this paper. The laboratory BPS prototype for education was designed, manufactured and installed at the Karlovac University of Applied Sciences at the Department of Mechanical Engineering, Study of mechatronics. The low-cost two degrees BPS system uses a USB HD camera for computer vision as feedback and two DC servomotors as actuators. Due to controlling problems, an advanced block diagram of control system is proposed and discussed. An open-source control system based on Python scripts that allows the use of ready-made functions from the library allows changing the color of the ball and the parameters of the PID controller, thus indirectly simplifying control system and directly the mathematical calculation. The authors will continue their research on this BPS mechatronic platform and control algorithms.

Research conducted on individuals with depression reveals that major depressive disorders (MDDs) coincide with diminished levels of the inhibitory neurotransmitter γ-aminobutyric acid (GABA) in the brain, as well as modifications in the subunit composition of the primary receptors (GABAA receptors) responsible for mediating GABAergic inhibition. Furthermore, there is substantial evidence supporting the significant role of GABA in regulating stress within the brain, which is a pivotal vulnerability factor in mood disorders. Although there is substantial evidence indicating that GABA may be affecting GABA receptors in peripheral tissues, there is comparatively less evidence supporting its direct action within the brain. Emerging evidence highlights that GABA may exert beneficial effects on the brain and psyche through the gut-brain axis. GABA is readily available and approved as a food supplement in many countries. While GABA enjoys wide consumer acceptance in Eastern Asian markets, with many consumers reporting favorable effects on stress regulation, mood, and sleep, rigorous independent research is still largely lacking. Basic research, coupled with initial clinical findings, makes GABA an intriguing neuro-nutritional compound deserving of clinical studies in individuals with depression and other psychological problems.

The main goal of the study is to assess the Decision criteria, and the determining factor for the sustainability of entrepreneurial startups in order to contribute towards social inclusion and capacity building. Both concepts are in the development phase and are the outcome of entrepreneurial ecosystem and individual behavior and traits. The current study observed the research problem as entrepreneurship and the entrepreneurial startups are the continuous phenomena required for every economy. The lack of an efficient ecosystem and incompetent trait of an entrepreneur drags the entrepreneurial startup to a failure. Therefore, an assessment on decision criteria and determining factors categorizing them with their importance may provide requisites to lead a successful entrepreneurial startup contributing to the social inclusion and capacity building. The study solved the research problem by statistical assessment of decision criteria and determining factors and categorizing them with their importance may provide requisites to lead a successful entrepreneurial startup. The research is built on research questions, objectives, conceptual model, and hypothesis which are tested based on the data collected. The collection of data was done through a survey questionnaire on a sample of established entrepreneurs. The study concludes that five components of decision criteria are Region, Competition, Funding Opportunities, Tax System, and Country Economic Situation, whereas the eight components Consciousness and Reliable, Pursuit Results, Flexibility, Stress Resistance, Skills Identification Exploitation Potential Market Opportunities, Leadership, Creativity and Innovation, and Delegation Decision Making of determining factor are required for a successful entrepreneurial startup to be able to work towards social inclusion and capacity building.

Abstract: Intensity of natural and man-made disasters is increasing day by day. Disaster is one of the major threats that affects the sustainable development of tourist attractions. Big data and Internet of Things(IoT) will greatly improve the disaster management. Based on the Big Data and IoT, a tourism attraction disaster management system is designed, divided into several stages namely pre-disaster early warning prevention, disaster mitigation, recovery and reconstruction after disaster and updating disaster planning. Then, the system flow is analysed, as well as the system structure is constructed. Additional, system function and its operation flow are introduced, including disaster warning, disaster relief, disaster assessment, real-time monitoring and supporting disaster planning functions. Finally, an application case is introduced. Research intends to improve tourism area disasters management.

We are in amidst of COVID-19 pandemic. Since Dec 2019, severe acute respiratory corona virus (SAR-CoV-2) has infected more than half a billion people killing nearly 7 million people worldwide. Now the BA.5 variant of SARS-CoV-2 is causing mayhem and driving the global surge. Epidemiologist are aware of the fact that this virus is capable of escaping immunity and likely to infect the same person multiple times despite adequate vaccination status. Elderly people of age more than 60 years and those with underlying health conditions are considered as high-risk who are likely to suffer complications and death. While it is tempting to frame complications and mortality from COVID-19 as a simple matter of too much of a virulent virus in too weak of a host, much more is at play here. Framing the pathophysiology of COVID-19 in the context of the Chrousos and Gold model of the central stress response system can shed insight into its complex pathogenesis. Understanding the mechanisms by which pharmacologic modulation of the central stress response system via administration of clonidine and/or dexamethasone may offer an explanation as to why a viral pathogen can be well tolerated and cleared by one host while inflaming and killing another.

In this paper, initially a mathematical model is formulated for transient frequency of power system considering time delays which occur while transmitting the control signals in open communication infrastructure. Time delay negligence in a power system leads to improper measurement of frequency variation in power system. The study of impact of time delays on the stability of power system is performed by estimating the decay rate of frequency wave form using Kalman Filter (KF). In power system, there is a possibility of multiple time delays. This paper also focusses on developing Interacting Multiple Model (IMM) Algorithm with multiple model space using Kalman Filter (KF) as state estimator tool. The multiple time delays in power system is considered as multiple model space. The result shows that KF provides better estimate of correct model for a particular input-set. The qualitative properties of Riccati difference equation (RDE) in terms of state error covariance of IMMKF are also analyzed and presented.

Chromium (VI) is carcinogenic through intermediates formed in the cellular milieu by reduction with small reductants like glutathione (GSH), ascorbate (As), cysteine (Cys) and NADPH. Although the reduction of chromate by thiols have been investigated, the participation of Cr(IV) intermediates has been inferred only indirectly due to its refractive behavior towards EPR spectroscopy and the lack of true chromium (IV) complexes for comparative studies. Biological data from numerous reports indicate that Cr(IV) is the species most likely responsible for the carcinogenicity of Cr(VI). Our kinetic studies suggested that in acidic solutions the reduction of chromate with GSH affords mostly a chromium(IV) intermediate. As a step towards the full characterization of the paramagnetic species involved in the reduction of chromate by thiols at neutral pH, we embarked in the investigation of this reaction using the Superconducting QUantum Interference Device (SQUID) Magnetic Property Measuring System®. Our results indicate a strong influence of the temperature. At 2K, the saturation magnetization method was applied to the frozen reaction when it reached the peak of formation of intermediates. Contrary to our expectations, the contributions were calculated to be 30 % of Cr(IV) and 69 % of Cr(V) at this pH. When the Curie-Weiss method was utilized, the effective magnetic moment was dependent on the portion of the data utilized and generally higher proportion of Cr(IV) was found when the fitting is performed with data from higher temperature.

The DNA damage response (DDR) system is a complicated network of signaling pathways that detects and repairs DNA damage or induces apoptosis. Critical regulators of the DDR network include the DNA damage kinases ataxia telangiectasia mutated Rad3-related kinase (ATR) and ataxia-telangiectasia mutated (ATM). The ATR pathway coordinates processes such as replication stress response, stabilization of replication forks, cell cycle arrest, and DNA repair. ATR inhibition disrupts these functions, causing reduction of DNA repair, accumulation of DNA damage, replication fork collapse, inappropriate mitotic entry and mitotic catastrophe. Recent data have shown that the inhibition of ATR can lead to synthetic lethality in ATM-deficient malignancies. In addition, ATR inhibition plays a significant role in the activation of the immune system by increasing the tumor mutational burden and neoantigen load as well as by triggering the accumulation of cytosolic DNA and subsequently inducing the cGAS-STING pathway and the type I IFN response. Taken together, we review stimulating data showing that ATR kinase inhibition can alter the DDR network, the immune system and their interplay and therefore potentially provide a novel strategy to improve the efficacy of antitumor therapy, using ATR inhibitors as monotherapy or in combination with genotoxic drugs and/or immunomodulators.

Epidemiological evidence points to an inverse association between Parkinson's disease (PD) and almost all cancers except melanoma, for which this association is positive. The results of many studies have found that patients with PD are at reduced risk of the majority of neoplasm occurrence and death. Several potential biological explanations exist for the inverse relationship between cancer and PD. Recent results identified several PD-associated proteins and factors mediating cancer development and cancer-associated factors affecting PD. Accumulating data points to the role of genetic traits, members of the synuclein family, neurotrophic factors, ubiquitin-proteasome system, circulating melatonin, and transcription factors as such mediators. We present recent data about shared pathogenetic factors and mediators that might be involved in the association between these two diseases. We also discuss how these factors, individually or in combination, may be involved in pathology, serve as links between PD and cancer, and affect the prevalence of these disorders. Identification of these factors and investigations of their mechanisms will lead to the discovery of new targets for the treatment of both diseases

Nowadays, energy saving is considered a key issue worldwide, as it brings a variety of benefits: reducing greenhouse gas emissions, reducing demand for energy imports, and lowering costs on a household and economy-wide level. Researchers and building designers are looking to optimize building efficiency by means of new energy technologies. Changes in existing buildings can also be made to reduce energy consumption and costs. These may include the usage of new heat transfer fluids based on nanofluids. In this work, an extended experimental campaign (from February 2020 to March 2021) has been carried out on an educational building in the Campus of University of Salento, Lecce – Italy, in order to evaluate the increase in performance of the HVAC system, due to the usage of a nanofluid. The results show that, under optimal operating conditions, plant performance improves more than 10%, with average daily peaks of about 15%.

Few data are available on the health impact of tobacco heating products (THPs) at the population level. We used systems dynamics modelling to estimate effects in the established THP market in Japan. We projected effects of THP use in overall mortality up to 2100 and compare those projections against a baseline scenario based on smoking rates pre-THP launch, i.e., smoking only. The model was informed using data from publicly available sources and the literature, including population size, yearly deaths and smoking prevalence with initialisation year (2004) and, births and migration from 2004 to 2065. Transitions between products were estimated from cross-sectional population surveys in Japan. Potential life-years saved with the introduction of THPs was 13 million by 2100 compared with smoking only. In worst-case scenario, population health gains would be seen with THPs risk 10–50% lower risk than smoking. Assuming equal risk for dual use and smoking, THP risk would need to be at least 10% lower than smoking to achieve a population health benefit by 2100. In credible scenarios, substantial population health gains will follow the introduction of THPs in Japan in a relatively short time frame.

The mechanisms underlying the effects of γ-globulin therapy for bacterial infections are thought to involve bacterial cell lysis via complement activation, phagocytosis via bacterial opsonization, toxin neutralization, and antibody-dependent cell-mediated cytotoxicity. Nevertheless, recent advances in the study of pathogenicity in gram-negative bacteria have raised the possibility of an association between γ-globulin and bacterial toxin secretion. Over time, new toxin secretion systems like the type III secretion system have been discovered in many pathogenic gram-negative bacteria. With this system, the bacterial toxins are directly injected into the cytoplasm of the target cell through a special secretory apparatus without any exposure to the extracellular environment and, therefore, with no opportunity for antibodies to neutralize the toxin. However, because antibodies against the V-antigen, which is located on the needle-shaped tip of the bacterial secretion apparatus, can inhibit toxin translocation, this raises the hope that the toxin might be susceptible to antibody targeting. Because multi-drug resistant bacteria are now prevalent, inhibiting this secretion mechanism is attractive as an alternative or adjunctive therapy against lethal bacterial infections. Thus, it would not be unreasonable to define the blocking effect of anti-V-antigen antibodies as the fifth mechanism for immunoglobulin action against bacterial infections.

Background: Low back pain (LBP) is a major cause of disability globally, and the diagnosis of LBP is challenging for clinicians. Objective: This study aimed to assess the accuracy level of arti-ficial intelligence as a Clinical Decision Support System (CDSS) called Therapa compared to MRI in predicting lumbar disc herniated patients. Methods: One hundred low back pain patients aged ≥18 years old were included in the study. The study was conducted in 3 stages, at first, a case se-ries was conducted by matching MRI and Therapha diagnosis for 10 patients. Subsequently, Delphi methodology was employed to establish a clinical consensus. Lastly, to determine the ac-curacy of the newly developed software, a cross-sectional study was undertaken, involving 100 patients. Results: The software showed a significant diagnostic accuracy with the area under the curve in the ROC analysis determined as 0.84 with a sensitivity of 88% and a specificity of 80%. Conclusion: The study’s findings revealed that CDSS using Therapha has a reasonable level of ef-ficacy, and this can be utilized clinically to acquire faster and more accurate screening of patients with lumbar disc herniation.

The aim of this study was to investigate the performance of the developed real-time controlled bio-liquor circulation system (BCS) on a farm-scale to optimize bioreactor operation for manure treatment, and to subsequently reduce both manure-related malodor and greenhouse gas (GHG) emissions from slurry pits. The BCS was operated sequentially as per swine manure inflow (anoxic, aerobic, and settling) circulation to the slurry pit. The duration of each operational phase was self-adjusted in real-time using a novel algorithm for detecting the control point on the oxidation-reduction potential (ORP) and pH (mV)-time profiles, the nitrogen break point (NBP), and the nitrate knee point (NKP) in the aerobic and anoxic phases, respectively. The NH₄-N in the slurry manure was thoroughly removed (100%) in the bioreactor, optimizing the duration of each operational phase by accurately detecting real-time control points. The newly-developed real-time BCS decreased the nitrogen and organic matter content in the slurry pit manure by > 70%, and the potential ammonia and methane emissions by 75% and 95%, respectively. This study highlights that the this improved BCS that utilizes ORP tracking and pH (mV)-time profiles can effectively optimize BCS operation, and thereby reduce malodor and GHG emissions from swine farms.

Waste heat recovery stands out as a promising technique to tackle both energy shortages and environmental pollution. Currently, this valuable resource, generated from processes like fuel combustion or chemical reactions, is often dissipated into the environment, despite its potential to significantly contribute to the economy. To harness this untapped potential, a travelling-wave thermo-acoustic generator has been designed and subjected to comprehensive experimental analysis. Fifty-two data corresponding to different working conditions of the system were extracted to build ANN, ANFIS and ANN-PSO models. Evaluation of performance metrics reveals that the ANN-PSO model demonstrates the highest predictive accuracy (R^2=0.9959), particularly in relation to output voltage. This research demonstrates the potential of machine learning techniques for the analysis of thermo-acoustic systems. By doing so, it is possible to get an insight into nonlinearities inherent to thermo-acoustic systems. This advancement empowers researchers to forecast the performance characteristics of alternative configurations with a heightened level of precision.

Individual investors often struggle to predict stock prices due to the limitations imposed by the computational capacities of personal laptop Graphics Processing Units (GPUs) when running intensive deep learning models. This study proposes solving these GPU constraints by integrating deep learning models with technical analysis methods. This integration significantly reduces analysis time and equips individual investors with the ability to identify stocks that may yield potential gains or losses in an efficient manner. Thus, a comprehensive buy and sell algorithm, compatible with average laptop GPU performance, is introduced in the study. This algorithm offers a lightweight analysis method that emphasizes factors identified by technical analysis methods, thereby providing a more accessible and efficient approach for individual investors. To evaluate the efficacy of this approach, we analyzed the performance of eight deep learning models: LSTM (4 layers), CNN, BiLSTM (4 layers), CNN Attention, BiGRU CNN BiLSTM Attention, BiLSTM Attention CNN, CNN BiLSTM Attention, and CNN Attention BiLSTM. These models were used to predict stock prices for Samsung Electronics and Celltrion Healthcare. The CNN Attention BiLSTM model displayed superior performance among these models, with the lowest validation mean absolute error value. In addition, an experiment was conducted using WandB Sweep to determine the optimal hyperparameters for four individual hybrid models. These optimal parameters were then implemented in each model to validate their back-testing rate of return. The CNN Attention BiLSTM hybrid model emerged as the highest-performing model, achieving an approximate rate of return of 5 percent. Overall, this study offers valuable insights into the performance of various deep learning and hybrid models in predicting stock prices. These findings can assist individual investors in selecting appropriate models that align with their investment strategies, thereby increasing their likelihood of success in the stock market.

The storage of biochemical information, which is a prerequisite for the development of the first cell, is an unsolved problem affecting all concepts of the origin of life. However, if the protected environment in the continental crust is taken into account, completely new possibilities emerge for identifying processes that may have been crucial for the formation of the first cell. Under this background, we can hypothesize that, before cellular life began, a self-sustaining cycle of molecular reaction steps with information storage in RNA existed outside of a cell. This cycle was made possible in an open system bound to gas-permeable tectonic fracture zones with a high proportion of CO2 and N2. The formation of peptides and vesicles in supercritical CO2 and the chemical evolution of peptides have already been proven for the upper continental crust. Further considerations include the interactions of vesicles with catalytic peptides and the emergence of proto-tRNA. In combination with the formation of proto-tRNA synthetases, which consist of only two amino acid species and associated proto-tRNAs, the first RNA as an information storage system could have been formed with the information of proto-enzymes.

Obstructive sleep apnea (OSA) is a common sleep disorder that is associated with a wide variety of health conditions, including cardiovascular, cerebrovascular, metabolic, neoplastic, and neurocognitive manifestations. OSA is mainly characterized by repeated upper airway obstructions during sleep that cause episodes of chronic intermittent hypoxia (IH), resulting in tissue hypoxia-reoxygenation. Decreased arterial oxygen pressure (PaO2) and hemoglobin saturation (SatO2) stimulate reflex responses to overcome the obstruction. The prevalence of OSA is significant worldwide, particularly in women during pregnancy, due to the physiological changes associated with this stage of life, especially in its later stages. It is associated with an increased risk of hypertension, pre-eclampsia and diabetes, among others. OSA during pregnancy can interfere with normal fetal development and is associated with growth retardation, preterm birth, or low term weight. Carotid body stimulation and hypoxia-reoxygenation episodes contribute to cardiovascular disease and oxidative stress, that can harm both, mother and the fetus, even into the adulthood. Because gestational intermittent hypoxia (GIH) is the hallmark of OSA in pregnancy, this review examines the impact of GIH on maternal, fetal, and offspring respiratory outcomes. Most adverse outcomes can be prevented by continuous positive airway pressure (CPAP) therapy and lifestyle changes if gestational OSA is detected early and treated appropriately.

Multiple Sclerosis (MS) is a neurodegenerative disease that mainly affects young adults. The high prevalence of the disease and its impact on patients' quality of life led to priority being given to the etiopathogenesis of the disease to identify possible modifiable factors and consequent effective intervention strategies. Recent hypotheses suggest that the etiopathogenesis of MS is multifactorial and includes factors related to the immune system, neuroinflammation and neuro-degeneration. In this scenario, sleep has a close - although indirect - relationship with MS, through its relationship with each of those factors. In particular, the role of cytokines in immune system impairment and neuroinflammation, mitochondrial dysfunction and oxidative stress in neurodegeneration all are associated with sleep in different forms. Furthermore, melatonin in relation with vitamin D has a potential therapeutic effect in MS sleep-related problems. Given the growing interest of research in the mechanisms underlying MS and therapies able to alleviate MS-related symptoms at all stages of the disease, a more in-depth study of the role of sleep disturbances and the factors intrinsically related to sleep and MS could be useful both to investigate the etiopathogenetic factors of MS and to develop potential non-invasive intervention strategies for MS treatment.

Excessive high fat diet (HFD) consumption can induce food addiction which is believed to involve the communication between the hypothalamus and mesolimbic dopaminergic neurons, originating in the ventral tegmental area (VTA) and projecting to the nucleus accumbens (NAc). These brain regions are densely populated with opioid receptors, raising the possibility that these receptors, and particularly mu opioid receptors (MORs), are involved in mediating reward induced by palatable foods. This study sought to investigate the involvement of MORs in high fat diet (HFD)-induced reward and if there is any difference between male and female subjects in this response. We also assessed if exposure to HFD would potentiate the rewarding action of oxycodone, a relatively selective MOR agonist. The place conditioning paradigm was used to determine if conditioning for a short time (STC, 2 h) or long time (LTC, 16 h) with HFD induces reward or alters the rewarding action of oxycodone. Male and female C57BL/6J mice as well as MOR knockout and wildtype mice of both sexes were tested for basal place preference on day 1 and then conditioned with HFD in one chamber and regular chow diet (RCD) in another chamber and tested for place preference again after three sets of STC and again after a set of LTC. Each set consisted of two conditioning with RCD and two conditioning with HFD. Controls were conditioned with RCD in both conditioning chambers. Following the last place preference test, mice were treated with oxycodone and conditioned in the HFD-paired chamber and saline in the RCD-paired chamber for one hour once a day to explore the possibility if the HFD could alter oxycodone reward. The result showed that HFD induced conditioned place preference (CPP) in both male and female subjects following the LTC. This response was further potentiated after oxycodone conditioning. The latter response was mediated via MORs, as it was blunted in MOR knockout mice. However, HFD-induced CPP was observed following LTC in only female MOR knockout mice, suggesting for sexual dimorphism in this response.

Compliant amplifying mechanisms are used widely in high-precision instruments driven by piezoelectric actuators, and the dynamic and static characteristics of these mechanisms are closely related to instrument performance. Although the majority of existing research has focused on analysis of their static characteristics, the dynamic characteristics of the mechanisms affect their response speeds directly. Therefore, this paper proposes a comprehensive theoretical model of compliant amplifying mechanisms based on the multi-body system transfer matrix method to analyze the dynamic and static characteristics of these mechanisms. The effects of the main amplifying mechanism parameters on the displacement amplification ratio and the resonance frequency are analyzed comprehensively using the control variable method. An iterative optimization algorithm is also used to obtain specific parameters that meet the design requirements. Finally, simulation analyses and experimental verification tests are performed. The results indicate the feasibility of using the proposed theoretical compliant amplifying mechanism model to describe the mechanism’s dynamic and static characteristics, which represents a significant contribution to the design and optimization of compliant amplifying mechanisms.

Metal nanoparticles supporting plasmons are widely used to enhance electromagnetic fields, resulting in strong light-matter interactions at the nanoscale in a diverse range of applications. Recently, it has been shown that when metal nanorods are periodically arranged with proper lattice periods, surface lattice resonances (SLRs) can be excited and near fields can be greatly enhanced over extended volumes. In this work, we report significant near field enhancement over even larger volumes by placing the metal nanorod array within a Fabry-Pérot (F-P) microcavity. Results show that taking advantage of strong coupling between the SLR and the photonic F-P resonances, the electric field intensity of the bonding split mode can be enhanced by up to 1935 times, which is about three times of the enhancement of the SLR, and the greatly enhanced field can extend over most of the F-P microcavity. We further show that the F-P resonances of both odd and even orders can strongly couple to the SLR by varying the nanorods position from the middle of the microcavity. We expect that the proposed plasmonic-photonic coupling system will find promising applications in nanolasers, nonlinear optics and sensing.

This paper introduces the AltumView Sentinare smart activity sensor for senior care and patient monitoring. The sensor uses an AI chip and deep learning algorithms to monitor the activity of people, collect activity statistics, and notify caregivers when emergencies such as falls are detected. To protect privacy, only skeleton (stick figure) animations are transmitted instead of videos. The sensor is highly affordable, accessible, and versatile. It was a CES 2021 Innovation Award Honoree, and has been selected by Amazon as one of only three fall detection devices integrated into its Alexa Together urgent response service, and has received very positive reviews from Amazon customers. It has also been used in different senior care settings in about ten different countries. The paper presents the main features of the system, the evidences and lessons learned from its practical applications, and future directions.

The North Railway in the Kingdom of Saudi Arabia (KSA) extends over vast areas, crossing various terrains, including valleys, sand veins, plateaus, and hills. Therefore, the Railway was designed and implemented to suit this environmental diversity under the highest safety standards. However, the Railway may be subject to hazards for various reasons. In general, the possibility of direct surface runoff disasters increases if there are residential areas and facilities within the boundaries of drainage basins. Therefore, these areas should be studied, and the degree of hazard in drainage basins should be accurately determined. Hence, this study analyzed the degree of risk of 14 drainage basins affecting the North Train railway within the Wadi Malham drainage basin, using the risk degree model with eight parameters that have hydrological indications to give an idea of the behavior of direct surface runoff and interfere with increasing the risk of direct surface runoff. Researchers found 28.57% of the total basins in the study area have high-risk basin overall score values, which are basins 6, 7, 13, and 14. It is recommended to estimate the rainfall depth during different return periods, analyze soil permeability and land use classification in the study area, and apply hydrological modeling of drainage basins, which contributes to estimating the volume and peak of direct surface runoff in such arid and semi-arid environments that do not contain hydrometric stations to monitor the runoff. .

Voltage sags can cause the interruption of power supply and can negatively affect operations of customers. In this paper, the authors study the impact of battery energy storage systems (BESS) on voltage sags. A stochastic method of fault positions is used. Faults of various types are simulated and voltages are recorded. Firstly, with the BESS integrated into the network, there are higher residual voltages, fewer voltage sags and less expected critical voltage loss. Secondly, if the BESS converter power factor is reduced, recorded residual voltages are higher, voltage sags are fewer, and the number of expected critical voltage sags is lower. Finally, when three BESS converter control modes, namely constant voltage, constant power factor, and constant reactive power, were assessed, results showed similar voltage sag performances for constant power factor and constant reactive power modes. Furthermore, operating in constant voltage control outperformed the other two modes as it resulted in higher residual voltages, a lower number of voltage sags, and fewer expected critical voltage sags. The paper has demonstrated that the BESS can improve voltage sag performance. In addition, the power factor of the BESS converter and the mode of operation of the converter can influence the magnitude of the voltage sag performance improvement.

Mixing is considered as a critical process parameter (CPP) during process development due to its significant influence on reaction selectivity and process safety. Nevertheless, mixing issues are difficult to identify and solve owing to their complexity and dependence on knowledge of kinetics and hydrodynamics. In this paper, we proposed an optimization methodology using Computational Fluid Dynamics (CFD) based compartmental modelling to improve mixing and reaction selectivity. More importantly, we have demonstrated that through the implementation of surrogate-based optimization, the proposed methodology can be used as a computationally non-intensive way for rapid process development of reaction unit operations. For illustration purpose, reaction selectivity of a process with Bourne competitive reaction network is discussed. Results demonstrate that we can improve reaction selectivity by dynamically controlling rates and locations of feeding in the reactor. The proposed methodology incorporates mechanistic understanding of the reaction kinetics together with an efficient optimization algorithm to determine the optimal process operation and thus can serve as a tool for quality-by-design (QbD) during product development stage.

The purpose of this study is to improve the performance of walls under out-of-plane loads, particularly when subjected to the hammering action of the floors. The idea behind the paper is to provide the masonry walls with a device that behaves like a buttress, without having to build a traditional buttress. The solution presented here consists of a mechanical coupling between the three-dimensional net of steel ribbons of the CAM system and the CFRP strips. Since the steel ribbons of the CAM system have a pre-tension, the mechanical coupling allows the steel ribbons to establish a semi-rigid transverse link between the CFRP strips bonded on two opposite sides of a wall. Therefore, two vertical CFRP strips tied by the steel ribbons behave like the flanges of an I-beam and the flexural strength of the ideal I-beam counteracts the out-of-plane displacements of the wall. The experimental results showed that the combined technique inherits the strong points of both constituent techniques: the delamination load is comparable to that of the specimens reinforced with the CFRP strips and the overall behavior is ductile as for the specimens reinforced with the CAM system. They also allowed us to design a more performing combined technique.

This study examined the association between pressure injuries and complexity of abdominal temperature measured in residents of a nursing facility. The temperature served as a proxy measure for skin thermoregulation. Refined multiscale sample entropy and bubble entropy were used to measure the complexity of the temperature time series measured over two days at 1-minute intervals. Robust summary measures were derived for the multiscale entropies and used in predictive models for pressure injuries that were built with adaptive lasso regression and neural networks. Both types of entropies were lower in the group of participants with pressure injuries (n=11) relative to the group of non-injured participants (n=15). This was generally true at the longer temporal scales, with the effect peaking at scale τ=23 minutes. Predictive models for pressure injury on the basis of refined multiscale sample entropy and bubble entropy yielded 92% accuracy, outperforming predictions based on any single measure of entropy. Combining entropy measures with a widely used risk assessment score led to the best prediction accuracy. Complexity of abdominal temperature series could therefore serve as an indicator of risk of pressure injury.

Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus SARS-CoV-2, has been confirmed in over 10,000,000 individuals worldwide and has resulted in more than 500,000 deaths in a few months since it first surfaced. With such a rapid spread it is no surprise that there has been a massive effort around the world to collectively elucidate the mechanism by which the virus is transmitted. Despite this, there is still no definitive consensus regarding droplet versus airborne transmission of SARS-CoV-2. Public health officials around the world have introduced guidelines within the scope of droplet transmission. However, increasing evidence and comparative analysis with similar coronaviruses, such as severe acute respiratory syndrome (SARS-CoV-1) and middle eastern respiratory syndrome (MERS), suggest that airborne transmission of SARS-CoV-2 cannot be effectively ruled out. As the data supporting COVID-19 airborne transmission grows, there needs to be an increased effort in terms of technical and policy measures to mitigate the spread of viral aerosols. These measures can be in the form of broader social distancing and facial covering guidelines, exploration of thermal inactivation in clinical settings, low-dose UV-C light implementation, and greater attention to ventilation and airflow control systems. This review summarizes the current evidence available about airborne transmission of SARS-CoV-2, available literature about airborne transmission of similar viruses, and finally the methods that are already available or can be easily adapted to deal with a virus capable of airborne transmission.

The crosstalk between gut microbiota, intestinal epithelial cells, and innate and adaptive immune system governs the maintenance of the intestinal homeostasis. Any interference in this tight dialogue and in the processes preserving cellular homeostasis (e.g., autophagy) may dysregulate the immune response and impair the clearance of harmful bacteria favoring the dysbiotic alteration of the microbic flora that leads to chronic inflammation. Gut dysbiosis is strongly associated with gastrointestinal inflammatory disorders, among them the inflammatory bowel disease (IBD). This review discusses the current knowledge on IBD, from the genetic background of high-risk patients to the molecular mechanisms underlying the disease, the contribution of the microbic flora, and the role of autophagy in intestinal epithelial cells and provides the state of art regarding the targeted-nutritional approaches aimed to restore the beneficial crosstalk between an “anti-inflammatory” microbiota and the host. Analysis of the molecular pathogenesis of IBD will help identify genetic and diet-associated risk factors and thus suggest personalized strategies to prevent and manage the disease to improve quality of life with long-term maintenance of the remission phase.

Effect of wireworm-damaged lettuce roots on antioxidative defence system (ascorbate-glutathione cycle, photosynthetic pigments) and movement of insect/slug parasitic nematodes towards determined root exudates was studied in a glasshouse experiment. Lettuce seedlings were grown in a substrate soil in the absence/presence of wireworms (Elateridae). Determination of antioxidants and photosynthetic pigments were analysed. Volatile organic compounds (VOC) emitted from lettuce roots were investigated by GC-MS. Herbivore induced root compounds, namely 2,4-nonadienal, glutathione and ascorbic acid, were selected for a chemotaxis assay with nematodes Steinernema feltiae, S. carpocapsae, Heterorhabditis bacteriophora, P. papillosa, and O. myriophilus. Root pests negatively affected photosynthetic pigment contents even before the appearance of visible symptoms, as protective pigments responded to the presence of reactive oxygene species (ROS). Using lettuce as a model plant, we recognised ascorbate-glutathione system as a redox hub in defense response against wireworms and analysed its role in root-exudate mediated chemotaxis of nematodes. Infected plants also demostrated increased levels of volatile 2,4-nonadienal. Entomopathogenic nematodes (EPNs, S. feltiae, S. carpocapsae, and H. bacteriophora) proved to be more mobile than parasitic nematodes O. myriophilus and P. papillosa towards chemotaxis compounds. Among them 2,4 – nonadienal repelled all tested nematodes. Most exudates that are involved in belowground tritrophic interactions remain unknown but an increasing effort is being made in this field of research. Understanding more of these complex interactions would not only allow a better understanding of the rhizosphere but could also offer ecologically sound alternatives in pest management of agricultural systems.

Given the recent advances in information technology, speed has become an important requirement in data transmission. Given that an LED (light emitting diode) can turn on and off several thousand or even millions of times per second, then LiFi (light fidelity) technology has strong advantages over WiFi (wireless fidelity) in terms of speed. In this research project, a consortium involving an academic institution decided to evaluate the maturity of LiFi technology and design a demonstrator that could be integrated in a vehicle via an intra-vehicle data transmission system. The demonstrator is to implement a two-way LiFi communication between the reading light of a vehicle and a portable device such as a tablet or smartphone. The nomadic device must display, via an interface, data in real time. The data is RNT (Digital Terrestrial Radio) and TNT (Digital Terrestrial Television) provided by an in-house antenna. LiFi depends on optical technology. The optical characteristics have significant influences on the system. The paper shows ways to improve the system’s throughput, communication range, and reception area. By replacing the reading light with a more powerful one with better optical characteristics, the communication range and reception area improve. By using symmetrical power supply, the throughput improves. Likewise, by using optical filters that eliminate noise, the throughput also improves. We have demonstrated that LiFi data transmission in the vehicle is feasible; low-definition videos were successfully transmitted. The paper acknowledges that there are still various avenues for this work’s improvement.

The connection of two phenomena - non-conservative friction forces and dissipation-induced instability can lead to many interesting engineering problems. The paper studies general material-dependent damping influence on dynamical instability of disc brake systems leading to brake squeal. The effect of general damping is demonstrated on a minimal and complex model of a disc brake. A complex system including material-dependent damping is defined in the commercial finite element software. The finite element model validated by experimental data on the brake-disc test bench is used to compute the influence of a pad and a disc damping variations on system stability by complex eigenvalue analysis. Analyzes show a significant sensitivity of the experimentally verified unstable mode of the system to the ratio of the damping between the disc and the friction material components.

Based on the topology of wide area protection system (WAPS), after studying the reliability of hardware system and information flow in the WAPS and establishing the reliability assessment model, the multi-service reliability analysis method with multi monitoring and protection tasks in WAPS was proposed. In the model, the impact of network quality of service (QoS) such as information flow loss and delay, is studied. On the base of the model, the multi-service reliability evaluation method is employed to analyze the reliability of a WAPS of IEEE14 node power system, and the key nodes of the WAPS is given, which provides a basis for improving the reliability of the WAPS.

Modern power systems are confronted with a spread concern on the frequency stability issue due to the widespread integration of randomly fluctuating renewable resources. To address the above concern, this work introduces a load frequency control (LFC) scheme based on a hydrogen based energy-storage unit and a parameter tuning strategy for fractional-order proportional-integral-derivative (FOPID) controller. Firstly, a two-area interconnected power system (IPS) model, including thermal, hydro, solar, wind, gas power generator, and hydrogen based energy-storage unit, is established. Then, a FOPID controller is designed for this IPS model, and an improved gradient-based optimizer (IGBO) is developed to adaptively regulate the parameters of the FOPID controllers. Finally, the effectiveness of the offered LFC scheme are tested through load disturbance and renewable energy fluctuations test scenarios, and provides comparison and robustness analysis among different schemes. The test results validated that the offered LFC scheme can effectively suppress the frequency fluctuations of IPS and has excellent robustness.

Modeling and assessing the sustainability of machining systems has been considered to be a crucial approach to improving the environmental performance of the machining processes. As the most common machining system, the computer numerical control (CNC) milling system is a typical man-machine cooperative system where the activities of the machine tool and operator generate material and energy consumption. However, the energy consumption of the operator in the CNC milling system has often been ignored in most existing research. Therefore, existing methods fail to provide a comprehensive understanding of the sustainability of the CNC milling system. To fill this gap, an exergy loss assessment method is proposed to investigate the sustainability of the CNC milling system, where the energy consumption of the operator, the energy consumption of the machine tool, and material consumption are taken into consideration. The key performance indexes of the energy consumption of the operator, the energy consumption of the machine tool, the exergy loss, and the specific exergy loss (SEL) are analyzed and modeled. To demonstrate the feasibility of the proposed method, a case study was carried out on a three-axis machining center (XH714D), in which SEL was found to be 88.04J/mm3. The proposed method is effective to assess the sustainability of the CNC milling system, and the established exergy loss models build a good basis for exergy efficiency optimization.

Alzheimer’s disease and related dementias (ADRD) remain a public health challenge in developing counties. We developed a needs-based essential care service package (ECSP) for care planning of persons living with dementia (PLWD) using a cross-sectional survey among PLWD in institutions in six cities in China (n= 1,299). Face-to-face interviews were conducted with caregivers of PLWD by trained staff between 2018 and 2019. Care service needs and utilization by the level of cognitive impairment were summarized. The average age of PLWD was 80.7 years. 76% of participants had severe cognitive impairment. The needs-based ECSP with 30 service items would be sufficient in supporting care services of PLWD in China, of which seven items are core care. The selection plan for ECSP at different levels is designed as “General Care Services + Selective Care Services”, in which service items for low-, mid-and high-level care for PLWD are 7+3, 7+6, and 7+10, respectively. The findings provide the first large-scale data on service needs and utilization of PLWD in mainland China. The ECSP for PLWD advanced in the paper was a practicable and effective quantitative management means. It is deserved to application in a large scale.

This work explores five falling film evaporator (FFE) simulation approaches combined with energy consumption minimization strategies, namely Mechanical Vapor Recompression and Thermal Vapor Recompression (MVR and TVR respectively). Global system analysis and advanced dynamic optimization strategies are then investigated to minimize steam consumption, the cost of steam, the total annualized cost and maximizing product yield. The results indicate that a higher TVR discharge pressure, or MVR compression ratios, along with higher feed temperatures enhance evaporation with but increase operational costs. The most economic option includes three evaporator effects with TVR for achieving 50% product dry mass content. However, for a 35% dry mass content, MVR becomes cost-effective with an 11% reduction in unit electricity prices or a simultaneous 7% drop in electricity prices and a 5% increase in gas-based steam prices. Furthermore, switching from milk powder production to milk concentrates, an annual cost reduction ranging from 10.75 to 44% is achieved. Also, a forecasted 20% (or more) reduction of biomass-based steam cost can lead to lower annual expenditure comparing with the nominal NG-based steam Case. Regarding the total annualized cost, for a new plant design, optimization strategies lead to a 9 - 45% reduction in the total cost depending on the Case under consideration.

Myocarditis has been recognized as a possible rare complication of COVID-19 mRNA vaccination. It concerns between one and five vaccinated people per 100,000 in the general population, with increased incidence in adolescent and young adult men. Most often, cases of myocarditis have been reported in the days following the second dose of vaccine mainly in younger male patients. This rare complication of vaccination usually resolves within days or weeks. However, the pathophysiological events responsible for the increase in frequency of myocarditis after COVID-19 vaccination remain unclear. Several recent reports have highlighted that free spike proteins circulating in the blood of patients at high levels appear to play a major role in myocarditis. Here, we review the most relevant data that partly lift the veil on the molecular mechanisms of the induction of myocarditis following mRNA-based COVID-19 vaccination. We hypothesize that a mechanism of molecular mimicry of the viral spike triggers transient dysregulation of angiotensin-converting enzyme 2, leading to increased soluble angiotensin II binding to the transmembrane receptor angiotensin II type I receptor, similar to what is observed during SARS-CoV-2 infection. We suggest to standardize management of suspected cases of mRNA-based COVID-19 vaccine-induced myocarditis, including angiotensin II and spike antigenemia monitoring.

Abstract: Pericyte(s) (Pcs) and resident perivascular macrophages (rPVMΦs) are positioned perfectly in the neurovascular unit (NVU) and perivascular spaces to facilitate metainflammation that results in brain endothelial cell activation and dysfunction and neuroinflammation. Their positions within the NVU allow intimate contact with one another as follows: Brain endothelial cells (BECs) and the Pcs via their shared basement membrane and physical contact peg-socket junctions with N cadherins and gap junctions Cx43; Pcs and rPVMΦs intimate contacts. Additionally, rPVMΦs have intimate contact with the astrocyte endfeet (ACef) that form the outermost membrane of perivascular spaces. Importantly, ACef have intimate contact with BECs that have intimate physical contact with neuronal axons and dendrites to complete NVU coupling. The multiplicity of intimate contacts of NVU cells allow for continuous crosstalk communications to provide brain homeostasis. While each of the cells of the NVU play important roles in the development of enlarged perivascular spaces (EPVS), this review focuses on the Pcs and rPVMΦs and discusses each of the intimate contacts and their functional significance in detail with numerous illustrations and transition electron microscopic images to demonstrate their role in the development of EPVS. EPVS are known to be biomarkers for cerebral small vessel disease and impaired glymphatic system waste clearance. Additionally, EPVS associate with mixed dementias of vascular dementia, vascular contributions to cognitive impairment and dementia, and Alzheimer’s disease that results in high economic and psychosocial cost to the global community.

Background: IBD is a spectrum of pathologies characterized by dysregulated immune activation leading to uncontrolled response against intestinal, thus resulting in chronic gut inflammation and tissue damage. Due to its complexity, the molecular mechanisms responsible for disease onset and progression are still elusive, thus requiring intense research effort. In this context, the development of models recapitulating the etiopathology of IBD is critical.Methods: Colon from C57BL/6 or BALB/c mice were cultivated in a gut-ex-vivo system (GEVS), exposed 5h to DNBS 1,5 or 2,5 mg/ml, and the main hallmarkers of IBD were evaluated.Results: Gene expression analysis revealed a DNBS-induced: i) compromised Tight junction organization, responsible for tissue permeability dysregulation; induction of ER stress, and iii) tissue inflammation in colon of C57BL/6 mice. Moreover, the concomitant DNBS-induced apoptosis and ferroptosis pathways was evident in colon from both BALB/c and C57BL/6 mice.Conclusions: Overall, we have provided results demonstrating that GEVS is a consistent, reliable, and cost-effective system for modeling DNBS-induced IBD, useful for studying the onset and progression of human disease at the molecular level, while also reducing animal suffering.

Apathy in patients with Parkinson's disease (PD) is one of the least studied manifestations of a wide range of neuropsychiatric disorders (PND). The frequency of apathy in PD, according to various researchers, is 17–80%. Structural and neurochemical changes associated with PD are considered as the leading pathophysiological factors of apathy. Possible general pathophysiological mechanisms of apathy and hypokinesia, depression, executive (frontal) cognitive functions, sleep disorders in PD are discussed. The basis of the pathophysiological commonality of apathy, hypokinesia, and executive functions is probably bilateral disturbances in the functional connections of the stratum and dorsolateral, medial parts of the prefrontal cortex. The combination of apathy and depression in PD may be due to dysfunction of the structures of the limbic system and the medial orbital prefrontal cortex responsible for motivational determined behavior. The variability of the relationship between apathy and hypokinesia, depression, cognitive impairments, sleep disorders at different stages of PD may be associated with the phenomenological heterogeneity of apathy.Apathy worsens the quality of life, makes a significant contribution to disorders of both household and social adaptation of PD patients. It is promising to study the possibility of correcting apathy using dopaminergic therapy. Against the background of the appointment of pramipexole (1.5-3.0 mg / day) to 20 patients with PD (middle stage, according to Hen-Yar-2.5) to correct movement disorders after 4-6 weeks of therapy, a statistically significant positive dynamics of apathy were noted. According to the total assessment of the scale of apathy SE Starkstein (AS). As a result of the study, no statistically significant correlation was found between the dynamics of the total apathy indicator and the dynamics of motor symptoms of PD according to the unified rating scale for assessing PD (“Motor functions”), which, we believe, indicates an independent effect of pramipexole therapy in relation to motivational disorders.

The treatment of critically ill patients remains an evolving and controversial issue. Mechanical circulatory support through a percutaneous approach for the management of cardiogenic shock has taken place in recent years. The combined use of IABP and Impella 2.5 device may have a role to play for this group of patients. A simulation approach may help with a quantitative as-sessment of the achievable degree of assistance. In this paper, we analyse the interaction be-tween Impella 2.5 pump applied as LVAD and IABP using the numerical simulator of the cardi-ovascular system developed in our laboratory. Starting with pathological conditions reproduced using literature data, the simulations were performed setting different rotational speeds for the LVAD and driving the IABP in full mode (1:1) or partial mode (1:2, 1:3 and 1:4). The effects in-duced by drug administration during the assistance were simulated too. The haemodynamic pa-rameters under investigation were: mean aortic pressure, systolic and diastolic aortic pressure, mean pulmonary arterial pressure, mean left and right atrial pressure, cardiac output, cardiac index, left and right ventricular end-systolic volume, left ventricular end-diastolic volume and mean coronary blood flow. The energetic variables considered in the study were: left and right ventricular external work and left and right atrial pressure-volume area. The outcome of our simulations shows that the combined use of IABP and Impella 2.5 achieves adequate support in the acute phase of cardiogenic shock compared to each standalone device. This would allow fur-ther stabilisation and transfer to a transplant centre should escalation of treatment be required.

Agriculture is the backbone of Kenya’s economy, supporting up to 80% of the rural livelihoods. Kenya’s export horticulture is currently the leading Agriculture subsector in Kenya has evolved from small-holder farming to agro-industrial large-scale export farming dominated by multinational companies. It is regarded as an agro-industrial food system based on the economies of scale producing for mass markets outside of the production area. Much of the food consumed from this food system has undergone multiple transformations and been subject to a host of formal and informal insitutions (rules, regulations, standards, norms and values). An Anthropological study of export horticulture in Northwest Mount Kenya was carried out utilizing qualitative data collection methods in Northwest Mount Kenya region. Data was coded and analysed thematically based on grounded theory approach. The study described the institutional settings of export horticulture from an emic perspective as changing and defining the operations of the food system access and management of common pool resources, namely water and land. With the agro-industrial food system competing for these scarce resources in a semi-arid zone, there is potential for conflict and also reduced production and overall benefits to the different actors in the study area.

This paper presents a two-stage hierarchical neural network using image classification and object detection algorithms as key building blocks for a system that automatically detects a potential design right infringement. This neural network is trained to return the Top-N original design right records that highly resemble the input image of a counterfeit. Design rights specify the unique aesthetic characteristics of a product. Due to the rapid change of trends, new design rights are continuously generated. This work proposes an Ensemble Neural Network (ENN), an artificial neural network model that aims to deal with a large amount of counterfeit data and design right records that are frequently added and deleted. At first, we performed image classification and objection detection learning per design right using the existing models with a proven track record of high accuracy. The distributed models form the backbone of the ENN and yield intermediate results aggregated at a master neural network. This master neural network is a deep residual network paired with a fully connected network. This ensemble layer is trained to determine the sub-models that return the best result for a given input image of a product. In the final stage, the ENN model multiples the inferred similarity coefficients to the weighted input vectors produced by the individual sub-models to assess the similarity between the test input image and the existing product design rights to see any sign of violation. Given 84 design rights and the sample product images taken meticulously under various conditions, our ENN model achieved average Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively. Upon introducing new design rights data, a partial update of the inference model was done an order of magnitude faster than the single model. ENN maintained a high level of accuracy as it scaled out to handle more design rights. Therefore, the ENN model is expected to offer practical help to the inspectors in the field, such as the customs at the border that deal with a swarm of products.

Internet of Things (IoT) technologies can greatly benefit from machine learning techniques and Artificial Neural Networks for data mining and vice versa. In the agricultural field, this convergence could result in the development of smart farming systems suitable for use as decision support systems by peasant farmers. This work presents the design of a smart farming system for crop production, which is based on low-cost IoT sensors and popular data storage services and data analytics services on the Cloud. Moreover, a new data mining method exploiting climate data along with crop production data is proposed for the prediction of production volume from heterogeneous data sources. This method was initially validated using traditional machine learning techniques and open historical data of the northeast region of the state of Puebla, Mexico, which were collected from data sources from the National Water Commission and the Agri-food Information Service of the Mexican Government.

Rapid Visual Screening (RVS) is a procedure that estimates structural scores for buildings and prioritize their retrofit and upgrade requirements. Despite the speed and simplicity of RVS, many of the collected parameters are non-commensurable and include subjectivity due to visual observations. It might cause uncertainties in the evaluation, which emphasizes the use of a fuzzy-based method. This study aims to propose a novel RVS methodology based on the interval type-2 fuzzy logic system (IT2FLS) to set the priority of vulnerable building to undergo detailed assessment while covering uncertainties and minimizing their effects during evaluation. The proposed method estimates the vulnerability of a building, in terms of Visual Damage Index, considering the number of stories, age of building, plan irregularity, vertical irregularity, building quality, and peak ground velocity, as inputs with a single output variable. Applicability of the proposed method has been investigated using a post-earthquake damage database of 28 reinforced concrete buildings from the Bingöl earthquake in Turkey.

Recently, solar energy is growing as a power source for potential alternative to meet the global demand. Unlike other energy sources such as coal, nuclear, gas and oil, their prices are not only stable, they prevent the harmful side-effects on the environment, being one of the best sources of clean energy (solar energy). This article presents an analysis of the transformation of the static system for the treatment of solar energy using photovoltaic modules. It is designed to generate energy for future generations to be more useful from different parts of the photovoltaic energy conversion system, such as a DC-DC converter, current inverter, maximum power tracking algorithm (MPPT), filter, the stability of a system, etc. The above result will be useful in the improvement of efficiency in photovoltaics structures.

The VisionCube is a 2-unit CubeSat developed in-house, of which the primary mission is aimed at detecting the occurrence of transient luminous events in the upper atmosphere and obtaining corresponding images from the low Earth orbit. An on-board TLE observation system of the VisionCube cubesat is designed and developed by incorporating a photon-sensitive MaPMT and a CMOS image sensor. Also, a distinctive TLE observation software which enables detecting the TLEs and capturing images in a timely manner is devised. By taking into account the limited resources of a small CubeSat in size and power, the on-board observation system is developed employing a system-on-chip design architecture by which both hardware and software can be integrated seamlessly. The purpose of this study is to investigate the functionality of the hardware and the validity of the software algorithm to show the on-board system will operate properly with no human intervention during the space operation. To this end, a ground simulation facility is constructed to emulate the space environment of the TLE occurrence using a set of UV LEDs inside a darkbox. Based on the analysis of the spectral and temporal properties of the TLEs, the randomly generated UV LED pulses are opted for verification scenarios for the TLE observation system. The validation results show that the hardware and the software algorithm of the on-board observation system can effectively detect the TLEs and obtain the images during the on-orbit operation.

By conjugating a radioantigen (o-quinone) with a protein - Esherichia coli endotoxin, a stable polyantigen complex-protein-quinoid radioantigen (BChA) - o-quinone imine (C6H10O-NH - protein) was obtained, including haptenic (o-quinone) and protein part of the antigenic complex (Esherichia coli endotoxin). Using the above components (hyperimmune antiradiotoxic serum, ethanol extract of the Vita-Force apiphitopreparation, metabolic products of Bifidobacterium bifidum, Bacillus subtilis and a highly dispersed fraction of bentonite), a polyfunctional composition «Polyapisogen» was compiled, including polyglobulins (500 cm3 per 1 L),500 cm3 of 4% ethanol extract of the Vita-Force apiphitopreparation, in which a highly dispersed fraction of bentonite (20 g), metabolism products (2 g powder) and Bacillus subtilis metabolism products (4 g) are dissolved. The polyfunctional preparation obtained by the above-described technology is used as an activator of the immune system against the background of its imbalance induced by agents of a radiogenic nature. It was found that a single subcutaneous injection of the drug at a dose of 25 mg / kg of live weight (dry matter basis) had a radioprotective effect increasing the survival rate of affected animals upto 75%. The studies carried out can serve as a basis for the creation of polyfunctional radioprotective, immunoprotective and bioprotective agents not only for isolated, but also for combined lesions of the body by agents of radiogenic and non-radiogenic nature using polyglobulins, convalescent serums, bee products, metabolic products of probiotic microorganisms and mineral sorbents.

In the paper the experimental methodology for the single-crystal circular plate deformation measurement and subsequent procedure for mechanical properties quantitation are developed. The procedure is based on a new numerical-analytical solution of nonlinear boundary-value problem for finite deformations of a circular anisotropic plate. Using the developed method, a study of the deformation of single-crystal circular plates formed on the basis of a silicon-on-insulator structure was carried out. The values of residual stresses are determined and it is shown that the presence of these stresses increases the flexural rigidity of the plate in several times.

In this paper, we concentrate on the study of the properties of residual Tsallis entropy for order statistics. Order statistics have an important role in reliability structural engineering for example for modelling lifetimes of series and parallel systems. The residual Tsallis entropy of ith order statistic from a continuous distribution function and its deviation from the residual Tsallis entropy of ith order statistics from a uniform distribution is investigated. In a mathematical framework, a method to express the residual Tsallis entropy of the ith order statistic from a continuous distribution in terms of the residual Tsallis entropy of the ith order statistic from a uniform distribution is provided. This approach may provide insight into the behavior and properties of the residual Tsallis entropy for order statistics. Further, we study the monotonicity properties of the residual Tsallis entropy of order statistics under dierent conditions. By studying these properties, deeper understanding of the relationship between the position of order statistics and the resulting residual Tsallis entropy is gained.

The investigation of the spatiotemporal propagation characteristics of the "meteorological-agricultural-hydrological" drought system, under diverse climatic conditions, is crucial for the development of a robust drought warning system and the effective implementation of proactive drought prevention and resilience strategies. To achieve this, the current study utilizes the Soil and Water Assessment Tool (SWAT) model to simulate key components of the watershed water cycle, such as evaporation, surface water, soil water, and groundwater. Specifically, the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Soil Moisture Index (SSMI), and the Nonlinear Joint Hydrological Drought Index (NJHDI) are employed to characterize meteorological drought, agricultural drought, and hydrological drought, respectively. By analyzing the correlation between these types of drought, the propagation characteristics of the "meteorological-agricultural-hydrological" drought system are elucidated using the rigorous strongest correlation coefficient method. The Yellow River Basin (YRB) is chosen as the case study for this research. Results showed that (1) The propagation time from meteorological to agricultural drought exhibited distinct seasonal characteristics, with durations of 5-6 months in spring, 2-3 months in summer, 3-5 months in autumn, and 6-8 months in winter. Compared to 1961-1990, the propagation time increased in spring and summer but decreased in autumn and winter during 1991-2010 for most YRB regions. (2) The agricultural to hydrological drought propagation showed no clear seasonal differences but increased over time. Specifically, zone C (arid/semi-arid with moderate temperatures) had shorter propagation time of 1-5 months, while zones B (transitional plateau to mid-latitude) and E (semi-arid/semi-humid temperate continental climate) experienced longer propagation time of 7-12 months. (3) Despite the extended timescales, agricultural-hydrological drought correlation was weaker than meteorological-agricultural linkage. This is because meteorological deficits directly reduce soil moisture, rapidly inducing agricultural drought. However, groundwater sustaining baseflow during agricultural drought delays streamflow deficits, prolonging the agricultural-hydrological propagation time.

As a precious natural resource, forests are being destroyed. In previous studies, there is a lack of interactive assessment of its distribution that comprehensively considers multiple external disturbances. This paper takes the Barcelona Metropolitan Region as an example. Based on remote sensing, it analyzes the development process of the forest from 2006 to 2018 through multiple landscape indicators, and OLS models were established to analyze variables that have direct and indirect effects on forest distribution. In addition, the ecological structure of the forest was analyzed based on NDVI. It was found that the forest area is the largest but has been decreasing, becoming more complex in distribution structure. Much of the forest was converted to agricultural land and grassland. The green quality of forests was increasing, and the broad-leaved forest, the second largest area, contributes the most. NDVI is the most important positively correlated variable, and daytime surface temperature is an important inverse factor related to NDVI. In addition, NDBI is also a negative condition that inhibits forest development. In conclusion: The BMR forest area is decreasing and becoming more fragmented. NDVI and daytime LST are the two most significant factors. Climate warming may lead to worse forest development.

Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this paper, we adopt a formal approach to analyze the security of the communication mechanism of ROS2. First, we use a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduce the CIA model on the basis of the established vulnerability model and use LTL to define its security properties. Then, we design and implement a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally test some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.

The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from time series data. This paper proposes the combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves respectively an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems.

We review the literature on medical discourse and attempt to build a computational model of it. Medical discourse sheds a light on communication structure of patient-doctor and other communication scenarios in healthcare and should be leveraged to facilitate and automate this communication when it is possible and practical. We propose a unified framework to represent communication discourse at the meta-level, where the subject of the communication is expressed in a language object. So far, the broad range of work on medical discourse is detached from computational discourse analysis, and we explore the possibilities of filling this gap and computationally treat the peculiarities of how information is passed between the agents in a hospital setting. We select the domain of question answering (QA) against a corpus of medical documents of diverse nature to evaluate our computational model of medical discourse. It turns out that applying specific structures obtained in medical discourse studies improves the relevance and efficiency of question answering.

This paper introduces a fuzzy logic-based autonomous ship-deck landing system for fixed-wing Unmanned Aerial Vehicles (UAVs). The ship is assumed to maintain a constant course and speed. The aim of this fuzzy logic landing model is to simplify the task of landing UAVs on moving ships in challenging maritime conditions, relieving operators from this demanding task. The designed UAV ship-deck landing model is based on a fuzzy system comprised of three interconnected subsystems. Each subsystem consists of three inputs and one output incorporating various fuzzy rules to account for external factors during ship-deck landings. Specifically, the Fuzzy Logic System (FLS) takes inputs such as the airspeed, the relative wind direction and speed, the range from the deck and the UAV's flight altitude. The FLS outputs provide data on the Speed of the UAV relative to the ship’s velocity, the Bank Angle, and the Angle of Descent of the UAV. The performance of the designed intelligent ship-deck landing system is evaluated using the standard configuration of MATLAB Fuzzy Toolbox.

Transforming Small-Scale LNG Business, Enhancing Efficiency and Resilience in an Evolving Supply Chain In various industries, innovative systems have proven their value due to their efficiency and adaptability. A prime example of this lies in the rapid growth of the liquefied natural gas (LNG) market. LNG, a fuel derived from natural gas, has gained significant traction in recent years. In light of this, we aim to explore optimal strategies for small-scale LNG businesses, with a focus on revolutionizing their supply chain to ensure adaptability and sustainability. Our objective is to enhance efficiency and sturdiness in this evolving sector. By devising innovative methods and implementing cutting-edge technologies, we aim to revolutionize the small-scale LNG market, paving the way for a more optimized and resilient supply chain. Through this transformation, we strive to create a sustainable and adaptable framework that will drive the growth and success of a small-scale LNG businesses.LNG' ability to be transported and stored more cost-effectively in its reduced volume compared to natural gas in its original state is a significant advantage. The distribution network for LNG starts at numerous supply terminals and extends to a diverse range of consumption centers, each with varying tank capacities. In the broader economic context, the establishment of efficient routes for LNG Truck tank plays a critical role.To tackle the challenge of selecting a pickup location and determining optimal destinations with specific unloading volumes for liquefied natural gas (LNG), this research presents an innovative solution utilizing state-of-the-art machine learning techniques. The proposed approach combines the power of reinforcement learning, recurrent neural networks, online learning, and graph theory. By leveraging these cutting-edge techniques, we aim to develop an intelligent system that can effectively determine the most efficient pickup location and sequence of destinations for LNG delivery. This system will take into account various factors such as transportation costs tank capacity, and demand fluctuations. Through the integration of reinforcement learning, the system will continuously learn and improve its decision-making process in real-time. The recurrent neural networks will allow for the analysis of historical aim to model and optimize the complex network of pickup and delivery routes, considering factors such as distance, capacity constraints, and operational efficiencies. With this innovative approach, we strive to revolutionize the selection and planning process for LNG pickup and delivery, ultimately enhancing efficiency and reducing cost within the LNG supply chain.Improving the distribution network offers several financial benefits, including cost reduction, faster delivery times, more affordable distribution, and enhanced efficiency in the utilization of tanker trucks for LNG transportation. By optimizing the distribution network, transit costs associated with LNG transportation can be significantly lowered. This can be achieved through intelligent route planning, taking into account factors such as distance, traffic conditions, and fuel consumption. Minimizing transit costs contributes to overall savings for businesses operating in the LNG market. Faster delivery times are another advantage of an enhanced distribution network. By designing efficient routes and eliminating unnecessary detours or delays, the time taken for LNG to reach its intended destinations can be reduced. This not only improves customer satisfaction but also allows for more frequent deliveries and better coordination of supply and demand. Cheaper distribution is a direct outcome of an optimized distribution network. By strategically planning pickup locations, destinations, and intermediate stops, the costs associated with transferring LNG between terminals and consumption centres can be minimized. This leads to savings in transportation expenses, ultimately benefiting both businesses and end-users. Efficient utilization of tanker trucks is crucial for maximizing profitability in the LNG industry. An improved distribution network enables better allocation of resources, ensuring that tanker trucks are utilized optimally. This includes reducing empty or underutilized trips, maximizing load capacities, and minimizing idle time. Such efficiency improvements translate to cost savings and increased revenue for businesses involved in LNG transportation. Overall, the financial gains from improving the distribution network are substantial. Lower transit costs, faster delivery times, cheaper distribution, and more efficient use of tanker trucks contribute to increased profitability, competitiveness, and sustainability in the LNG market.

This research describes a detailed analysis in the enactment of a Lean Tool, the Last Planner System during pandemic period which helps in optimizing the resources for better coordination among all stakeholders in pandemic period. LPS as it is known, focusses on minimizing the factors such as uncertainties, barriers and variability to make construction projects more flexible for better project management. These include variations and deviations, supervision, delays in approvals, change resistance, subcontractor dedication which are all related to various stakeholders in the project. Following that, a Design Science Research technique is used to evaluate the effect of applying LPS in buildings. An action strategy is being used to attain this goal, and four case studies were being documented which were concerned in the implementation of LPS in the building of the Boy's Hostel, Research Scholar Quarters, Faculty Housing, and Girls' Hostel at Chennai, Tamilnadu, India. Data was gathered by observation of site activities, interviews, documentation analysis, and a questionnaire survey and grouped into various factors. While adopting the LPS tool in the research the factors affecting the implementation were found in the covid-19 pandemic period. Further these factors were anlayzed, measured, ranked and validated for adopting in projects.

In the struggle for survival, all organisms have evolved adaptations for protection from predation. Angiosperms are among the most abundant producers on the planet, but they are trapped in a precarious symbiosis with animals for pollination and fruit dispersal, while also having to deter herbivory. About 10% of all angiosperms produce the deterrent latex from ubiquitous isoprene precursors. Latex is an aqueous suspension of rubber particles and other compounds which works as an antifeedant by clogging chewing mouthparts and by coagulating into a solid in stomach acid. A survey of stomach pHs from various animals supports the hypothesis that foregut-fermenting ruminants have evolved the capacity to evade latex coagulation due to the presence of anterior digestive chambers with near-neutral to alkaline pH before it enters the acidic abomasum. Hindgut-fermenting animals lack this adaptation and have to eat forage which is low in latex, such as grasses. The latex-adaptive foregut browsers vs. latex-maladapted hindgut grazer hypothesis has implications for both livestock and zoo animals which may be provided with forage that is not optimal for their gut health. Further, ruminants and herbivorous tadpoles are at risk for damage by popular bioinsecticides such as Bt which are activated by alkaline gut pH.

Asthma is a chronic respiratory disease that impairs breathing. Management of asthma presents a significant challenge due to its inherent variability; that is, its symptoms can substantially differ among individuals, thereby complicating the prediction and management of exacerbations. Furthermore, individuals with asthma often have unique triggers that precipitate symptoms or attacks. The identification of these triggers can often prove to be a challenging, and at times, an impractical attempt. To address this, our research proposes a practical, personalized alert system, predicated on individual lung function tests conducted under varying environmental conditions classified by air-quality sensors. To validate this concept, we conducted an observational pilot study involving healthy individuals. We recruited twelve healthy participants and monitored their responses across a broad spectrum of environments, characterized by varying air quality, temperature, and humidity conditions. The lung function for each participant, assessed using peak expiratory flow (PEF) values, was recorded in each of these environments. Our results highlighted substantial variability in pulmonary responses to different environments. Utilizing these insights, we proposed a personalized alarm system that provides real-time air-quality monitoring and issues alerts when environmental conditions may potentially become unfavorable. We also explored the feasibility of employing basic machine learning techniques to predict PEF values in the aforementioned environmental conditions. This proposed system has the potential to empower individuals in actively safeguarding their respiratory health and mitigating discomfort caused by environmental conditions, especially in cases of asthma patients. By enabling timely and personalized interventions, the system aims to provide individuals with the necessary tools to minimize exposure to asthma’s possible triggers.

The goal of this paper is the performance evaluation of a deep learning approach when deployed in fifth-generation (5G) millimeter wave (mmWave) multicellular networks. To this end, the optimum beamforming configuration is defined by two neural networks (NNs) that are properly trained, according to mean square error (MSE) minimization. The first network has as input the requested spectral efficiency (SE) per active sector, while the second network the corresponding energy efficiency (EE). Hence, channel and power variations can now be taken into consideration during adaptive beamforming. The performance of the proposed approach is evaluated with the help of a developed system level simulator via extensive Monte Carlo simulations. According to the presented results, machine learning (ML)-adaptive beamforming can significantly improve EE compared to the standard non-ML framework. Although this improvement comes at the cost of increased blocking probability (BP) and radiating elements (REs) for high data rate services, the corresponding increase ratios are significantly reduced compared to the EE improvement ratio. In particular, considering 21.6 Mbps per active user and ML adaptive beamforming, then EE can reach up to 5.3 Mbps/W, which is significantly improved compared to the non-ML case (0.9 Mbps/W). In this context, BP does not exceed 2.6%, which is slightly worse compared to 1.7% in the standard non-ML case. Moreover, approximately 20% additional REs are required, with respect to the non-ML framework.

The high-speed railway operation environment monitoring network system has a wide variety of sensors and inconsistent business requirements, which have certain requirements for data transmission delay and network life. Most of the previous research focuses only on prolonging network lifetime or reducing data transmission delays when designing or optimizing routing protocols, without co-designing the two. In addition, due to the harsh operating environment of high-speed railways, when the network changes dynamically, the traditional routing algorithm will generate unnecessary redesigns and lead to high overhead. Based on the actual needs of high-speed railway operation environment monitoring, this paper proposes a novel Double Q-values adaptive model combined with the existing reinforcement learning method, which considers the energy balance of the network and real-time data transmission, and constructs energy saving and delay. The two-dimensional reward avoids the extra overhead of maintaining a global routing table while capturing network dynamics. In addition, the adaptive weight coefficient is used to ensure the adaptability of the model to each business of the high-speed railway operation environment monitoring system. Finally, simulations and performance evaluations are carried out and compared with previous studies.Finally, simulations and performance evaluations are carried out and compared with previous studies. The results show that the proposed routing algorithm successfully extends the network’s life cycle and gets good real-time data performance. It also saves energy and has fewer delays than the other three routing protocols in different situations.

The lactoperoxidase (LPO) system shows promise in the prevention of dental caries, a common chronic disease. This system has antimicrobial properties and is part of the non-specific antimicrobial immune system. Understanding the efficacy of the LPO system in the fight against biofilms could provide information on alternative strategies for the prevention and treatment of caries. In this study, the enzymatic system was modified using four different (pseudo)halide substrates (thiocyanate, thiocyanate-iodide mixture, selenocyanate, and iodide). The study evaluated the effects of such modifications on the ability of Streptococcus mutans to form a biofilm, the synthesis of insoluble polysaccharides, the synthesis of lactate, the consumption of glucose and sucrose, the concentrations of intracellular NAD+ and NADH and the efficiency of transmembrane glucose transport by phosphoenolpuryvate PEP-carbohydrate phosphotransferase (PTS). The results showed that the LPO-iodide system had the strongest inhibitory effect on biofilm growth and lactate synthesis (complete inhibition). This was associated with an increase in the NAD+/NADH ratio and an inhibition of glucose PTS activity. The LPO-selenocyanate system showed a moderate inhibitory effect on biofilm biomass growth and lactate synthesis. The other systems showed relatively small inhibition of lactate synthesis and glucose PTS but no effect on the growth of biofilm biomass. This study provides a basis for further research on the use of alternative substrates of the LPO system, particularly the LPO-iodide system, in the prevention and control of biofilm-related diseases.

Drowsiness detection is an important task in road safety and other areas that require sustained attention. In this article, an approach to detect drowsiness in drivers is presented, focusing on the eye region, since eye fatigue is one of the first symptoms of drowsiness. The method used for the extraction of the eye region is Mediapipe, chosen for its high accuracy and robustness. Three neural networks were analyzed based on: InceptionV3, VGG16 and ResNet50V2 that implement deep learning. The database used is NITYMED, which contains videos of drivers with different levels of drowsiness. The three networks were evaluated in terms of accuracy, precision and recall in detecting drowsiness in the eye region. The results of the study show that all three convolutional neural networks have high accuracy in detecting drowsiness in the eye region. In particular, the Resnet50V2 network achieved the highest accuracy, with a rate of 99.71% on average. For better visualization of the data, the Grad-CAM technique is used, with which we get a better understanding of the performance of the algorithms in the classification process.

Industrial process control systems commonly exhibit features of time-varying, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still a challenging task. In this paper, data-driven modeling techniques and deep learning methods are used to accurately capture a category of smooth nonlinear system’s spatiotemporal features. The operating point of these systems may change over time, and their nonlinear characteristics can be locally linearized. We established the LSTM-CNN-ARX model by utilizing a fusion of long short-term memory (LSTM) network and convolutional neural network (CNN) to fit the coefficients of the state-dependent exogenous variable autoregressive (SD-ARX) model. Compared to other models, the hybrid LSTM-CNN-ARX model is more effective in capturing the nonlinear system’s spatiotemporal characteristics due to its incorporating the strengths of LSTM for learning temporal characteristics and CNN for capturing spatial characteristics. The model-based predictive control (MPC) strategy, namely LSTM-CNN-ARX-MPC, is developed by utilizing the model's local linear and global nonlinear features. The control comparison experiments conducted on a water tank system (WTS) show the effectiveness of the developed models and MPC methods.

Pursuing "intelligent justice" necessitates an impartial, productive, and technologically driven methodology for judicial determinations. This scholarly composition proposes a framework that harnesses Artificial Intelligence (AI) innovations such as Natural Language Processing (NLP), ChatGPT, ontological alignment, and the semantic web, in conjunction with blockchain and privacy techniques, to examine, deduce, and proffer recommendations for the administration of justice. Specifically, through the integration of blockchain technology, the system affords a secure and transparent infrastructure for the management of legal documentation and transactions while preserving data confidentiality. Privacy approaches, including differential privacy and homomorphic encryption techniques, are further employed to safeguard sensitive data and uphold discretion. The advantages of the suggested framework encompass heightened efficiency and expediency, diminished error propensity, a more uniform approach to judicial determinations, and augmented security and privacy. Additionally, by utilizing explainable AI methodologies, the ethical and legal ramifications of deploying intelligent algorithms and blockchain technologies within the legal domain are scrupulously contemplated, ensuring a secure, efficient, and transparent justice system that concurrently protects sensitive information upholds privacy.