Temperature dependent entropic force acting between the straight direct current I and the linear system (string with length of L) of N elementary non-interacting magnets/spins μ is reported. The system of elementary magnets is supposed to be in the thermal equilibrium with the infinite thermal bath T. The entropic force at large distance from the current is given by Fmagnen=Nμ02μ2I24π2kBTr3 , where r is the distance between the edge of the string and the current I, and kB is the Boltzmann constant; (r≫L is adopted). The entropic magnetic force is the repulsion force. The entropic magnetic force scales as Fmagnen~1T , which is unusual for entropic forces. The effect of “entropic pressure” is predicted for the situation when the source of the magnetic field is embedded into the continuous media, comprising elementary magnets/spins.

COVID-19 is now a major global health crisis, can lead to severe food crisis unless proper measures are not taken. Though a number of scientific studies have addressed the possible impacts of COVID-19 in Bangladesh on variety of issues, problems and food crises associated with aquatic resources and communities are missing. Therefore, this study aimed at bridging the gap in the existing situation and challenges of COVID-19 by linking its impact on aquatic food sector and small-scale fisheries with dependent population. The study was conducted based on secondary data analysis and primary fieldwork. Secondary data focused on COVID-19 overview and number of confirmed, recovered and death cases in Bangladesh; at the same time its connection with small-scale fisheries, aquatic food production, demand and supply was analyzed. Community perceptions were elicited to present how the changes felt and how they affected aquatic food system and small-scale fisheries and found devastating impact. Sudden illness, reduced income, complication to start production and input collection, labor crisis, transportation abstraction, complexity in food supply, weak value chain, low consumer demand, rising commodity prices, creditor’s pressure were identified as the primary affecting drivers. Dependent people felt the measures taken by the Government should be based on protecting the health and food security, although it could be detrimental to economic growth in the short term. The study provides insight into policies adopted by the policy makers to mitigate the effects of the pandemic on aquatic food sector and small-scale fisheries.

Cyanobacterial blooms occur with increasing frequency in freshwater ecosystems, posing a hazard to human and environmental health. Exposure of human to cyanobacterial metabolites occurs mostly via accidental ingestion through contaminated drinking water or during recreational activities and, most frequently, results in gastrointestinal symptoms. Despite the clinical manifestation, cyanobacterial metabolites are rather investigated for their toxicity towards specific organs or tissues, especially hepato-, nephro- and neurotoxicity, then for effects on the gastrointestinal tract and the associated lymphoid tissue. The aim of this review was to systematically summarize available literature on the effects on the gastrointestinal tract and the mucosal innate immune system and compile the data from both, in vitro and in vivo studies, focusing on human-health relevant models. Our systematic literature review revealed significant data gaps in the understanding on metabolites breaching the gastrointestinal barrier and the role of the immune system in the establishment of clinical symptoms. Microcystins and cylindrospermopsin were linked to gastrointestinal symptoms, immune system effects or both. Furthermore, implications for cyanobacterial bloom lipopolysaccharides in gastrointestinal inflammation were reported in several cases, while other metabolites received only minor attention. The collected data indicate the need for a reassessment of potential enterotoxicity of microcystins and cylindrospermopsin. Additionally, the carcinogenic potential of cyanotoxins, especially microcystins, has to be clarified, as an increasing amount of epidemiological studies show correlations between cyanobacterial blooms and gastrointestinal cancer incidence. Furthermore, other, often highly abundant bioactive metabolites like aeruginosins, have to be toxicologically evaluated at levels also accounting for (sub-)chronic exposure to low concentrations and in combination with naturally co-occurring metabolites, as can be expected in drinking water supplies. studies, focusing on human-health relevant models. Our systematic literature review revealed significant data gaps in the understanding on metabolites breaching the gastrointestinal barrier and the role of the immune system in the establishment of clinical symptoms. Microcystins and cylindrospermopsin were linked to gastrointestinal symptoms, immune system effects or both. Furthermore, implications for cyanobacterial bloom lipopolysaccharides in gastrointestinal inflammation were reported in several cases, while other metabolites received only minor attention. The collected data indicate the need for a reassessment of potential enterotoxicity of microcystins and cylindrospermopsin. Additionally, the carcinogenic potential of cyanotoxins, especially microcystins, has to be clarified, as an increasing amount of epidemiological studies show correlations between cyanobacterial blooms and gastrointestinal cancer incidence. Furthermore, other, often highly abundant bioactive metabolites like aeruginosins, have to be toxicologically evaluated at levels also accounting for (sub-)chronic exposure to low concentrations and in combination with naturally co-occurring metabolites, as can be expected in drinking water supplies.

Emerging pollutants as pharmaceuticals have been focusing international attention for few decades. Ciprofloxacin (CIP) is a common drug widely found in effluents from hospitals, industrial and different wastewater treatment plants, as well as rivers. In this work, the lab-scale 3D-BER system was established, and more than 90% of the antibiotic CIP removal from the Low C/N wastewater. Best results were obtained with current intensity, and different C/N ratio significantly improve the removal of CIP and nitrates, when the ideal conditions were; C/N = 1.5-3.5, pH =7.0-7.5, and I = 60 mA. The highest removal efficiency of CIP = 94.20 %, NO3--N= 95.53 % and total nitrogen (TN) = 84.27 %, respectively. In this novel system, the autotrophic-heterotrophic denitrifying bacteria played vital role for the removal of CIP and enhanced denitrification process. Thus, autotrophic denitrifying bacteria uses CO2 and H2 as carbon sources to reduce nitrates to N2. This system has the assortment and prosperous community revealed at the current intensity of 60 mA, and the analysis of bacterial community structure in effluent samples fluctuates under different condition of C/N ratios. According to the results of LC-MS/MS analysis, the intermediate products were proposed after efficient biodegradation of CIP. Microbial community on biodegrading was mostly found at phylum, and class level was dominantly responsible for the NO3--N and biodegradation of CIP. This work can provide some new insights towards the biodegradation of CIP and the efficient removal of nitrates from low C/N wastewater treatment by the novel 3D-BER system.

Building on the carbon reduction targets agreed in the Paris Agreements, many nations have renewed their efforts toward achieving carbon neutrality by the year 2050. In line with this ambitious goal, nations are seeking to understand the appropriate combination of technologies which will enable the required reductions in such a way that they are appealing to investors. Around the globe, solar and wind power lead in terms of renewable energy deployment, while carbon capture and storage (CCS) is scaling up toward making a significant contribution to deep carbon cuts. Using Japan as a case study nation, this research proposes a linear optimization modeling approach to identify the potential contributions of renewables and CCS toward maximizing carbon reduction and identifying their economic merits over time. Results identify that the combination of these three technologies could enable a carbon dioxide emission reduction of between 55 and 67 percent in the energy sector by 2050 depending on resilience levels and CCS deployment regimes. Further reductions are likely to emerge with increased carbon pricing over time. The findings provide insights for energy system design, energy policy making and investment in carbon reducing technologies which underpin significant carbon reductions, while identifying potential regional social co-benefits.

This paper studies the trajectory tracking control of an autonomous underwater vehicle (AUV) based on a stochastic uncertain nonlinear system. We investigate the time-varying gain adaptive control method looking for possible approaches to alleviate the heavy computational burden. Under nonlinear growth, conditions satisfy polynomial growth conditions. These two problems are resolved the fast response time and good path tracking, respectively. Novel adaptive algorithms are developed exploiting the dynamic properties of the AUV motion. By appropriately turning the original controller design problems into parameters choosing problems and then solving them in a functional time-varying observer technical theorem. In order to deal with the station error of system converges to arbitrarily small domains with stochastic uncertain disturbance. A coordinate transformation is proposed for all system states to meet boundedness. We show that the convergence of the AUV trajectory errors can be guaranteed by the proposed contraction constraints in the stability analysis, closed-loop stability is proved, and the system is asymptotically probabilistic in the global scope. They are taking advantage of the guaranteed stability. Extensive simulation studies on the AUV model demonstrate the effectiveness and robustness of the proposed approach. A real-time time-varying gain constructive control strategy is further developed for the Hardware-in-the-loop simulation; the controller design results are imported into the AUV actuator model to verify the effectiveness of the controller design.

Angiotensin II (Ang II) plays a role in several inflammatory diseases and is capable of inducing the release of inflammatory mediators in several cell types. The objective was to investigate the potential of Ang II to induce mRNA expression of inflammatory mediators by cultured primary human periodontal cells. A synergistic effect of a co-treatment with Ang II and IL1β on mRNA expression of inflammatory mediators was also explored. Primary cultures of human fibroblast-like cells isolated from gingival and periodontal ligament tissues from 3 subjects were established using explant techniques. Immunophenotyping of STRO-1, Ang II type 1 receptor (AT1R) and Ang II type 2 receptor (AT2R) was performed by flow cytometry analysis. Cell cultures were challenged with Ang II (1M) for 3, 6 and 24h with or without a co-treatment with IL1β (0.1ng/mL) for 24h. mRNA expression of inflammatory mediators was performed by real time quantitative polymerase chain reaction (RT-qPCR). We present, for the first time, a precise quantification for AT1R and AT2R on human gingival and periodontal fibroblast-like cell types; the percentage of positive immunostaining compared to the total population of cells varied from 3.35% through 5.29% for AT1R and 2.97% through 4.57% for AT2R. Ang II slightly upregulated mRNA expression of IL6 and CCL2/MCP1 in gingival cells and IL8 and PTGS2/COX2 in periodontal ligament cells. IL1β induced upregulation of IL8, IL6 CCL2/MCP1, PTGS2/COX2 and IL1β mRNA in both cell types. Co-treatment with Ang II and IL1β did not show a synergistic effect under these experimental conditions. Ang II shows a low potential to induce mRNA upregulation of inflammatory mediators in cultured primary human gingival and periodontal ligament cells, most likely due to the low percentage of Ang II receptors in such cells, and no synergistic effect with the co-treatment with IL1β.

Angiotensin II (Ang II) plays a role in several inflammatory diseases and is capable of inducing the release of inflammatory mediators in several cell types. The objective was to investigate the potential of Ang II to induce mRNA expression of inflammatory mediators by cultured primary human periodontal cells. A synergistic effect of a co-treatment with Ang II and IL1β on mRNA expression of inflammatory mediators was also explored. Primary cultures of human fibroblast-like cells isolated from gingival and periodontal ligament tissues from 3 subjects were established using explant techniques. Immunophenotyping of STRO-1, AT1 and AT2 receptors was performed by flow cytometry analysis. Cell cultures were challenged with Ang II (1M) for 3, 6 and 24h with or without a co-treatment with IL1β (0.1ng/mL) for 24h. mRNA expression of inflammatory mediators was performed by real time quantitative polymerase chain reaction (RT-qPCR). We present, for the first time, a precise quantification for AT1 and AT2 receptors on human gingival and periodontal fibroblast-like cell types; the percentage of positive immunostaining compared to the total population of cells varied from 3.35% through 5.29% for AT1 and 2.97% through 4.57% for AT2. Ang II slightly upregulated mRNA expression of IL6 and CCL2/MCP1 in gingival cells and IL8 and PTGS2/COX2 in periodontal ligament cells. IL1β induced upregulation of IL8, IL6 CCL2/MCP1, PTGS2/COX2 and IL1β mRNA in both cell types. Co-treatment with Ang II and IL1β did not show a synergistic effect under these experimental conditions. Ang II shows a low potential to induce mRNA upregulation of inflammatory mediators in cultured primary human gingival and periodontal ligament cells, most likely due to the low percentage of Ang II receptors in such cells, and no synergistic effect with the co-treatment with IL1β.

Staphylococcus aureus is a Gram-positive bacterium causing a range of mild to life-threatening infections including bone infections such as osteomyelitis. S. aureus is able to invade and persist within non-professional phagocytic cells such as osteoblasts. In the present study four different S. aureus strains, 2SA-ST239-III, 5SA-ST5-II, 10SA-ST228-I, and 14SA-ST22-IVh were tested for their ability to modulate cell viability in MG-63 osteoblast-like cells following a successful invasion and persistence. Methicillin-sensitive S. aureus (MSSA) ATCC-12598-ST30 was used as control. Despite the demonstrated similar abilities of internalization and persistence of ATCC-12598-ST30, 2SA-ST239-III, and 14SA-ST22-IVh strains in MG-63 osteoblast-like cells under our experimental conditions, we demonstrated that the decrease in cell viability was due to the different behavior of the considered strains, with the number of intracellular bacteria playing a limited role. We focused our attention on different cellular biochemical functions related to inflammation, cell metabolism, and oxidative stress during osteoblast infections. We were able to show that: 1) ATCC-12598-ST30 and 2SA-ST239-III were the only two clones able to persist and maintain their number into the cellular hostile environment during the entire period of infection; 2) 2SA-ST239-III was the only clone able to significantly increase the gene expression (3 and 24 h) and protein secretion (24 h) of both interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) in MG-63 osteoblast-like cells; 3) the same clone determined a significant up-regulation of transforming growth factor-β1 (TGF-β1) and the metabolic marker glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNAs at 24 h post infection; 3) neither the MSSA nor the four MRSA strains induced oxidative stress phenomena in MG-63 cells, although a very different expression pattern towards nuclear factor E2-related factor 2 (Nrf2) and its downstream gene heme oxygenase 1 (HO-1) activation was observed among the different clones. Our results can open a new way of considering therapies, going in the direction of an individualized therapeutic strategy that should take into account the difference existing between MSSA and MRSA as well as the distinctive features of the different clones. Not only, therefore, a different antibiotic approach but also a starting point for considering different host factors, i.e. the modulation of specific cytokines such as IL-6, TNF-α, and TGF-β1.

The hydrodynamic behavior of the air-acetic acid system in a bubble column is studied using a differential pressure transmitter, double probe optical fiber probe, and the electrical resistance tomography (ERT) technique. The superficial gas velocity ranges from 0.016 to 0.094 m/s under ambient temperature and pressure. The influences of viscosity and surface tension on gas holdup, bubble rising velocity, and bubble chord distribution in the column are discussed with different mass fractions of an acetic acid solution. The results show that as the mass fraction of acetic acid increases, the surface tension of the liquid phase decreases, and the viscosity first increases and then decreases. This causes the gas holdup in the column to first increase and then decrease, and reaches the maximum value at an acetic acid mass fraction of 55% to 60%. The rising velocity of the bubbles in the column is high in the central region and has a low-value distribution near the wall. The bubble chord length distribution is concentrated, and the distribution of the bubble chord length in the column becomes narrow with any decrease in surface tension. Studying the hydrodynamic behavior of a bubble column with the air-acid system is of great significance considering the absence of data on air-organic acid systems.

This paper shares some new information on the ambient temperature profile and the heat stress occurrences directly underneath ground-mounted Solar Photovoltaic (PV) Arrays (monocrystalline-based) focusing on different temperature levels. A common ground for this work lies on the fact that 10C increase of PV cell temperature results in reduction of 0.5% energy conversion efficiency thus any means of natural cooling mechanism would gain much benefit especially to the Solar Farm operators. Transpiration process plays an important role in the cooling of green plants where in average it could dissipate around 32.9% of the total solar energy absorbed by the leaf making it a good natural cooling mechanism. This condition is relatively applied for herbs specifically for this project, Orthosiphon Stamineus or generally known as Java Tea are used as the high value crops. The thermal process via convective heat and mass exchange of leaves with the environment is relevant for a better understanding of plant physiological processes in response to environmental conversion factors for a wide range of applications. An important fact for plant heat stress with respect to the Ambient temperature is that the range lies between 10 C to 15 C above the surrounding value. This heat stress condition is relatively important and should be modelled in crops-energy integration. Agrivoltaic concept is a system that combines commercial agriculture and photovoltaic electricity generation in the same space. The concept is in line with the Kyoto Protocol and the United Nation Sustainable Development Goals (UN-SDG) which highlights the clean energy and sustainable urban living. The integration of agrivoltaic systems would optimize the yield, improving clean system efficiency and solving the issue of land resource sustainability. The PV bottom surface temperature are the main source of dissipated heat as shown in the thermal images recorded at 5 minutes interval at 3 sampling time. Statistical analysis shows that the Thermal correlations for transpiration process and heat stress occurrences between PV bottom surface and plant height will be an important finding for large scale plant cultivation in agrivoltaic farms.

The proposal that the genetic code was formed on the basis of protein synthesis directed by tRNA dimers is reviewed and updated. The tRNAs paired through the anticodon loops are an indication on the process. Dimers are considered mimics of the ribosomes – structures that hold tRNAs together and facilitate the transferase reaction, and of the translation process – anticodons are at the same time codons for each other. The primitive protein synthesis system gets stabilized when the product peptides are able to bind the producers therewith establishing a self-stimulating production cycle. The chronology of amino acid encoding starts with Glycine and Serine, indicating the metabolic support of the Glycine-Serine C1-assimilation pathway, which is also consistent with evidence on origins of bioenergetics mechanisms. Since it is not possible to reach for substrates simpler than C1 and compounds in the identified pathway are apt for generating the other central metabolic routes, it is considered that protein synthesis is the beginning and center of a succession of sink-effective mechanisms that drive the formation and evolution of the metabolic flow system. Plasticity and diversification of proteins construct the cellular system following the orientation given by the flow and implementing it. Nucleic acid monomers participate in bioenergetics and the polymers are conservative memory systems for the synthesis of proteins. Protoplasmic fission is the final sink-effective mechanism, part of cell reproduction, guaranteeing that proteins don’t accumulate to saturation, which would trigger inhibition.

Wireless Sensor networks (WSNs) are gaining more and more research interest due to their ability to monitor large areas more or less independent. However, their reliability is an important issue, since their system reliability is influenced from a hardware, a data and an energetic point of view due to different factors like loading conditions, signal attenuation and battery lifetime. Regarding these issues, the positions of sensor nodes have to be chosen correctly to maximize the system reliability, which has to be considered during the development of new products equipped with WSNs. In this paper, an approach to estimate a WSNs system reliability during the development phase based on the analysis of the measurements on the example of strain measurements in finite elements (FE) models is presented. For that, the part under consideration is divided into regions with similar strains by the use of a Region Growing Algorithm (RGA). Afterwards, the WSN’s configuration is analyzed regarding its reliability in accordance to the data paths and measurement redundancy due to the sensor positions in the found measuring regions. This methodology is tested on an exemplary WSN configuration at an aircraft wing box under bending load and was found to work very well regarding the estimation of the hardware perspective on the system reliability. Therefore, the methodology and the developed algorithm show potentials for optimizations in sensor node positions for better reliability results.

The Internet of Things technology opens the horizon for a broader scope of intelligent applications in smart cities. However, the massive amount of traffic exchanged among devices may cause security risks, significantly when devices are compromised or vulnerable to cyber-attack. An intrusion detection system is the most powerful tool to detect unauthorized attempts to access smart systems. It identifies malicious and benign traffic by analyzing network traffic. In most cases, only a fraction of network traffic can be considered malicious. As a result, it is difficult for an intrusion detection system to detect attacks at high detection rates while maintaining a low false alarm rate. This work proposes an integrated framework to detect suspicious traffic to address secure data communication in smart cities. This paper presents an approach to developing an intrusion detection system to detect various attack types. It can be done by implementing a Principal Component Analysis method that eliminates redundancy and reduces system dimensionality. Furthermore, the proposed model shows how to improve intrusion detection system performance by implementing an ensemble model.

The North Sea Offshore Grid concept has been envisioned as a promising alternative to: 1) ease the integration of offshore wind and onshore energy systems, and 2) increase the cross-border capacity between the North Sea region countries at low cost. In this paper we explore the techno-economic benefits of the North Sea Offshore Grid using two case studies: a power-based offshore grid, where only investments in power assets are allowed (i.e. offshore wind, HVDC/HVAC interconnectors); and a power-and-hydrogen offshore grid, where investments in offshore hydrogen assets are also permitted (i.e. offshore electrolysers, new hydrogen pipelines and retrofitted natural gas pipelines). We compare these scenario results with a business as usual scenario, in which offshore wind is connected radially to the shore and no offshore grid is deployed. All scenarios are run with the IESA-NS model, without any specific technology ban and under open optimization. This paper also presents a novel methodology, combining Geographic Information Systems and Energy System Models, to cluster offshore spatial data and define meaningful offshore regions and offshore hub locations. This novel methodology is applied to the North Sea region to define nine offshore clusters taking into account offshore spatial claims, and identifying suitable areas for single-use and multi-use of space for renewable energy purposes. The scenario results show that the deployment of an offshore grid provides relevant cost savings, ranging from 1% to 4.1% of relative cost decrease (2.3 bn € to 8.7 bn €) in the power-based, and ranging from 2.8% to 7% of relative cost decrease (6 bn € to 14.9 bn €) in the power-and-hydrogen based. In the most extreme scenario (H2) an offshore grid permits to integrate 283 GW of HVDC connected offshore wind and 196 GW of HVDC meshed interconnectors. Even in the most conservative scenario (P1) the offshore grid integrates 59 GW of HVDC connected offshore wind capacity and 92 GW of HVDC meshed interconnectors. When allowed, the deployment of offshore electrolysis is considerable, ranging from 61 GW to 96 GW, with capacity factors of around 30%. Finally, we also find that, when imported hydrogen is available at 2 €/kg (including production and transport costs), large investments in an offshore grid are not optimal anymore. In contrast, at import costs over 4 €/kg imported hydrogen is not competitive.

A multi-server parallel system dispatches the incoming job, which contains kn tasks into n servers. A job is considered to be computed if all the tasks associated with the job are processed. One job’s tasks can be encoded into at least kn “replicas” such that the job is considered to be served if any kn replicas finishing computation. In this paper, we analyze the random scheduling policy of a multi-server computing system under discrete time model in terms of Quality of Exponent (QoE), which is defined as the probability exponent that a typical job can be computed within a given number of time slots. We let kn/n be a constant. Assuming that any task of any job can be randomly dispatched by a “scheduler” to any server, and computing each task takes exactly one time slot. We divide the calculation of probability exponent into two parts, exponent of numerator and exponent of denominator. For the denominator, we give the almost exact exponent using Lagrange multiplier method, while for the numerator, an upper bound of the numerator’s exponent is provided. In addition, we also express the exponent in terms of information theoretical quantities and reconsider both of exponents in the context of large deviation theory.

Inflammatory granulocytes are characterized by oxidative burst, which may promote oxidative stress and lipid modification both in affected tissues and on systemic level. On the other hand, redox signaling involving lipid peroxidation products acting as second messengers of free radicals play important, not yet fully understood, roles in pathophysiology of inflammation and various stress-associated disorders. Therefore, the aim of this study was to evaluate the onset of oxidative stress and alterations of enzyme-dependent lipid metabolism resulting from redox imbalance in granulocytes and plasma obtained from patients with psoriasis vulgaris or psoriatic arthritis, in comparison to the healthy subjects. The results obtained revealed enhanced activity of pro-oxidant enzymes NADPH and xanthine oxidases in granulocytes, with a decrease of enzymatic and non-enzymatic antioxidants in plasma of psoriatic patients. The Nrf2 and its regulators were increased in both forms of psoriasis, while HO-1 levels were increased only in psoriasis vulgaris. Redox imbalance was associated with decreased levels of phospholipids and of free PUFAs, but with enhanced activity of enzymes involved in lipid metabolism (PLA2, PAF-AH COX1/2) and increased lipid peroxidation products 4-hydroxynonenal (4-HNE), isoprostanes and neuroprostanes. Increased endocannabinoids and GPR55 were observed in both forms of the disease, while expression of CB1 was increased only in pateints with psoriatic arthritis, opposite to CB2, which was increased only in psoriasis vulgaris. Protein modifications by ROS and lipid peroxidation product 4-HNE promoted apoptosis of granulocytes by increased caspases in both forms of psoriasis. This study indicates that excessive activation of granulocytes, causing oxidative stress and lipid modifications, is an important pathophysiology of psoriasis. Consequently, lower Nrf2 activity and CB2 expression may promote progression of psoriasis into advanced, arthritic form of the disease.

Building Information Modelling (BIM) is increasingly adopted in supporting Facility Management (FM). However, in the future perspective of efficiently encouraging the management of building estates, e.g. owned by public authorities or institutions, providing an effective integration between Building Information Models and Geographic Information Systems (GIS) becomes also essential. This contribution is therefore aimed at presenting a methodological framework integrating BIM and GIS environments by mainly taking advantage of open-source tools (e.g. QGIS and Dynamo) and non-proprietary data exchange formats like the COBie (Construction-Operations Building Information Exchange) international standard. Unlike what has been mainly proposed in the literature concerning BIM-GIS integration so far, the methodology presented in this paper does not provide for an integral transfer of BIM data to the GIS platform. The work led instead to the development of an effective bidirectional integration between the two informative systems, by properly relating them and allowing for an easy switch from one system database to the other. This integrated framework is intended to enable facility managers to easily visualize in the GIS platform, through queryable 2D and 3D maps, some relevant information from BIM to efficiently manage workspaces. At the same time, this hybrid informative system allows BIM practitioners to access and manage more detailed information stored in Building Information Models. The findings of experimental applications of the proposed methodological approach to the ex-Macciotta pediatric hospital, a healthcare building owned by the University of Cagliari, are finally presented and discussed.

The concept of geostationary VHTS (Very High Throughput Satellites) is based on multibeam coverage with intensive frequency and polarization reuse in addition to the use of larger bandwidths in the feeder links, in order to provide high capacity satellite links at a reduced cost per Gbps in orbit. The dimensioning and design of satellite networks based on VHTS imposes the analysis of multiple trade-offs to achieve an optimal solution in terms of cost, capacity and figure of merit of the user terminal. In this paper, we propose a new method for sizing VHTS satellite networks based on an analytical expression of the forward link CINR (Carrier-to-Interference-plus-Noise Ratio) that is used to evaluate the trade-off of different combinations of system parameters. The proposed method considers both technical and commercial requirements as inputs including the constraints to achieve the optimum solution in terms of the user G/T, the number of beams and the system cost. The cost model includes both satellite and ground segments. Exemplary results are presented with feeder links using Q/V bands, DVB-S2X and transmission methods based on CCM and VCM (Constant and Variable Coding and Modulation, respectively) in two scenarios with different service areas.

Inspired by amazing result obtained by Semay [1], this study revisits generalised Kepler's third law of an n-body system from the perspective of dimension analysis. To be compatible with Semay's quantum n-body result, this letter reports a conjecture which had not be included in author's early publication [2] but formulated in the author's research memo. The new conjecture for quantum N-body system is proposed as follows: Tq|Eq|^3/2 = πG/√2[(Σ^N_i=1 Σ^N_j=i+1 m_im_j)³/(Σ^N_k=1 m_k)]^1/2. This formulae is, of course, consistent with the Kepler's third law of 2-body system, and exact same as Semay's quantum result for identical bodies.

The construction industry is one of the most fatal industries, so it is important to pay more attention to safety solutions. Even though work-related accidents are known as major waste in construction projects, little attention has been paid so far to incorporating safety into the lean construction framework. In this research, lean construction theory is reviewed through the lens of safety. That being so, the identified challenges in previous research on improving safety in construction projects are categorized and those related to the concept of lean project delivery are introduced. Then, the principles of the lean construction framework are explained and the relevant changes for incorporating safety into the framework are introduced and discussed. It is expected that this hybrid model would further enrich the lean construction framework. The careful attention of project executives to this model may improve the safety situation in construction projects.

Due to the imbalance between the supply and demand of oxygen, the oxygen system of iron- and steel-making enterprises in China has problems with high oxygen emission and high pressure in the pipelines, resulting in the energy consumption of oxygen production being high. To relieve this problem, using a large-scale iron- and steel-making mill as a case study, the research on demand forecasting and optimal scheduling of the oxygen system was carried out. The ARIMA model and the GABP model are employed to forecast oxygen demand. Based on the forecast results, an optimal scheduling model for the oxygen system was developed to conduct optimal scheduling. The case study shows that based on the oxygen demand forecast and the optimal scheduling, the oxygen emission and the pipeline pressure in the studied iron- and steel-making enterprise can be significantly reduced, thereby achieving considerable energy-saving effects and economic benefits. Specifically, the following conclusions are obtained: (1) For the oxygen demand forecast, the prediction accuracy of the GABP model is better than that of the ARIMA model. The average MAPE of the 12 sets of data of the ARIMA and GABP models are 23.8% and 20.2%, respectively. (2) By comparing the scheduling results and the field data, it is found that after the scheduling, the amount of oxygen emission has decreased by 6.32%, the pipeline pressure has decreased by 0.61%, and the energy consumption of oxygen compression has decreased by 1.6%. Considering both the oxygen emission loss and the energy consumption of oxygen compression, the total power consumption of the oxygen system is reduced by 1.38%, which saves the electricity cost of about 9.03 million RMB per year.

Installations of decentralised Renewable Energy Systems (RES) are becoming increasing popular as governments introduce ambitious energy policies to curb emissions and slow surging energy costs. This work presents a novel model for optimal sizing for decentralised renewable generation and hybrid storage system to create a Renewable Energy Community (REC), developed in Python. The model implements PV Solar and Wind Turbines combined with a hybrid battery and Regenerative Hydrogen Fuel Cell (RHFC). The electrical service demand is derived using real usage data from a rural island case study location. Cost remuneration is managed with an REC virtual trading layer, ensuring fair distribution among actors in accordance with the European RED(III) policy. A multi-objective Genetic Algorithm (GA) stochastically determines the system capacities such that the inherent trade-off relationship between project cost and decarbonisation can be observed. The optimal design results in an LCOE of 0.15€/kWh, reducing costs by over 50% compared with typical EU grid power, with a project IRR of 10.8%, simple return of 9.6%/year, and ROI of 9 years. Emissions output from grid only use is reduced by 72% to 69 gCO2e/kWh. Further research of lifetime economics and additional revenue streams in combination with this work could provide a useful tool for users to quickly design and prototype future decentralised REC systems.

The potential of quantum computing for scientific and industrial breakthroughs is immense, however, we are still in the Noisy Intermediate-Scale Quantum (NISQ) era, where the currently available quantum devices contain small numbers of qubits, are very sensitive to environmental conditions and prone to quantum decoherence. Even so, existing NISQ computers have already been shown to outperform conventional computers on specific problems and the key question is how to make use of today’s NISQ devices to achieve quantum advantage in the field of computational science and engineering (CSE). In this direction, this work proposes a hybrid computing formulation by combining quantum computing with machine learning for accelerating the solution of parameterized linear systems in NISQ devices. In particular, it focuses on the Variational Quantum Linear Solver (VQLS), which is a hybrid quantum- classical algorithm for solving linear systems of equations. VQLS employs a short-depth quantum circuit to efficiently evaluate a cost function related to the system solution. The algorithm’s circuit consists of a quantum gate sequence (unitary operators) that involves a set of tunable parameters. Then, well-established classical optimizers are utilized to tune the parameters of the sequence in order to minimize the cost function, which is equivalent to finding the system solution at an acceptable level of accuracy. In this work, it is demonstrated that machine learning tools such as feed-forward neural networks and nearest-neighbor interpolation techniques can be successfully employed to accelerate the convergence of the VQLS algorithm towards the optimal values for the circuit parameters when applied to parameterized linear systems that need to be solved for multiple parameter instances.

In this paper, the experimental study of heating in 2 h lunch mode of air-conditioned bed system is carried out. The results show that the power consumption of heat conduction heating is only 0.34 kW·h, and the average heat dissipation is 81.3 W, while the power consumption of convection heating is 1.43 kW·h, with an average heat dissipation of 748.7 W. Regardless of power consumption or heat dissipation, convection heating is significantly higher than heat conduction heating, resulting in the room air temperature of the former increased from 12.3 °C to 17.3 °C, while the latter only increased from 14.4 °C to 15.2 °C. The study found that the water temperature of heat conduction heating can meet the thermal comfort needs of the human body in the range of 38~40 °C, while convection heating requires a higher temperature range, In contrast, the grade of hot water required for heat conduction heating is lower. Through the experiment, it is also found that the temperature of convection heating rises fast, but it is easy to appear dry feeling after a long time, while heat conductive heating has a slow temperature rise. there is a cool feeling within 20 min after the heating starts, and then the thermal comfort is better. The air conditioning system in this paper is used for the heating experiment in the winter lunch break mode, compared with convection heating, the overall thermal comfort of heat conduction heating is better and more energy efficient. it is suggested to adopt heat conduction heating mode in the winter heating of this system.

Metastases cause high mortality in several cancers and immunotherapies are expected to be effective in the prevention and treatment of metastatic disease. However, only a minority of patients benefit from immunotherapies. This creates a need for novel therapies that are efficacious regardless of the cancer types and metastatic environments they are growing in. Preclinical immuno-oncology models for studying metastases have long been limited to syngeneic or carcinogenesis-inducible models that have murine cancer and immune cells. However, the translational power of these models has been questioned. Interactions between tumor and immune cells are often species-specific and regulated by different cytokines in mice and humans. For increased translational power, mice engrafted with functional parts of human immune system have been developed. These humanized mice are utilized to advance understanding the role of immune cells in the metastatic process, but increasingly also to study the efficacy and safety of novel immunotherapies. From these aspects, this review will discuss the role of immune cells in the metastatic process and the utility of humanized mouse models in immuno-oncology research for metastatic cancers, covering several models from the perspective of efficacy and safety of immunotherapies.

Irrigation scheduling is used by growers to determine the right amount and timing of water application. In most parts of Mozambique, 90% of the total yearly precipitation occurs from November to March. The El Niño Southern Oscillation (ENSO) phenomenon influences the climate in Mozambique and affects the water demand for crop production. The objectives of this work were to quantify the effects of ENSO phenomenon on tomato crop water requirements, and to create the AgroClimate irrigation tool (http://mz.agroclimate.org/) to assist farmers in improving irrigation management. This study was based on daily grid-based climate information from 1983 to 2016 from the Climate Forecast System Reanalysis. Daily crop evapotranspiration was calculated by Hargreaves equation and crop coefficients. This tool is available online and considers different planting dates, ENSO phases, and crop growing season lengths. Irrigation needs varied from less than 250 mm per growing cycle during winter to 550 mm during spring. Both El Niño and La Niña influenced the irrigation scheduling, especially from November to March. El Niño periods were related with increased water demand due to drier and warmer conditions while the opposite was observed for La Niña. The ENSO information might be used to understand climate variability and improve tomato irrigation scheduling in Mozambique.

In this study, we present case studies to explore the dynamics of implementation and maintenance of health interventions. We analyze how specific interventions are built and eroded, how the building and erosion mechanisms are interconnected, and why we can see significantly different erosion rates across otherwise similar organizations. We use multiple comparative obesity prevention case studies to provide empirical information on the mechanisms of interest, and use qualitative systems modeling to integrate our evolving understanding into an internally consistent and transparent theory of the phenomenon. Our preliminary results identify reinforcing feedback mechanisms, including design of organizational processes, motivation of stakeholders, and communication among stakeholders, which influence implementation and maintenance of intervention components. Over time, these feedback mechanisms may drive a wedge between otherwise similar organizations, leading to distinct configurations of implementation and maintenance processes.

This research focuses on developing a Student Performance Evaluation System to enhance effectiveness and oversight in higher education. The system aims to address challenges faced by instructors in evaluating student performance using a distributed system approach. Agile methodology is employed in development, and database replication techniques are applied for data synchronization. System features include registration, assessment form creation, assessment submission, statistical visualization, and access to assessment history. The system is designed to improve efficiency and transparency in the assessment process. Key features of the system include registration process, assessment form creation, assessment submission, statistical visualization, and access to assessment history. Its goal is to enhance efficiency and transparency throughout the assessment process. The article details the system workflow and the roles played by Admins, Instructors, and students in system usage. Additionally, it discusses the back-end development model and pathways, as well as the performance testing results. Performance testing indicates that the system achieves high throughput of up to 21.55 requests per second, with an average response time of only 34 milliseconds and an error rate of zero percent. These results affirm that the system efficiently handles user loads and nearly instantaneously responds to requests, providing an optimal user experience in the context of student performance evaluation in higher education.

This paper bases on panel data from 30 provinces and municipalities in China spanning the period of 2010 to 2021 to examine the evolving mechanisms and influencing factors of agricultural product circulation efficiency in the provinces along the Belt and Road Initiative (BRI) within the context of green total factor productivity (TFP). The study research employs various methodologies including the SBM-DDF model, GML index, system GMM, and global Moran's I index. The findings are as follows: Initially, the provinces along the BRI exhibit an overall negative growth trend in the green TFP of agricultural product circulation, with a noticeable decline observed particularly after the initiation of the BRI in 2014. Secondly, foreign investment levels significantly contribute to the positive enhancement of green TFP in agricultural product circulation among the provinces along the BRI. Eventually, environmental regulations, government support, and the level of international trade in agricultural products exert significant negative impacts. Thirdly, the results of spatial effect tests reveal substantial spatial spillover effects in the green TFP of agricultural product circulation among the provinces along the BRI, as demonstrated through examinations utilizing three distinct spatial matrices.

Objectives: The present study was conducted to evaluate the indicators of the aging-friendly using GIS software in the eight districts of Ahwaz, southwest Iran in 2022. Materials and methods: this descriptive-analytical study was conducted on 317 older adults by stratified random sampling method. Data collection tools were demographic characteristics and aging-friendly city indicators. GIS methods were used for the spatial analysis and SPSS version 28 software was used to analyze descriptive statistics. Results: The mean age of the participants was 66.21±6.99 years. Most of the participants in this study were male (53.9%), illiterate (34.7%), married (84.9%) and residents of district 7 (22.1%). The findings of the present study showed that all 8 districts follow spatial autocorrelation and cluster pattern in all components of the aging-friendly city (urban open spaces, intra-urban transportation system, public and religious places and buildings, safety and ease of traffic, social participation and communication, social respect, culture-recreation, health and treatment). Conclusion: According to the results, the city of Ahwaz is far from the ideal level among the components of an aging-friendly city; therefore, provincial and city officials should pay more attention to these indicators and take more effective steps to increase their quality.

The study sought to determine the rate of discordant results between genotypic and phe-notypic tests for the diagnosis of drug resistant tuberculosis (DR-TB). Sputum samples and cultured isolates from suspected DR-TB patients were respectively analysed for My-cobacterium tuberculosis by Xpert® MTB/RIF (Cepheid, USA) and Line Probe Assays (LPA) (Hain, Germany). Discrepant Rifampicin (RMP) resistant results were confirmed using BACTEC MGIT960 (BD, USA). Of 224 RMP-resistant by Xpert MTB/RIF, 5.4% were suscep-tible to RMP by LPA. MGIT960 showed 75% agreement with LPA. Discrepancy was at-tributed to either heteroresistance or DNA contamination during LPA testing in 58.3% of cases. In 25% of samples showing agreement in RMP resistance between Xpert MTB/RIF and MGIT960, discrepancy was attributed to laboratory errors causing false RMP suscep-tible results with LPA. Of 16.7% of cases, discrepancy was attributed to false RMP suscep-tible results with Xpert MTB/RIF. Of 224 isolates, susceptibility to isoniazid (INH) by LPA was performed in 73.7% RMP resistant isolates, of which, 80.6% were resistant. All RMP resistant isolates by Xpert MTB/RIF were confirmed in 98.5% by LPA if TB isolates were resistant to INH, but only confirmed in 81.3% if TB isolates were INH susceptible (ρ < 0.001). In conclusion, Laboratory errors should be considered when investigating dis-cordant results.

The geographical location of Yunnan province is at the upstream area of water vapor transportation from the Bay of Bengal and the South China Sea to inland China. Understanding the spatiotemporal variations of water vapor over this region holds significant importance. We utilized the GNSS data collected from 12 stations situated in Yunnan, which are part of Crustal Movement Observation Network of China, to retrieve hourly precipitable water vapor (PWV) from 2011 to 2022. The retrieved PWV at Station KMIN were evaluated by the nearby radiosonde data, and the result shows that the mean bias and RMS of the differences between the two datasets are 0.08 and 1.78 mm respectively. Average PWV values at these stations are in the range of 11.77 to 33.53 mm, which decrease from the southwest to the north of Yunnan and are negatively correlated with the station heights and latitudes. Differences between average PWV in wet season and dry season range from 12 to 27 mm. These differences tend to increase as the average PWV increases. The yearly rates of PWV variations, averaging 0.18 mm/year, are all positive for the stations, indicating a year-by-year increase in water vapor. Amplitudes of the PWV annual cycles are 9.75-20.94 mm. The spatial variation of these amplitudes is similar to that of average PWV over the region. Generally, monthly average PWV values increase from January to July and decrease from July to December, and the growth rate is less than the decline rate. Average diurnal PWV variations show unimodal PWV distributions over the course of the day at the stations except Station YNRL, where bimodal PWV distribution was observed.

Objective: This study aimed to examine the relationship between health structure disparities and hospitalizations and deaths among the pediatric population in different regions. Method: The study employed an ecological time series design, utilizing population-based secondary data ob-tained from DATASUS, from the Brazilian Ministry of Health. The data included in this study encompassed the population aged zero to 19 years, health establishments, physicians working in health establishments, hospitalizations, and deaths related to Morbidities listed in the Interna-tional Classification of Diseases. The focus was explicitly on individuals diagnosed with Cere-bral Palsy and other paralytic syndromes. The data for this study were gathered from January 2018 to December 2021. TABNET was used for data collection, and Excel for tabulation and anal-ysis. Results: Analysis of health structure data across regions revealed notable disparities, par-ticularly in the North and Northeast regions, compared to other regions. These discrepancies prompted a decision to conduct a comparative assessment between these regions and the rest of the country. Conclusion: In the North region, a significant 50% difference in hospitalizations based on residence versus a place of care was observed for individuals with Cerebral Palsy and other paralytic syndromes, indicating the necessity for relocation to access appropriate medical treatment.

Purpose Prospective life cycle assessment (pLCA) is a key method for providing environmental decision support for future technologies. The specific technology under study is often modeled by the practitioner (the so-called foreground system), whereas data for supply chains are sourced from life cycle inventory databases (the so-called background system). Within pLCA, it is paramount to consider possible future developments for both foreground and background systems to avoid a temporal mismatch between the specific technology under study and the wider economy, which could lead to incorrect conclusions. Therefore, and given the importance of future-oriented environmental decision support, efforts to develop prospective life cycle inventory (pLCI) databases have recently increased. However, there is still much to be improved to realize the widespread use of these databases and to have a stronger real-world effect on how future technology is designed. Methods This paper summarizes recent developments and structures the information chain behind pLCI databases. We then discuss conditions for the broad application of pLCI databases, and we propose priorities and ways forward to overcome existing challenges to foster the use of pLCI databases. Results The conditions are scientific integrity, usefulness to LCA practitioners, accessibility, usability, interpretability, and a continuous improvement process in which core stakeholders coordinate efforts to streamline the generation, sharing, and use of pLCI databases. Conclusions Priority should be given to installing a continuous process to improve the coverage and consistency of pLCI databases, to improve related guidance and documentation, and to remove practical barriers relating to the access and usability of pLCI databases in LCA software.

This paper is regarding design and program an Micro-controller Arduino Uno board by using Arduino software to work as a photo-sensor(Active) single axial solar tracker system(SASTS). A solar panel, two photo-resistors (LDR) in two sides (north/south) of the photo-voltaic(PV) and a servo motor are connected to the Uno board, which is running a code that prepared by Arduino software IDE in advanced then it works as a tracking system. Here, the LDRs send the signal of presence or absence of the light to the board and based on that sent signal the Uno reflects a new signal to the servo motor to rotate and finds the light source. Lastly, the photo-sensor single axis tracker is made while Continuously, the system tries to face the panel to the sun and whilst changing the irradiance intensity it starts searching to find the angle of highest irradiance. Based on results that are extracted from the data, the tracker system significantly boosts the output efficiency of the solar panel. By using the Micro-controller Uno board, LDRs, servo motor and special designed mechanical base, the tracking system is constructed, based on acquired data the influence of the STS on the increasing the solar panel efficiency is more obvious. Significantly, the tracker system rises the efficiency of the PV .

The lymphatic system of the brain meninges and head plays a crucial role in the clearance of amyloid-β protein (Aβ), a peptide thought to be pathogenic in Alzheimer’s disease (AD), from the brain. The development of methods to modulate lymphatic clearance of Aβ from the brain coild be a revolutionary step in the therapy of AD. The opening of the blood-brain barrier (OBBB) by focused ultrasound is considered as a possible tool for stimulation of clearance of Aβ from the brain of humans and animals. Here, we propose an alternative method of non-invasive music-induced OBBB that is accompanied by the activation of clearance of fluorescent Aβ (Fαβ) from the mouse brain. Using confocal imaging, fluorescence microscopy and magnetic resonance tomography, we clearly demonstrate that OBBB by music stimulates the movement of Fαβ and Omniscan in the cerebrospinal fluid and lymphatic clearance of Fαβ from the brain. We propose the extended detrended fluctuation analysis (EDFA) as a promising method for the identification of OBBB markers in the electroencephalographic (EEG) patterns. These pilot results suggest that music-induced OBBB and the EDFA analysis of EEG can be a non-invasive, low cost, labelling free, clinical perspective and completely new approach for the treatment and monitoring of AD.

In this work we study the intersection properties of a finite disk system in the euclidean space. We accomplish this by utilizing subsets of spheres with varying dimensions and analyze specific points within them, referred to as poles. Additionally, we introduce two applications: estimating the common scale factor for the radii that makes the re-scaled disks intersects in a single point, this is the \v{C}ech scale, and constructing the minimal Axis-Aligned Bounding Box (AABB) that encloses the intersection of all disks in the system.

Motivation: there is a paradox at the heart of informatics where practical implementation generally fails to understand the socio-technical impact of novel technologies and disruptive innovation when adopted in `real-world’ systems. This phenomenon, termed technological determinism, is manifested in a time-lag between the adoption of novel technologies and an understanding of the underlying theory which develops following research into their adoption. Methods: we consider informatics theory as it relates to: social informatics and how humans’ function in society, the relationship between society and technology, information systems, information systems design, and human-computer interactions. The challenges posed by novel technologies and disruptive innovation are considered as they relate to information systems and information systems design. Open research questions with directions for future research are discussed with an introduction to and our proposed approach to socio-technical information system design. Significance: we conclude that the adoption of disruptive innovation presents both opportunities and threats for all stakeholders in computerised systems. However, determinism is a topic requiring research to generate a suitable level of understanding and technological determinism remains a significant challenge.

The Yangtze River is cross-linked with numerous lakes within its floodplain and is a worldwide biodiversity hotspot. There is no evidence indicating when this unique river-lake system developed. The endemic East Asian cyprinid clade has evolved diverse spawning adaptations to different flow conditions. Our ancestral egg-type reconstruction showed an ancestral state of adhesive eggs and later demersal eggs origination (both stream adaptations). Semi-buoyant eggs emerged ~18 Mya as a fast-flowing river adaptation, with increased hydration via three yolk protein degradation pathways, ion transport pathways and egg envelope permeability transition pores. Adhesive eggs evolved secondarily ~14 Mya with the egg envelope increasing to four layers and an adhesive layer, along with an increase in adhesiveness via microfilament/adhesive-related protein crosslinking and enhanced glycosaminoglycan biosynthesis, improving adherence to submerged lake plants, indicating that the cross-linked river-lake system formed in the mid-Miocene. This study provides a unique biological evidence for large-scale water system evolution.

Internet of Things (IoT) opens new horizons by enabling automated procedures without human interaction using IP connectivity. IoT deals with devices, called things which are represented as any item from our daily life that is enhanced with computing or communication facilities. Among various mobile communications, Zigbee communication is broadly used in controlling or monitoring applications due to its low data rate and low power consumption. Securing IoT systems have been the main concern for the research community. In this paper, different security-threats of Zigbee networks in IoT platform have been addressed to predict the potential security threats of Zigbee protocol and a Security Improvement Framework (SIF) has been designed for intelligent monitoring in an office environment. Our proposed SIF can predict and protect various potential malicious attacks in the Zigbee network and respond accordingly through a notification to the system administrator. This framework (SIF) is designed to make automated decisions immediately based on real-time data which are defined by the system administrator. Finally, the designed SIF has been implemented in an office security system as a case study for real-time monitoring. This office security system is evaluated based on the capacity of detecting potential security attacks. The evaluation results show that the proposed SIF is capable of detecting and protecting several potential security attacks efficiently enabling more secure way of intelligent monitoring.

The hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R² equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

A mathematical model given by a two - dimensional differential system is introduced in order to understand the transition process from the normal hematopoiesis to the chronic and accelerated acute stages in chronic myeloid leukemia. A previous model of Dingli and Michor is refined by introducing a new parameter in order to differentiate the bone marrow microenvironment sensitivities of normal and mutant stem cells. In the light of the new parameter, the system now has three distinct equilibria corresponding to the normal hematopoietic state, to the chronic state, and to the accelerated acute phase of the disease. A characterization of the three hematopoietic states is obtained based on the stability analysis. Numerical simulations are included to illustrate the theoretical results.

The paper proposed a modification of the GeoSER(D) model previously developed by us by detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents, this made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of 3 types of the strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. The paper showed that SARS COV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the consequence, not the initiator of the epidemic. Fluctuations in the dynamics of various indicators of the spread of SARS COV 2 associated with the difference in the daily schedule on weekdays and weekends. It has been shown that people's daily schedules strongly influence the spread of SARS COV 2. For the more contagious "rapid" strains of SARS COV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious "slow strains" (alpha) of SARS COV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious – "fast" strain of the SARS COV 2 virus (omicron) spreads faster in public transport. For less contagious – "slow" strains of the virus (alpha), the greatest infection occurs due to work and educational contacts.

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.

We present a method to turn results of model-based optimisations into resilient and comprehensible control strategies. Our approach is to define priority lists for all available technologies in a district energy system. Using linear discriminant analysis and the results of the optimisations, these are then assigned to discrete time steps using a set of possible steering parameters. In contrast to the model-based optimisations, the deduced control strategies do not need perfect foresight but solely rely on data about the present. Our result indicate that the results of the control strategies obtained using the proposed method are comparable to the results of the linear optimisations, in our case in terms of emissions and prices.

We hypothesized possibility that inline registered reticulorumen pH can be as biomarker of cows reproduction and health status. Aim of this study was to evaluate the relationship of reticulorumen pH with biomarkers from automatic milking system (AMS) and some blood parameters and determinate reticulorumen pH as biomarker of cows reproduction and health status. According to cows reproductive status the cows were classified as belonging to the following four groups: 15-30 d. postpartum; 1-34 d. after insemination; 35 d. after insemination (non-pregnant); 35 d. after insemination (pregnant). According reticulorumen pH assay experimental animals were divided into four classes: 1) pH<6.22 (5.3% of cows), 2) pH - 6.22-6.42 (42.1% of cows), 3) pH - 6.42-6.62 (21.1% of cows), 4) pH >6.62 (10.5% of cows). Rumination time, body weight, milk yield, milk fat – protein ratio, milk lactose, milk somatic cell count (SCC), milk electrical conductivity of all quarters of udder were registered with the help of Lely Astronaut® A3 milking robots. The pH, temperature of the contents of cow reticulorumens and cow activity were measured using specific smaX-tec boluses. Blood gas parameters were analyzed using a blood gas analyzer (EPOC, Canada). We found that pregnant cows has higher reticulorumen pH during insemination time, comparing with non-pregnant. Cows with lower reticulorumen pH has lowest milk fat – protein ratio, and lactose concentration, and highest SCC. Cows with lowest reticulorumen pH has lowest blood pH. With increase reticulorumen pH, increases blood potasium and hematocrit, decreases CO2, saturation and sodium.

When analyzing user geospatial information through deep learning methods, it is typically necessary to annotate existing geospatial data. Currently, manual annotation methods are commonly employed, suffering from issues related to low efficiency and accuracy. This design is based on the TensorFlow deep learning framework and first realizes the BiLSTM+CRF+AGG deep learning model. Among the models, AGG is the aggregation layer, which is introduced to solve the problem of solid particle size equilibrium. Secondly, based on the characteristics of original data and professional data, an automatic labeling algorithm is proposed. The algorithm first preprocesses the acquired original data set and professional data set, The most valuable unlabeled data set which can make the training model converge quickly is selected from the original data set as the target data set. Sort the target data set based on preset rules and set annotation parameters for the sorted target data set. Based on the set annotation parameters, the corpus is synthesized and used as the annotation result. Thirdly, based on the active learning strategy, a manual annotation auxiliary scheme is proposed, and an Excel generation module that is convenient for manual annotation correction is designed and developed to further improve the efficiency and quality of annotation through iterative processing. The combination of the BiLSTM+CRF+AGG deep learning model and high-quality annotation can accurately identify non-standard, incomplete, or even incorrect geographical information entities. This annotation method has found successful practical application within the context of the research project and has been granted an invention patent.

News recommendation schemes utilize features of the news itself and information about users to suggest and recommend relevant news items to the users towards the interest they have. However, the effectiveness of the existing news recommendation scheme is limited in the occurrence of new user cold start problems. Therefore, we designed a news recommender system using hybrid approaches to address new user cold start problems to ease and suggest more related news articles for new users. To achieve the objective mentioned above, user demographic data with a hybrid recommendation system that contains the scheme of both content-based and collaborative filtering approaches is proposed. To evaluate the effectiveness of the proposed model, an extensive experiment is conducted using a dataset of news articles with user rating value and user demographic data. The performance of the proposed model is done by two ways of experiment. So, the performance of the proposed model performs around 68.05% of Precision, 42.46% of Recall and 52.1% of the average F1 score for the experiment based on individual user similarity in the system. And also performs around 93.75% of precision, 40.25% of recall and 56.31% F1-score for the similarity of users based on the similarity of users within the same category which is better than the first experiment.

The winged bean is an underutilized legume that has the potential to contribute to nutrition and food security globally, especially in sub-Saharan Africa (SSA). However, very little research attention is paid to exploiting its full potential due to a lack of adequate knowledge of the existing genetic diversity in the available winged bean germplasm. To bridge this gap, seed morphometric and selected agro-morphological traits which are crucial for yield and seed quality determination in crop plants were used to assess the genetic diversity of thirty accessions of winged bean sourced from the Gene bank of the International Institute of Tropical Agriculture (IITA), Ibadan. The thirty winged accessions were evaluated under field trials at three agro-ecological zones in Nigeria. The agro-morphological data as well as the seed morphometric dataset obtained from a Multi-Spectral Imaging (MSI) system were subjected to Analysis of Variance (ANOVA), Principal Component (PC) analysis, cluster analysis, and correlation analysis. Significant differences (p &lt; 0.05) were observed among the accessions for all measured traits. The first three PCs accounted for 93% of the variation observed among the accessions for all measured traits. Accessions were grouped into three clusters based on the agro- morphological traits and three clusters under the MSI system. Seed yield per plant had the highest estimates of significant positive correlation with hundred seed weight, pod weight per plant, etc. at both phenotypic and genotypic levels. TPt-31 had the highest seed yield per plant, pod weight per plant, and early maturity while TPt-7 had an extended flowering and maturity period, the highest number of pods per peduncle and pods per plant as well as the lowest seed yield. These accessions could serve as a good resource for future winged bean improvement programs. The result also confirmed that the MSI system is an invaluable tool for discriminating among accessions of the same crop species.

This paper proposes a parameter coordination optimization design of Power System Stabilizer (PSS) based on an improved Quasi-Affine Transformation Evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power system. To begin, the Simulated Annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to power system. Second, Since damping ratio is one of the criteria to measure the dynamic stability of power system, this paper sets the objective function according to the principle of maximization of damping coefficient of electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the stability margin of the system as much as possible. Finally, the method's rationality and validity were validated by applying it to the simulation examples of IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, and can restrain the low-frequency oscillation of power system more effectively and enhance the system stability.

A retrospective study was conducted on pathologically diagnosed arrhythmogenic cardiomyopathy (ACM) from consecutive cases over the past 34 years (n = 1,109). The cardiac conduction system (CCS) was removed in two blocks, containing the following structures: Sino-atrial node (SAN), atrio-ventricular junction (AVJ) including the atrio-ventricular node (AVN), the His bundle (HB), the bifurcation (BIF), the left bundle branch (LBB) and the right bundle branch (RBB). The ACM cases (2.07% of the total cases) consisted of 20 (86.96%) sudden unexpected cardiac death (SUCD) and 3 (13.04%) native explanted hearts; 16 (69.56%) were males and 7 (30.44%) were females, ranging in age from 5 to 65 (mean age ± SD, 36.13 ± 16.06) years. The following anomalies of the CCS, displayed as percentages of ACM SUCD cases, have been detected: Hypoplasia of SAN (80%) and/or AVJ (86.67%) due to fatty-fibrous involvement, AVJ dispersion and/or septation (46.67%), central fibrous body (CFB) hypoplasia (33.33%), fibromuscular dysplasia of SAN (20%) and/or AVN (26.67%) arteries, hemorrhage and infarct-like lesions of CCS (13.33%), islands of conduction tissue in CFB (13.33%), Mahaim fibers (13.33%), LBB block by fibrosis (13.33%), AVN tongue (13.33%), HB duplicity (6.67%%), CFB cartilaginous meta-hyperplasia (6.67%), and right sided HB (6.67%). Arrhythmias are the hallmark of ACM, not only from the fatty-fibrous disruption of the ventricular myocardium that accounts for reentrant ventricular tachycardia, but also from the fatty-fibrous involvement of CCS itself. The careful examination of the cardiac conduction system on serial sections was crucial in documenting the fatty-fibrous infiltration of CCS in ACM.

With the development of information technology and the popularization of mobile devices, collecting various types of customer data such as purchase history or behavior patterns became possible. As the customer data being accumulated, there is a growing demand for personalized recommendation services that provide customized services to customers. Currently, global e-commerce companies offer personalized recommendation services to gain a sustainable competitive advantage. However, previous research on recommendation systems has consistently raised the issue that the accuracy of recommendation algorithms does not necessarily lead to the satisfaction of recommended service users. It also claims that customers are highly satisfied when the recommendation system recommends diverse items to them. In this study, we want to identify the factors that determine customer satisfaction when using the recommendation system which provides personalized services. To this end, we developed a recommendation system based on Deep Neural Networks (DNN) and measured the accuracy of recommendation service, the diversity of recommended items and customer satisfaction with the recommendation service. The experimental results of is the study showed that both recommendation system accuracy and diversity would have a positive effect on customer satisfaction. These results can further improve customer satisfaction with the recommendation system and promote the sustainable development of e-commerce.

This report is considered different aspects of the concept of the networked distributed engine control system (DECS) of future air vehicles. These aspects include the following: the structure of multiple networks similar to NATO Generic Vehicle Architecture (NGVA), the role of Artificial Intelligence (AI) in DECS, and the use Augmented Reality (AR) as Human-Machine Interface between AI and pilots. Deployment of AI solutions for monitoring equipment in on-board infrastructure can be provided on physical or virtual servers and in the clouds. In this case, it is possible to use various methods of alerting the pilot and ground personnel on the basis of AR. The use of AI allows covering an unlimited set of scenarios, to provide an assessment of the likelihood of equipment failure, classification alarm is normal, and recognition of the development of defects. To collect Big Data from sensors and the pre-processing of this data before a machine learning (ML) procedure it is proposed to form data sets with the help of the face-splitting matrix product. To decrease the time of reaction of Neural Networks it has been suggested the implementation of advanced tensor-matrix theory on the basis of penetrating face product of matrices. Other important results of the report are a possible version of the AR data format for DECS and a proposal about the use of non-orthogonal frequency discrete multiplexing (N-OFDM) signals to data transfer via fibre optics.

In this paper we investigate in different scenarios the feasibility of using massive multiple-input multiple-output (mMIMO) with Reconfigurable Intelligent Surfaces (RISs) to boost the throughput and coverage performances with high energy efficiency, considering sub-6 GHz, mmWave and THz bands. With that objective, a centralized radio access network (C-RAN) suitable for beyond fifth generation (B5G) systems is considered, where we integrate the base stations (BSs) with multiple RISs and devices or user equipment. RISs with a large number of quasi-passive reflecting elements constitute a low-cost approach capable of shaping the radio wave propagation and improve wireless connectivity. We consider a scenario where multiple RISs are combined with mMIMO in the uplink in order to provide connectivity to Smart City (thousands of active low power devices) wirelessly, in the 3.6GHz and 28GHz bands. We also address a scenario where RISs are adopted with mMIMO in the downlink so as to offer connectivity to a Stadium with Pitch, (thousands of active user equipment) in the 28GHz band. Finally, we have also studied the connectivity at 100GHz of a Factory where several RIS panels replacing most of the BSs equipped with mMIMO assure improved throughput and coverage. We concluded that RISs are capable of improving the performance in any of these analyzed scenarios at the different frequency bands, justifying that they are a key enabling technology for 6G.

Upper limb paresis is very common after stroke. An important tool to assess motor recovery is to use marker-based motion capture systems to measure the kinematic characteristics of patients’ movements in ecological scenarios. These systems are, however, very expensive and, as such, not readily available for many rehabilitation units. Here, we explored whether the markerless hand motion capabilities of the cost-effective Oculus Quest head-mounted display could be used to provide clinically meaningful measures. 14 stroke patients executed ecologically relevant upper limb tasks in an immersive virtual environment. During task execution, we recorded their hand movements simultaneously by means of the Oculus Quest’s and a marker-based motion capture system. Our results showed that the markerless estimates of the hand position and peak velocity provided by the Oculus Quest were in very close agreement with those provided by a marker-based commercial system with their regression line having a slope close to 1. Furthermore, the Oculus Quest had virtually the same sensitivity as that of a commercial system in distinguishing healthy from pathological kinematic measures. The Oculus Quest was as accurate as a commercial marker-based system in measuring clinically meaningful upper limb kinematic parameters in stroke patients.

Nanosized drug delivery systems (DDS) have been studied as a novel strategy against cancer due to their potential to simultaneously decrease drug inactivation and systemic toxicity and increase passive and/or active drug accumulation within the tumor(s). Triterpenes are plant-derived compounds with interesting therapeutic properties. Betulinic acid (BeA) is a pentacyclic triterpene which has great cytotoxic activity against different cancer types. Herein, we developed a nanosized protein-based DDS of bovine serum albumin (BSA) as the drug carrier combining two compounds: doxorubicin (Dox) and the triterpene BeA using an oil-water-like micro-emulsion method. Spectrophotometric assays were performed to determine protein and drug concentrations in the DDS. The biophysical properties of these DDS were characterized using dynamic light scattering (DLS) and circular dichroism (CD) spectroscopy confirming nanoparticle (NP) formation and drug loading into the protein structure, respectively. The encapsulation efficiency was 77% for Dox and 18% for BeA. More than 50% of both drugs were released within 24 h, at pH 6.8, while less drug was released at pH 7.4 in this time period. Co-incubation viability assays of Dox and BeA alone for 24 h demonstrated synergistic cytotoxic activity in the low μM range against the non-small cell lung carcinoma (NSCLC) A549 cells. Viability assays of the BSA(Dox+BeA) DDS demonstrated a higher synergistic cytotoxic activity than the two drugs with no carrier. Moreover, confocal microscopy analysis confirmed cellular internalization of the DDS and nucleus accumulation of the Dox. We determined the mechanism of action of the BSA(Dox+BeA) DDS, confirming S-phase cell cycle arrest, DNA damage, caspase cascade activation, and downregulation of the epidermal growth factor receptor (EGFR) expression. This DDS has the potential to synergistically maximize the therapeutic effect of Dox and diminish chemoresistance induced by EGFR expression using a natural triterpene against NSCLC.

Following the transfer of hospital services to provincial government oversight, it is essential to understand how such changes impact service quality and efficiency. This study, conducted from 1 March 2023 to 31 March 2024 at sub-district health-promoting hospitals in Thailand, aims to identify the factors affecting hospital service quality post-transfer and evaluate the role of profes-sional nurses in maintaining service standards under new governance. Utilizing a mixed-methods design based on the Global Fund-HSS framework (2012), which categorizes factors into accessibility, processes, productivity, and outcomes, we collected data from 340 nurses and 400 patients through structured questionnaires. These questionnaires were divided into seven sec-tions, capturing metrics such as the personal data of nurses, hospital service activities, and lead-ership qualities. We employed a Likert scale to gauge perceptions and expectations of service quality and conducted paired t-tests to compare performance metrics before and after the admin-istrative transfer. One-way ANOVA was used to assess variability among different service units, and binary logistic regression helped identify key determinants of service quality. The analysis revealed a significant correlation between the attitudes and competencies of healthcare teams and the levels of service quality. Notably, service units transferred less than 50% exhibited mini-mal changes, indicating that the degree of transfer significantly impacts service outcomes. Despite these variations, the fundamental mission of promoting health remained consistent. The study emphasizes the critical role of nurses and recommends further research to identify additional factors that could improve service quality in transferred healthcare facilities, potentially contributing to enhanced healthcare delivery across restructured health systems.

The Duffy protein, a transmembrane molecule, functions as a receptor for various chemokines and facilitates attachment between the reticulocyte and the Plasmodium Duffy antigen-binding protein. Duffy expression correlates with the Duffy receptor gene for the chemokine, located on chromosome 1, and exhibits geographical variability worldwide. Traditionally, researchers have described the Duffy negative genotype as a protective factor against phenotypic malaria expression. However, recent studies suggest this microorganism's evolution potentially diminishes this protective effect. Nevertheless, there is currently insufficient global data to demonstrate this phenomenon. This study aimed to evaluate the relationship between the Duffy genotype/phenotype and the prevalence of Plasmodium vivax infection. The protocol for the systematic review was registered in PROSPERO as CRD42022353427 and involved reviewing published studies from 2012 to 2022. Medline, Web of Science, Scopus, and Scielo databases were consulted. Assessments of study quality were conducted using the STROBE and GRADE tools. A total of 34 studies were included, with Africa accounting for most recorded studies. The results varied significantly regarding the relationship between the Duffy genotype/phenotype and Plasmodium vivax invasion. Some studies predominantly featured the negative Duffy genotype yet reported no malaria cases. Other studies identified minor percentages of infections. Conversely, certain studies observed a higher prevalence (99%) of Duffy-negative individuals infected with Plasmodium vivax. In conclusion, no evidence of a gender-specific distribution of malaria between Duffy-negative men and women was found. However, evidence supports that the homozygous Duffy genotype positive for the A allele (FY*A/*A) is associated with a higher incidence of Plasmodium infection. Furthermore, the negative Duffy genotype does not confer protection against this disease.

Glacial meltwater has been lifeline of agriculture in Gilgit-Baltistan for centuries which is being effected by the climatic changes and associated challenges. The government line institutions and development agencies have been investing to sustain and further extend the cultivated areas by leveraging the available glacial meltwater source. However, approximately 13% arable land, laying mainly along river banks, has limited access to glacial meltwater. Moreover, already cultivated areas also need alternate and sustainable irrigation water supplies to insurefood security. Hence, there is need to explore more potential options to address these challenges and bringing arable land under irrigation for regional prosperity and environmental gains. The International Centre for Integrated Mountain Development (ICIMOD) and its partners piloted solutions for aforementioned issues in Gojal, District Hunza. These solutions included river water lifting through solar and Hydraulic Ram Pumps, uphill water storages and application through drip irrigation systems. In order to assess the technical and economical viabilities of these piloted systems, a semi-structured questionnaire survey, field observation visits to collect data and economic assessment studies were carried out in Morkhun and Khyber. Both piloted systems have been proven as climate resilient, adaptable, environment friendly and economically viable. The cost to benefit ratio for solar powered drip irrigation and Hydraulic Ram Pump drip irrigation systems are 2.96 and 4.21 respectively, with payback period of 9.21 and 8.25 years respectively. The drip irrigation systems at Khyber and Morkhun sites have exhibited excellent hydraulic performance with Coefficient of Uniformity 92.61 and 93 % respectively. However, the field application efficiency of drip irrigation systems estimated for Khyber and Morkhun sites are 85.24 and 88.88% which are lower than the optimal value mainly due to lesser water holding capacity. The Hydraulic Ram Pump can lift four times more water to greater height, while comparing to solar pump of approximately similar capacity and higher capital costs. Both water lifting systems have some limitations and requires local customization for sustainable use.

Information overload is the biggest challenges nowadays for any website, especially the e-commerce website. However, this challenge arises for the fast growth of information on the web (WWW) with easy access to the internet. Collaborative filtering based recommender system is the most useful application to solve the information overload problem by filtering relevant information for the users according to their interests. But, the existing system faces some significant limitations like as data sparsity, low accuracy, cold-start and malicious attacks. To alleviate the mentioned issues, the relationship of trust incorporates in the system where it can be between the users or items, and such system is known as the trust-based recommender system (TBRS). From the user perspective, the motive of the TBRS is to utilize the reliability between the users to generate more accurate and trusted recommendations. However, the aim of the paper is to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes on twenty-four trust metrics in terms of the methodology, trust properties &amp; measurement, validation approaches, and the experimented dataset.

The complexity of objects and control systems increases the requirements for mathematical models. The structural identifiability (SI) assessment of nonlinear systems is one of the identification problems. Until now, this problem solves by parametric methods using various approximation methods. This approach is not always effective under uncertainty. We apply an approach to SI estimation based on the analysis of virtual structures. The requirements form for the system input based on the expansion of the excitation constancy property and S-synchronizability. The approach generalization to structural identifiability used in the analysis of systems with symmetric nonlinearity is given. homothety and identifiability conditions for systems with non-symmetric nonlinearities (SNN) obtained. The detectability and recoverability proofed for virtual frameworks (VF) guaranteed the SI estimation under uncertainty. The conditions under which the non-symmetric nonlinearity is hypothetical symmetric nonlinearity obtained. SI estimation examples considered for closed nonlinear systems under uncertainty.

Abstract: Objective: To assess both individual and interactive effects of prenatal medical conditions depression and Diabetes, and health behaviors including smoking during pregnancy on infant birth defects. Methods: The data for this research study were collected by the Pregnancy Risk Assessment Monitoring System (PRAMS) in 2018. Birth certificate records were used in each participating jurisdiction to select a sample representative of all women who delivered a live-born infant. Complex sampling weights were used to analyze the data with a weighted sample size of 4,536,867. Descriptive statistics were performed to explore frequencies of the independent and dependent variables. Bivariate and multivariable analyses were conducted to examine associations among the independent and dependent variables. Results: The results indicate and significant interaction between the variables smoking and Depression and Depression and Diabetes (OR= 3.17; p-value <0.001 and OR= 3.13; p-value <0.001 respectively). Depression during pregnancy was found to be strongly associated with delivering an infant with a birth defect (OR= 1.31, P-value < 0.001). Conclusion: Depression during pregnancy and its interaction with smoking and Diabetes are vital in determining birth defects in infants. The results indicate that birth defects in the United States can be lowering Depression in pregnant women.

Managing epidemics need to unite each individual of the nations. Lockdown is an essential strategy to fatal and threatening epidemic. All of the citizens should realize that each of people has a responsibility to support the public health crisis. How to organize individual to fight against the epidemic plaque depends each of them. This essay discussed the COVID-19 pandemic relevant self-governance of community from a historical perspective in China. Self-governance of neighborhood committee and community residential office in urban and rural helped to control the COVID-19 pandemic in the communities in China. Co-operation and collective responsibility of citizens and community support is a critical condition to prevent epidemic. The community self-governance can track back Qin Dynasty in China history. It established baojia system to maintain social control for thousand years. Now, the community-based system, baojia system is becoming more autonomous for local citizenship and socialized governance in China. We reviewed a historical pneumonia plaque in North China in 1911. Chinese doctor, Wu Lien-teh (伍連德) confronted the epidemic crisis with many measures such as lockdown, quarantine, the wearing of mask, setup mobile hospitals, travel restriction, the cremation of victims, and border control. Dr. Wu made effort to establish the modern public health service in China. We think that the combination of culture background of China community with modern public healthcare system determinedly played important role to control the COVID-19 pandemic.

The objective of this study is to investigate the effect of the premixed ethanol ratio based on the same total heating value in-cylinder on the equivalence ratio distributions and the injected fuel droplet behavior in the cylinder of the RCCI engine. The spray simulation was conducted by dividing two-part. First, the spray validation simulations progressed to find the spray-influenced factor of the test injector. Next, the engine simulations were performed with the spray-influenced factor obtained from spray validation simulations to investigate the effect of the premixed ethanol ratio based on the same total heating value in-cylinder on the injected fuel atomization and the equivalence ratio distributions. The introduced total heating value was fixed at 595J based on the lower heating value of diesel 14mg. The heating value of the premixed ethanol ratio varied from 0% to 40% based on the same total heating value in-cylinder in steps of 10%. It was revealed that when the premixed ethanol ratio based on the same total heating value in-cylinder was increased, the spray tip penetration value was reduced from the time after 4deg of diesel injection start because of the short injection duration by the small amount of diesel fuel. The SMD value was also more enormous up to 32.58% with increasing the premixed ethanol ratio because of the low kinetic energy of the injected fuel by the short injection duration and the slow evaporation of the injected fuel by the low cylinder temperature.

We consider a system of two coupled parallel queues, queue 1 and queue 2, with infinite waiting rooms. The time setting is discrete. In either queue, the service of a customer requires exactly one discrete time slot. Arrivals of new customers occur independently from slot to slot, but the numbers of arrivals of both types within a slot may be mutually interdependent. Their joint probability generating function (pgf) is indicated as A(z1, z2) and characterizes the whole model. It is well-known that, in general, determining the steady-state joint probability mass function (pmf) u(m, n), m, n ≥ 0 or the corresponding joint pgf U(z1, z2) of the system contents (i.e., numbers of customers present) in both queues is a formidable task. Only in a number of isolated cases, for very specific choices of the arrival pgf A(z1, z2), explicit results are known in the literature. In this paper, we identify a multiparameter, generic class of arrival pgfs A(z1, z2), for which we can explicitly determine the system-content pgf U(z1, z2). We find that, for arrival pgfs of this class, U(z1, z2) has a denominator which is a product, say r1(z1)r2(z2) of two univariate functions. This property allows a straightforward inversion of U(z1, z2), resulting in a pmf u(m, n) which can be expressed as a finite linear combination of bivariate geometric terms. We also observe that our generic model encompasses most of the previously known results as special cases.

Methotrexate is an antimetabolic agent with proliferative and immunosuppressive activity. It has been demonstrated to be an effective treatment for acute lymphoblastic leukemia (ALL) in children. However, there is evidence of an association between methotrexate and toxicity risks, which influences the personalization of treatment, particularly in the case of childhood ALL. This article presents the development and implementation of an algorithm based on artificial neural networks to detect methotrexate toxicity in pediatric patients with acute lymphoblastic leukemia. The algorithm utilizes historical clinical and laboratory data, with an effectiveness of 99% in the tests performed with the patients dataset. The use of neural networks in medicine is often linked to disease diagnosis systems. However, neural networks are not only capable of recognizing examples, but also hold very important information. For this reason, one of the main areas of application of neural networks is the interpretation of medical data. In this article we diagnose with the application of neural networks in medicine with a concrete example: Detecting Methotrexate in Pediatric Patient in its early stages.

This study focuses on the optimal deployment problem, and determines the optimal size of a military force to send to the battle field. The decision is optimized, based on an objective function, that considers the cost of deployment, the cost of the time it takes to win the battle, and the costs of killed and wounded soldiers with equipment. The cost of deployment is modeled as an explicit function of the number of deployed troops and the value of a victory with access to a free territory, is modeled as a function of the length of the time it takes to win the battle. The cost of lost troops and equipment, is a function of the size of the reduction of these lives and resources. An objective function, based on these values and costs, is optimized, under different parameter assumptions. The battle dynamics is modeled via the Lanchester differential equation system based on the principles of directed fire. First, the deterministic problem is solved analytically, via derivations and comparative statics analysis. General mathematical results are reported, including the directions of changes of the optimal deployment decisions, under the influence of alternative types of parameter changes. Then, the first order optimum condition from the analytical model, in combination with numerically specified parameter values, is used to determine optimal values of the levels of deployment in different situations. A concrete numerical case, based on documented facts from the Battle of Iwo Jima, during WW II, is analyzed, and the optimal US deployment decisions are determined under different assumptions. The known attrition coefficients of both armies, from USA and Japan, and the initial size of the Japanese force, are parameters. The analysis is also based on some parameters without empirical documentation, that are necessary to include to make optimization possible. These parameter values are motivated in the text. The optimal solutions are found via Newton- Raphson iteration. Finally, a stochastic version of the optimal deployment problem is defined. The attrition parameters are considered as stochastic, before the deployment decisions have been made. The attrition parameters of the two armies have the same expected values as in the deterministic analysis, are independent of each other, have correlation zero, and have relative standard deviations of 20%. All possible deployment decisions, with 5000 units intervals, from 0 to 150000 troops, are investigated, and the optimal decisions are selected. The analytical, and the two numerical, methods, all show that the optimal deployment level is a decreasing function of the marginal deployment cost, an increasing function of the marginal cost of the time to win the battle, an increasing function of the marginal cost of killed and wounded soldiers and lost equipment, an increasing function of the initial size of the opposing army, an increasing function of the efficiency of the soldiers in the opposing army and a decreasing function of the efficiency of the soldiers in the deployed army. With stochastic attrition parameters, the stochastic model also shows that the probability to win the battle is an increasing function of the size of the deployed army. When the optimal deployment level is selected, the probability of a victory is usually less than 100%, since it would be too expensive to guarantee a victory with 100% probability. Some of many results of relevance to the Battle of Iwo Jima, are the following: In the deterministic Case 0 analysis, the optimal US deployment level is 66200, the time to win the battle is 30 days and 14000 US soldiers are killed or wounded. If the marginal cost of the time it takes to win a victory doubles, the optimal deployment increases to 75400, the time to win a victory is reduced to 26 days, and less than 12000 soldiers are killed or wounded. In the stochastic Case 0 analysis, the optimal US deployment level is 65000, the expected time to win the battle is 46 days and almost 25000 US soldiers are expected to be killed or wounded. If the cost per killed or wounded soldier increases from 0 to 5 M $US, the optimal deployment level increases to 75000. Then, the victory is expected to appear after 35 days and 19900 US soldiers are expected to be killed or wounded.

Gene editing is now easy to make new disease models through in-vivo and in vitro tests. It can potentially be used to make animals with single-gene or multiple-gene changes. The mutant strains with changed germlines are no longer needed with in vivo gene editing, which uses the CRISPR-Cas9 system to target cells of interest in their normal tissues. Whereas, the AAVs and other viral vectors have made it possible to change cells selectively. Gene editing hade made it possible to use human induced pluripotent stem cells (iPSC) to model diseases that run in families. Researchers can compare and contrast the human genomes of many different ethnic and racial groups using this method. Scientists may be able to make a disease in a lab dish using iPSCs from a patient. Using CRISPR, iPSCs made from patient cells can be fixed if they have certain problems. This shows that gene therapy is possible and shows what happens when cells aren't working right. The fact that CRISPR-Cas9 can change the DNA by just one nucleotide has had a huge effect on biological studies. CRISPR is becoming more and more popular, which shows how useful, easy, and effective it is. With the broad use of CRISPR-based apps, the tool is now used for much more than just changing genes. This method can be used to screen the whole genome, control the translation of genes based on their sequence, and edit several genes at the same time. Scientists can now model diseases in different species and learn more about how genes work because of these advances. Genome-wide association studies and genome-editing tools like CRISPR are giving us a good look at the future of personalized medicine.

The current paper provides a cutting-edge information data length- theoretic approach to the transient M/M/Infinity queueing system. This is a first time ever investigation that unifies information data length and queueing theories. The significant impact of both time and the number of states for the transient M/M/ Infinity queue on both the upper and lower bounds of the obtained information data length is observed and noted. The paper concludes with some challenging open problems, combined with concluding remarks and future research pathways.

Water networks are critical infrastructure components, ensuring the continuous supply of high-quality drinking water to consumers. To secure such water supply, regular maintenance, including the replacement of deteriorating pipelines, is essential. In this study, a methodology has been developed for determining optimal pipeline replacement solutions in water supply systems at water utilities with limited data availability. Hydraulic analysis has been conducted on the segment of 25 km of the water supply network using the free software EPANET. Applying water network optimization, eight pipeline replacement projects according to 13 pre-defined criteria have been identified and evaluated. The paper outlines the methods for evaluating the criteria, including defining specific quantitative limits. The Analytical Hierarchical Process (AHP) method was used in the paper to determine the weights of the criteria. The reason for applying this method refers to problems that involve a set of criteria with a mixed structure, including both quantitative and qualitative aspects. Also, the paper describes the steps of the multi-criteria optimization method VIKOR, used to select the optimal project. This paper, considered as case studie, describes a method, i.e. application of a new principles and an innovative way to solve a problem for developing countries.

At the beginning of 2023 the Italian former prime minister, the former health minister and 17 others including the current president of Lombardy region, were placed under investigation on suspicion of aggravated culpable epidemic in connection with the government’s response at the start of the COVID-19 pandemic. The charges revolve around the failure by authorities to take adequate measures to prevent the spread of the virus in the Bergamo area, which experienced a significant excess of deaths during the initial outbreak. The aim of this paper is to analyse pandemic data of Italy and Lombardy region in the first 10 days of the pandemic, spanning from the 24th of February 2020 to the 4th of March 2020. The objective is to determine whether the use early warning indicators could have facilitated the identification of a critical increase in infections. This identification, in turn, would have enabled the timely formulation of strategies for pandemic containment, thereby reducing the number of deaths. In conclusion, to translate our findings into practical guidelines, we propose a low-cost early warning method for infectious respiratory diseases with asymptomatic carriers.

Although global life expectancy has increased over the past 20 years due to improvements in managing infectious diseases, one in five people still succumbs to infections. Humanity is tirelessly working towards developing vaccines and antimicrobial agents in response to this ongoing threat. However, pathogens continue to acquire numerous resistance mechanisms and proliferate. Meanwhile, the COVID-19 pandemic has prompted us to recognize that focusing solely on the killing strategies of immunology and microbiology in complex and multisystemic infectious diseases is insufficient for understanding survival. In essence, there is a need for a treatment approach centered on disease tolerance, capable of alleviating pathology and/or promoting physiological functions in conjunction with the existing strategies of host immune response and pathogen destruction. Against this backdrop, this narrative review comprehends and introduces microbiome therapeutics, which recently garnered attention as next-generation treatments regarding two defense strategies against infectious diseases. First, we delineate the application of existing probiotics and then the microbiome in infectious diseases. Subsequently, we expand this concept to encompass host microbiome crosstalk and present related microbiome therapeutics' current status and direction. Ultimately, the review presents a fresh perspective on understanding microbiome therapeutics in infectious diseases based on a cooperative defense system.

Irrigation is crucial in cleaning and disinfection of the root canal system, because endodontic instruments are unable to reach a large part of the root canal system (isthmuses, accessory canals, apical ramifications) and bacteria that can reside. Sodium hypochlorite (NaOCl) is currently used as irrigant in root canal therapies for its non-specific protheolytic and antimicrobial properties, but undesirable effects may be observed especially when used near the terminus of apical foramen. This study aims to evaluate antimicrobial properties of Thyme Essential Oil (TEO) used alone or in combination with NaOCl against different bacterial strains, especially Staphylococcus aureus and Streptococcus mutans. TEO non-cytotoxic concentration (9,28mg/mL) showed antimicrobial properties comparable to NaOCl after 1min of contact, both in presence of organic material (6% sheep blood). Moreover, the combination of TEO and NaOCl did not compromise their individual antimicrobial properties at the same time of contact. These data suggest that TEO could be used as antimicrobial irrigant in root canal therapies in association with NaOCl, to reduce concentration of NaOCl and its undesirable side effects. Due to the absence of cytotoxic effects at tested dilution, TEO could be safely used also near the terminus of apical foramen for its cytocompatibility.

Commercial formulates of beneficial microbes have been used to enrich the rhizosphere microbiome of tomato plants grown in pots located in a glasshouse. These plants have been subjected to attacks by soil-borne parasites, such as root-knot nematodes (RKNs), and herbivores, such as the miner insect Tuta absoluta. The development of both parasites and the symptoms of their parasitism were restricted in these plants with respect to plants left untreated. A mixture, named in the text as Myco, containing plant growth promoting rhizobacteria (PGPR), opportunistic biocontrol fungi (BCF), and arbuscular mycorrhizal fungi (AMF) was more effective in limiting pest damages than a formulate containing the sole AMF (Ozor). Therefore, Myco-treated plants inoculated with RKNs were taken as a model to further study which biocontrol agent (BCA) was specifically responsible of plant immunization and the molecular pathways exploited to achieve such an immunization. The PGPR contained in Myco were not able to reduce nematode infection, rather, they worsened symptoms on plants, compared with those observed on untreated plants. When plants were treated with suspensions of Bacillus subtilis at concentrations similar to those provided with Myco and then inoculated with RKNs, infection was actually more severe than that on untreated plants. Therefore, it was argued that both BCF and AMFwere the microorganisms that, colonizing roots, synergistically stimulate plant immune system against RKNs. Beneficial fungi lowered the activities of the defense supporting enzymes endochitinases and β-1,3-glucanase to be able to colonize the roots. However, as early as three days after nematode inoculation, these enzyme activities, and the expression of the encoding pathogenesis related genes (PR-2, PR-3) were found to be enhanced in roots with respect to not inoculated plants, thus indicating that plants had been primed against RKNs. Addition of paclobutrazol, which reduced salicylic acid (SA) levels in cells, and of diphenyliodonium chloride, which inhibits superoxides generation, completely abolished the repressive effect of Myco on nematode infection and presumably plant immune reaction. Inhibitors of copper enzymes and of the alternative cyanide-resistant respiration did not significantly alter resistance induction by Myco. When Myco-treated plants were subjected to a moderate water stress and inoculated with nematodes, they retained numbers of developed individuals in the roots similar to those present in regularly watered plants, in contrast to what occurred in roots of untreated stressed plants that hosted very few individuals because of poor nutrient availability.

Recently, e-commerce companies have been adopting and continuously enhancing personalized product recommendation systems, and there is active research in this field within academia. However, personalized product recommendations in offline retail stores have not yielded significant outcomes thus far. Especially for small-format offline retail businesses like convenience stores, where customer information might be limited, providing personalized product recommendations poses even greater challenges. To address this issue, this study aimed to find a solution by shifting the perspective on recommendation methods and altering the target of recommendations in existing personalized recommendation models. In this study, recommending products that customers need in an offline store was defined as suggesting products that should be introduced and displayed within the store. In other words, the recommendation focus shifted from individuals to the stores themselves. This recommendation system proposes products that individual stores have not yet introduced but are anticipated to be purchased by customers among the products managed by the headquarters. Building upon this, we devised a store-based product recommendation system. The widely used user-based collaborative filtering model, a common technique in existing recommendation systems, was adapted into a store-based collaborative filtering model. Furthermore, various rules and logic pertinent to store operations and business considerations for convenience stores were integrated to implement this store product recommendation system. The accuracy and effectiveness of the system were demonstrated through its application in actual convenience stores. Results from the pilot implementation of the system showed that 88% of the newly recommended products in individual stores were sold based on one week of sales data, and the sales revenue was 1.75 times higher than the average sales revenue of those products across the entire company. Survey results on business owners' satisfaction yielded a score of 4.2 out of 5, indicating a high level of contentment. Additional observed effects included the expansion of product range and reduction in order lead times. However, 12% of the total recommended SKUs did not sell within one week, and 34% of the SKUs did not meet the overall average sales quantity. This research holds significance in extending the scope of personalized recommendation studies from primarily online platforms to offline retail businesses like convenience stores. It provides tangible methodologies and outcomes that can be implemented in real-world settings through the construction and validation of an actual system. The study also suggests avenues for future research to address some of the identified limitations.

Background and Objectives: Botulinum toxin injections are commonly used for the treatment of spasticity. However, injection procedures are associated with pain and procedural anxiety. While pharmacological approaches are commonly used to reduce these, inno-vative technology might be considered as a potential non-pharmacological alternative. Given this context, immersive virtual reality (VR) has shown effectiveness in the man-agement of procedural pain[1]. Our retrospective pilot study aimed to assess the potential added value of virtual reality in the management of pain and anxiety during intra-muscular injections of botulinum toxin. Materials and Methods: Twenty-one adult patients receiving botulinum toxin injections were included. A numerical rating scale was used to assess pain and anxiety during the injection procedure. The patient reported the pain experienced during previous injections without VR before injection and the pain expe-rienced in the current procedure with VR after the end of the procedure. Level of satis-faction of VR experience, if they agreed to reuse VR for the subsequent toxin botulinum injection, and if they would recommend VR to other patients were assessed. Results: The use of virtual reality showed a decrease of 1.8 pain-related points compared to without technology. No significant improvement in the level of anxiety was reported. Patients were very satisfied by their VR experience (7.9 out of 10) and would agree to reuse VR in their next injection procedure (88%) and to recommend the use of VR in other patients (100%). Conclusion: VR was useful for managing procedural pain related to botulinum toxin injection in adults, with a high level of satisfaction reported by the patient. VR should be considered as a valuable alternative to pharmacological approach to manage procedural pain during Botulinum toxin injection in adults.

incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of Mixed Kernel Principal Components Analysis and Dynamic Canonical Correlation Analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, Comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient fault in nonlinear and dynamic system. Meanwhile, fault identification experiments were conducted, validating the high accuracy of fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance.

The efficiency success status of organization built environment assets management needs robust and comprehensive maintenance processes which depends on the efficiency of the contract information documents for the maintenance management within the assets life cycle and achieve positive return of the investments. Therefore, this paper highlights on the appropriate interactive approach to structuring information flow scope of the asset facilities management contract for construction projects based on GIS and BIM integration processes, sustainability standards, and As-built contractual documents of the project to support the organization owner and stakeholder to improve the asset management processes. Using expert interviews with comprehensive understand for the information type in several facility management contracts conducted in common Organization types based on reviewing available international facility management standards manual and contract of procedures for different services types, and required information flow inside international facility management firms. The study classify and build significant integrated information and data flow model contract drafting with measures and indicators of performance efficiency as guideline for monitoring, technically evaluating, and financially procedures for performance efficiency and quality of services provided in the facilities management contract for King Faisal University campus projects as a case study.

Two clear-air over-the-horizon propagation mechanisms affecting Automatic Identification System (AIS) detection range are considered. Comparison results are presented between the path loss due to tropospheric ducting and path loss due to tropospheric scattering (troposcatter) for the AIS frequencies. The calculations are based on the well-known parabolic equation approximation to the wave equation, in which a simple troposcatter formula is incorporated. In most studied cases the ducting assures significantly greater reduction of path loss than troposcatter even when the AIS frequencies are not well trapped in the duct. Emphasis is placed on elevated trapping layers and some features that may make ducting propagation less favorable in terms of increasing the AIS detection range are discussed.

The problem of blood delivery has been a critical concern in past decades. However, the unsteady behavior of blood donors, along with the uncertainties associated with demands in a crisis imposes several challenges for resource management. In this research work, we analyze the blood delivery behavior in a crisis modeled by coupled queues of patients and blood sources. Using Markovian modeling and results associated with G-network, we derive two critical probabilities, namely lack of unit of blood (unit of donations) or not having any storage capacity left to accept new donors' blood. We propose a closed-form solution to calculate the optimal blood storage size, and also we suggest sufficient conditions that guarantee the feasibility of the model. Finally, we conduct a sensitivity analysis to investigate the impact of model parameters on storage size and the proportion of time that storage is full.

Cerebral ischemia, which is a leading cause of disability and mortality worldwide, sets off a chain of molecular and cellular pathologies that are associated with some central nervous system (CNS) disorders, mainly including ischemic stroke, Alzheimer's disease (AD), Parkinson's disease (PD), epilepsy and other CNS diseases. In recent times, despite significant advancements in the treatment of the pathological processes underlying various neurological illnesses, effective therapeutic approaches specifically targeted at minimizing the damage of such diseases remain absent. Remarkably, ischemia causes severe damage to cells in Ischemia-associated CNS Diseases. Cerebral ischemia initiates oxygen and glucose deprivation, which subsequently promotes mitochondrial dysfunction, including MPTP opening, mitophagy dysfunction, and excessive mitochondrial fission, triggering various forms of cell death, such as autophagy, apoptosis, as well as ferroptosis. Ferroptosis, a novel type of regulated cell death (RCD), is characterized by iron-dependent accumulation of lethal reactive oxygen species and lipid peroxidation. Mitochondrial dysfunction and ferroptosis both play critical roles in the pathogenic progression of Ischemia-associated CNS Diseases. In recent years, growing evidence has indicated that mitochondrial dysfunction interplays with ferroptosis to aggravate cerebral ischemia injury. However, the potential connections between mitochondrial dysfunction and ferroptosis in Cerebral Ischemia have not yet been clarified. Thus, we analyze the underlying mechanism between mitochondrial dysfunction and ferroptosis in Ischemia-associated CNS Diseases. We also discovered that GSH depletion and GPX4 inactivation cause lipoxygenase activation and calcium influx following cerebral ischemia injury, resulting in MPTP opening and mitochondrial dysfunction. Additionally, dysfunction in mitochondrial electron transport and an imbalanced fusion-to-fission ratio can lead to the accumulation of reactive oxygen species and iron overload, which further contribute to the occurrence of ferroptosis. This creates a vicious cycle that continuously worsens cerebral ischemia injury. In this study, our focus is on exploring the interplay between mitochondrial dysfunction and ferroptosis, which may offer new insights into potential therapeutic approaches for the treatment of Ischemia-associated CNS Diseases.

The impact of obesity upon bone metabolism is controversial since both negative and positive effects have been reported. Bone remodeling is modulated by the central nervous system through cytokines, hormones and neuromodulators. The present study aimed to evaluate the effects evoked by bilateral retroperitoneal white adipose tissue (rWAT) denervation (Dnx) upon bone mineral metabolism and remodeling in an experimental model of obesity in rats. Male Wistar rats were fed during 18 weeks with high-fat diet (HFD) or standard diet (SD) as controls, and rWAT Dnx or Sham surgery was performed at the 14th week. Biochemical and hormonal parameters, bone histo-morphometry, rWAT and hypothalamus protein and gene expression were analyzed. HFD group presented decreased bone formation parameters, increased serum and bone leptin and FGF23, increased serum and hypothalamic neuropeptide Y (NPY) and decreased serum 1,25-dihydroxyvitamin D3 and PTH. After rWAT Dnx, bone markers and histomorphometry showed restoration of bone formation, serum and hypothalamic NPY decreased, without altera-tion in leptin levels. The present study showed that the denervation of rWAT improved bone formation in obese rats mediated by a preferential reduction in neurohormonal actions of NPY, emphasizing the relevance of the adipose tissue-brain-bone axis in the control of bone metabolism in obesity.

Solar energy is a rapidly growing sector, and solar farms are playing an increasingly important role in meeting the world's energy needs. However, as the size and complexity of these farms increase, so do the challenges associated with managing them efficiently. This article presents a comprehensive analysis of the fundamental parameters that underpin solar energy systems. Focusing on the latest research, we examine the challenges and opportunities intrinsic to the implementation of solar energy systems, paying particular attention to the various parameters that contribute to their performance. These parameters encompass a range of factors such as thermal performance, atmospheric boundary layer, solar energy meteorology, heat islands, cloud influences, agrivoltaic systems, shading factors, and surface energy budget. The review underscores the importance of considering a diverse array of parameters when developing solar energy systems to optimize their efficiency and effectiveness. Ultimately, by unraveling these challenges and opportunities, we can work towards creating more sustainable and reliable renewable energy sources and reducing our dependence on non-renewable alternatives.

Misuse of measurement units and orientations leads to errors in scientific applications, Cyber Physical Systems (CPS), and IoT C/C++ programs. Standard type system are inadequate in preventing such errors. Although dimensional and orientational analysis in physics can manually detect these errors in equations, analyzing complex code with intricate physical computations is impractical. To overcome this challenge, we propose an advanced type system that incorporates units and orientations as integral components within a specialized type library. Our enhanced type system automatically detects potential errors during compile time by representing physical quantities as types and utilizing dimensional analysis, orientational analysis, and metaprogramming techniques. Our improved type system enables formal verification of C++ software, successfully verifying programs with extensive codebases. We also employ it for runtime verification of dynamic linking and pointer operations in C++ programs. The integration of compile-time verification, dimensional analysis, orientational analysis, and advanced type system enhances the robustness and accuracy of scientific applications, CPS, and IoT C/C++ programs. By leveraging these approaches, we ensure precise calculations and prevent errors related to measurement units and orientations, resulting in substantial improvements in reliability and accuracy.

The rapid growth of Earth's global geospatial data necessitates an efficient system for organizing the data, facilitating data fusion from diverse sources, and promoting interoperability. Mapping the spheroidal surface of the planet presents significant challenges, as it involves balancing distortion and splitting the surface into multiple partitions. The distortion decreases as the number of partitions increases, but at the same time the complexity of data processing increases, since each partition represents a separate data set and is defined in its own local coordinate system. In this paper, we propose the dual orthogonal equidistant cylindrical projection method to mitigate distortion and reduce the number of partitions. Additionally, we use the rotation of projection cylinders to effectively minimize average angular and areal distortions of the Earth’s landmass and reduce the interruption of continental plates caused by partition edges. By incorporating auxiliary latitudes and proposing an approximate authalic latitude, we further enhance the mapping of the ellipsoid onto the sphere, simplifying calculations. Experimental results demonstrate a substantial reduction in distortion and interruption of continental plates. With only two partitions, an average landmass angular distortion of less than 3.56 degrees and an average normalized surface distortion of less than 1.07 were achieved.

When designing rule-based dialogue systems, the need for elaborate design by the designer is a challenge for dialogue systems. One way to reduce the cost of creating content is to generate utterances from data collected in an objective and reproducible manner. This study focuses on rule-based dialogue systems using survey data, and more specifically on opinion dialogue where the system models the user. In opinion dialogue, there has not been much study of topic transition methods for modeling users while maintaining their motivation for dialogue. To model them, we adopted information content. Our contribution includes the design of a rule-based dialogue system that does not require elaborate design. We also reported an appropriate topic transition method based on information content. This is confirmed by the influence of the user's personality characteristics. The content of questions can give the user a sense of the system's intention to understand them. We also reported the possibility that the system's rational intention contributes to the user's motivation for dialogue with the system.

Lipid nanoparticles have an interesting part of drug delivery system. In this study, the modification of the convention nano-lipid based soybean lecithin was demonstrated. Ginger oil derived Zingiber officinale was used along with lecithin, cholesterol and span 80 to fabricate nano-lipid (GL nano-lipid) using thin-film method. Through TEM and confocal microscope, GL nano-lipid exposes the liposome- like morphology. The average size of the resultant nanoparticles was 249.1nm with monodistribution (PDI= 0.021). The ζ-potential of GL nano-lipid was negative as similar to as prepared nano-lipid based lecithin. GL nano-lipid express the highly stable over 60 days of storage at room temperature in term of size, ζ-potential. A shift of pH value from alkaline to acid was detected in lecithin nano-lipid, while with the incorporation of ginger oil, pH value of nano-lipid dispersion was around 7.0. Furthermore, due to the rich of shogaol-6 and other active compounds in ginger oil, the GL nano-lipid is endowed with intrinsic antibacterial feature. In addition, the sulforhodamine B (SRB) assay and live/dead imaging revealed the excellent biocompatibility of GL nano-lipid. Notably, GL nano-lipid was capable to carry the hydrophobic agents as curcumin and perform a pH-dependent release profile. A subsequent characterization are a suitable potential for drug delivery system.

Proposal: Pineal region cysts (PCs) may affect the tectum and aqueduct and cause deep central vein congestion and endocrine dysfunction. In addition to headaches, PC often causes a broad range of symptoms, leading to prolonged diagnosis and therapy. The aims of this study are to reveal parameters that might explain the ambiguity of the symptoms and to identify factors association with the respiration driven neurofluid preload system. Methods: This retrospective study included 28 paediatric patients (mean age 11.6 years) who received surgical treatment for pineal region cysts and 18 patients (mean age 11.3 years) who were followed conservatively. Multiple clinical patient characteristics, such as symptoms, time to neuroimaging diagnosis, cyst size, ventricular indices, head circumference and postoperative outcome, were analysed. Four patients were investigated for CSF dynamics with real-time MRI. The mean follow-up time was 1.6 years. Results: The most common early onset symptoms were headaches (92%), blurred vision (42.8%), sleep disturbances (39.3%) and vertigo (32.1%). Tectum contact was observed in 82% of patients, and MRI examinations revealed that imaging flow void signals were absent in 32.1% of patients. The mean cyst diameters were 13.7 mm for the axial axis and 15.6 mm for the longitudinal axis. Together with a postoperative flow void signal, 4 patients recovered their respiration-driven CSF upward flow, which was not detectable before OP. After operation in 92.1% of patients, the leading symptoms improved without any mortality or morbidity. Conclusion: Despite proximity to the tectum and aqueduct with frequently absent aqueductal flow void signals, hydrocephalic ventricular enlargement was never detected. Data from real-time MRI depicted a reduced preoperative filling of the ventricular CSF compartments, indicating a diminished fluid preload, which recovered postoperatively.

To determine the ethical-attitudinal profile of university students in the education career during the pandemic with a tendency towards a new normality. University students of the education career were surveyed online, in an ethical key (axiological attitude, moral attitude and attitude towards new normality). The research was descriptive of univariate hypothesis, with non-probabilistic snowball sampling, reaching a sample of 480 participants. The Ethical-Attitudinal Profile Scale (EPEA) was designed, whose reliability was 0.93 in Lawshe's formula and 0.89 in Cronbach's Alpha. In the axiological attitude, values were obtained more frequently: respect with 79%, sincerity with 73%, prudence with 72%. In the moral attitude, a polar result is contrasted between laxity (67%) and kindness (45%); while, in the attitude towards new normality, there was no spike in frequencies, being distributed more homogeneously between indicators, with a low resilience index. There is an adequate evaluative profile of the university students of the education career despite the context of the pandemic and the trend towards a new normality; having a moral attitude of laxity inferred by the circumstances of uncertainty that are experienced in adverse contexts.

The Failure Mode and Effect Analysis (FMEA) is often used to improve a system's reli-ability. This paper proposes a new approach that aims to overcome the most critical defects of the traditional FMEA. This new methodology combines the Entropy and Best Worst Method (BWM) methodology with the EDas and System Dynamics, FMECA: The EN-B-ED Dynamic FMECA. The main innovation’s point of the proposed work is the presence of an unknown factor (Cost), that allows to obtain an objective weighted factor, a risk index when a machine failure occurs. The criticality analysis has been carried out using software (Vensim PLE x64) to simulate System Dynamics models to identify corrective actions and evaluating the possible implementation of these ac-tions. The methodology proposed is applied to a case study in a relevant Italian com-pany in the agri-food sector.

In the first year of its appearance, the 2019 coronavirus disease (COVID-19) has affected more than 150 million individuals and killed 3 million people worldwide. The pandemic has also triggered numerous global initiatives to tackle the newly emerging disease, including the development of SARS-CoV-2 vaccines and the attempt to discover potential pharmacological therapies. Nonetheless, despite the success of SARS-CoV-2 vaccines development, COVID-19 therapy remains challenging. Several repurposed drugs that were documented to be useful in small clinical trials have been shown to be ineffective in larger studies. Additionally, the pathophysiology of SARS-CoV-2 infection displayed the predominance of hyperinflammation and immune dysregulation in inducing multiorgan damage. Therefore, the potential benefits of both immune modulation and suppression in COVID-19 have been extensively discussed. Here, we reviewed the roles of immunomodulation as potential COVID-19 pharmacological modalities based on the existing data and proposed several new immunologic targets to be tested in the foreseeable future.

The entropy of a system gives a powerful insight into its microscopic degrees of freedom, however standard experimental ways of measuring entropy through heat capacity are hard to apply to nanoscale systems, as they require the measurement of increasingly small amounts of heat. Two alternative entropy measurement methods have been recently proposed for nanodevices: through charge balance measurements and transport properties. We describe a self-consistent thermodynamic framework for treating few-electron nanodevices which incorporates both existing entropy measurement methods, whilst highlighting several ongoing misconceptions. We show that both methods can be described as special cases of a more general relation and prove its applicability in systems with complex microscopic dynamics – those with many excited states of various degeneracies.

The purpose of this work is to determine the possibility of using an automated SkyWatcher Virtuoso system to analyze distribution of sky luminance. The article presents the procedure for measuring the sky luminance distribution defined according to CIE using a manual luminance meter cooperating with the SkyWatcher system. The use of matrix luminance meter was also analyzed.

We consider a Mean Field Games model where the dynamics of the agents is given by a controlled Langevin equation and the cost is quadratic. A change of variables, introduced in [13], transforms the Mean Field Games system into a system of two coupled kinetic Fokker-Planck equations. We prove an existence result for the latter system, obtaining consequently existence of a solution for the Mean Field Games system.

this review summarizes current knowledge of the hydrogen sulfide role in cardiovascular system, the proposed mechanisms of its action and the prospects for its applicability in the treatment of cardiovascular diseases. Hydrogen sulfide was recently recognized as gasotransmitter – simple signaling molecule which freely penetrates the cell membrane and regulates a number of biological functions. In humans endogenous H2S is generated via enzymatic and non-enzymatic pathways and its content varies in different tissues and is strictly regulated. In cardiovascular system H2S is produced by myocardial, vascular and blood cells and regulates a number of vital functions. Numerous experimental data prove that endogenously generated as well as exogenously administered H2S exerts a wide range of actions in cardiovascular system, including vasodilator/vasoconstrictor effects, regulation of blood pressure, pro-apoptotic and anti-proliferative effects in the vascular smooth muscle cells, influence on angiogenesis and erythropoiesis, myocardial cytoprotection in ischemia-reperfusion injury, oxygen sensing, inhibition of platelet aggregation and blood coagulation, modification of erythrocyte microrheological properties (aggregability and deformability). Understanding of molecular mechanisms of H2S action and molecular crosstalk between H2S, NO, and CO is essential for the development of its diagnostic and therapeutic potential.

Recently, the application of deep reinforcement learning in recommender system is flourishing and stands out by overcoming drawbacks of traditional methods and achieving high recommendation quality. The dynamics, long-term returns and sparse data issues in recommender system have been effectively solved. But the application of deep reinforcement learning brings problems of interpretability, overfitting, complex reward function design, and user cold start. This paper proposed a tag-aware recommender system based on deep reinforcement learning without complex function design, taking advantage of tags to make up for the interpretability problems existing in recommender system. Our experiment is carried out on MovieLens dataset. The result shows that, DRL based recommender system is superior than traditional algorithms in minimum error and the application of tags has little effect on accuracy when making up for interpretability. In addition, DRL based recommender system has excellent performance on user cold start problems.

Extracorporeal shock wave therapy (ESWT) is a well investigated and widely used treatment modality for a number of musculoskeletal disorders. A limitation of ESWT is its potential painfulness at higher, clinically relevant energy flux density (EFD), which may limit its applicability and, thus, effectiveness. Various studies in the literature demonstrated that neither application of a higher number of extracorporeal shock waves with lower EFD nor use of local anesthesia may solve this problem. Based on the results of several other studies in the literature it is hypothesized here that in patients suffering from musculoskeletal disorders that can be treated with ESWT, pretreatment with a pulsed, high power laser with a wavelength of 904 or 905 nanometers (hereafter: "laser pretreatment") does not only allow to apply higher EFDs in subsequent ESWT but actually results in faster and/or better treatment outcome than ESWT without laser pretreatment. Accordingly, it is hypothesized here that combining ESWT with laser pretreatment leads to synergistic effects and, thus, is superior to either treatment modality alone. Confirming this hypothesis in preclinical and clinical research may raise significance and increase the use of ESWT in physical and rehabilitation medicine, with immediate benefit for patients.

Capacity Expansion Models (CEMs) are optimization models used for long-term energy planning on national to continental scale. They are typically computationally demanding, thus in need of simplification, where one such simplification is to reduce the temporal representation. This paper investigates how using representative periods to reduce the temporal representation in CEMs distorts results compared to a benchmark model of a full chronological year. The test model is a generic CEM applied to Europe, equipped with a novel formulation for storage in model versions with reduced temporal representation. We test the performance of reduced models at penetration levels of wind and solar of 90%. Three measures for accuracy are used: (i) system cost, (ii) total capacity mix and (iii) regional capacity. We find that: (i) the system cost is well represented (~5% deviation from benchmark) with as few as ten representative days, (ii) the capacity mix is in general fairly well (~20% deviation) represented with 50 or more representative days, and (iii) the regional capacity mix displays large deviations (>50%) from benchmark for as many as 250 representative days. We conclude that modelers should be aware of the error margins when presenting results on these three aspects.

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.