Background: Diabetes mellitus (DM) and osteoporosis are two of the most widespread metabolic disease in the world. The aim of this study is to investigate the prevalence of DM among patients affected by osteoporosis and fragility fractures, and to search differences in clinical features. Methods: 713 patients belonging to CTO Bone Unit because of osteoporosis and fragility fractures were divided into two groups, according to the diagnosis of DM. The clinical and bone characteristics of patients were compared. Results: Prevalence of DM was 10.8%. When compared to patients without DM, the median age at the time of first fracture was similar (72 years ± 14 IQR vs 71 years ± 12 IQR); prevalence of combination of vertebral and hip fractures was higher (p=0.039), as well as prevalence of males, although not significantly (p=0.051). Bone mineral density (BMD) at all sites was higher in DM group; Trabecular Bone Score (TBS), instead, was significantly lower (p<0.001). Conclusions: Patients with fragility fractures and DM more frequently show combination of major fractures with higher BMD levels. In these patients, TBS could be a better indicator of bone health than BMD and therefore should be used as a diagnostic tool in clinical practice.

The design and operation of quantum networks are both decisive in the current push towards a global quantum internet. Although space-enabled quantum connectivity has already been identified as a beneficial candidate for long-range quantum channels for over two decades, the architecture of a hybrid space-ground network is still a work in progress. Here, we propose an analysis of such a network based on a best-path approach, where either fiber- or satellite-based elementary links can be concatenated to form a repeater chain. The network consisting of quantum information processing nodes, equipped with both ground and space connections, is mapped into a graph structure, where edge weights represent the achievable secret key rates, chosen as the figure of merit for the network analysis. A weight minimization algorithm allows identifying the best path dynamically, i.e., as the weather conditions, stray light radiance, and satellite orbital position changes. From the results we conclude that satellite links will play a significant role on the future large-scale quantum internet, and both a constellation of satellites and significant advances in filtering technology are required to fulfill continuous coverage.

When wastewater containing methylene blue, a cationic dye is improperly treated or immediately dumped into the environment, it can degrade the quality of water bodies and contaminate the ecological environment. The removal of methylene blue from the aqueous solution is critical and urgent. The present work focuses on the use of maize stalk agricultural waste to prepare an adsorbent for the removal of cationic dye (methylene blue) from an aqueous solution. The prepared maize stalk biochar (MSB) was modified with MnO2 and the modified maize stalk biochar was characterized using Fourier transform infrared (FTIR) spectroscopy. Taguchi technique was employed to determine the optimum conditions for the removal of methylene blue (MB) dye from aqueous solution by MMSB. The factors were adsorbent dose (0.5, 1.0 and 1.5 g/100 mL), initial dye concentration (100, 200 and 300 mg/L), contact time (30, 60, 90 minutes) and pH (4, 6 and 8). Among the investigated process factors, adsorption efficiency was influenced by adsorbent dose and initial MB dye concentration. The optimum parameters were 1 g/100 mL adsorbent dose, 100 mg/L initial dye concentration, 60 minutes contact time and a pH of 8.0. This yielded a removal efficiency of 87.49% with adsorption capacity of 206.53 mg/g. The adsorption kinetics agreed with the pseudo-second-order (R2 of 0.999968) model and fitted a Langmuir isotherm (R2 of 0.9795) representing homogeneous monolayer chemisorption.

Plasma generation by means of electrical discharge requires specialized power supply systems. The applicability of plasma for various plasma processes depends on its parameters, and these in turn depend on the parameters of the power supply systems. Arc plasma can be unstable, generating a lot of electromagnetic interference and overvoltage and overcurrent. The power system of a plasma reactor must guarantee good plasma control characteristics, be immune to disturbances and ensure good cooperation with the power grid. The article analyzes the cooperation of a three-phase plasma reactor, with gliding arc discharge, with a power supply system of a new type. This system integrates an AC/DC/AC converter with a five-column transformer of special design in a single device. Using the properties of magnetic circuits, it was possible to integrate the functions of ignition and sustaining the burning of the discharge in the reactor in a single transformer. Proper design of the transformer is crucial to achieve good cooperation of the AC/DC/AC converter with both the plasma reactor and the power supply network. The presented power supply design shows a number of positive features predisposing it to powering arc plasma reactors.

Artificial Intelligence (AI) has many advantages over humans in that it can detect incredibly subtle patterns within large quantities of data. This study suggests using AI algorithms in user research tasks for mining variables ranging from the tone of voice, image banks, historical records, and product use to determine where brands sit and where there could be competitive advantages. This study reviews the current design processes of User Experience (UX) designers and the advancements in Deep Learning (DL). It identifies potential areas of automation in the Preparation, Incubation and Illumination stages of the design process. It recognises the possibility of using Long Short-Term Memory (LSTM) models to automate design feedback and the creation of alternate designs with Generative Adversarial Networks (GANs).

Analysing and understanding donor behaviour in Non-profit Organisations (NPOs) is challenging due to the lack of human and technical resources. Machine learning (ML) techniques can analyse and understand donor behaviour at a certain level; however, it remains to be seen how to build and design an Artificial Intelligence enabled Decision Support System (AI-enabled DSS) to analyse donor behaviour. Thus, this paper proposes an AI-enabled DSS conceptual design to analyse donor behaviour in NPOs. A conceptual design is created following a Design Science Research approach to evaluate an AI-enabled DSS's initial DPs and features to analyse donor behaviour in NPOs. The evaluation process of the conceptual design applied formative assessment through conducting interviews with stakeholders from NPOs. The interviews were conducted using the Appreciative Inquiry framework to facilitate the process of interviews. The results of analysis based on the interviews provide insightful information not only about the proposed conceptual design for an AI-enabled DSS, but also about what is required to analyse donor behaviour NPOs using DSS. Evaluating the conceptual design results recommend efficiency, effectiveness, flexibility, and useability in the requirements of the AI-enabled DSS. This research contributes to the design knowledge base of AI-enabled DSS for analysing donor behaviour in NPOs. Future research will combine theoretical components to introduce a practical AI-enabled DSS for analysing donor behaviour in NPOs. This research is limited to such analysis on donors who donate money or volunteer time for NPOs.

(1) Background: This study aims to validate the use of Bloom's revised taxonomy as an instrument for the design of assessment tests; (2) Methods: A validation has been carried out by external judges, as well as by teachers and students, validating the instrument by means of Aiken's V; (3) Results: Judges, teachers and students consider Bloom's revised taxonomy as an effective tool for the design of assessment tests; (4) Conclusions: Using Bloom's revised taxonomy as a model for designing assessment tests promotes learning.

Nowadays brushless DC motors (BLDCMs) are becoming indispensable components as the electrification revolution in the mobility industry is happening. Electric kick scooters, so-called e-scooters, are among these micro-mobility vehicles which are powered by these motors. Due to the uncertain and nonlinear features, the controller performance developed for these motors degrades. For these reasons, a chattering-reduced cascaded Sliding Mode Control (SMC) scheme to effectively track reference motor speed in the outer loop by eliminating torque ripples in the inner loop current control was designed. Field-oriented Control (FOC) methodology was used to implement the SMC in the BLDCM. An exponential reaching law algorithm was proposed for sliding surfaces of the inner and outer loop controllers. The suitability and performance of electric scooter-hub motors were analyzed in terms of traction control. A cascaded speed and torque controller produced significantly favorable results representing minimized torque and current ripples, and operation over a wide speed range.

Global crop yields are estimated to be reduced by 30–40% per year on account of plant pests and pathogens. Agricultural insect pests raise concerns about constraining global food security and climate changes contributing to the rise of infestation. The current management relies on plant breeding, associated or not with transgenes and chemical pesticides. Both approaches face serious technology obsolescence on the field due to resistance breakdown or development of insecticide resistance. The need for new Modes of Action (MoA) approaches in managing crop health grows each year, driven by market demands to reduce economic losses and phytosanitary requirements to meet the consumer perception. Disabling pest genes by sequence-specific expression silencing is considered a promising tool in the development of environment and health respectful biopesticides. The specificity conferred by long dsRNA-base solutions give support to minimizing effects on off-targeted genes in the insect pest genome and the target gene in non-target organisms (NTOs). In this review, we summarize the current status of gene silencing by RNA interference (RNAi) for agricultural control. More specifically, we focus on the engineering, development and application of gene silencing to control Lepidoptera by the employment of non-transforming dsRNA technologies. Despite some delivery and stability drawbacks of topical applications, we reviewed works showing convincing proof-of-concept results that point to imminent innovative solutions. Considerations about the regulamentation of the ongoing research on dsRNA-based pesticides to produce commercialized products for exogenous application are discussed. Academic and industry initiatives reveal a worthy effort to accomplish controlling Lepidoptera pests with this new mode of action to provide more sustainable and reliable technologies to field management. New data on genomics of this taxon encourage the increment of a customized target genes portfolio. As a case of study, we illustrate how dsRNA and associated methodologies could be applied to control an important Lepidopteran coffee pest.

Plasmodium falciparum dihydroorotate dehydrogenase (PfDODH) is one of the enzymes currently explored in the treatment of malaria. Although there is currently no clinically approved drug targeting PfDODH, many of the compounds in clinical trials have [1, 2, 4,] triazolo [1, 5-a] pyrimidin-7-amine backbone structure. This study sought to design new compounds from the fragments of known experimental inhibitors of PfDODH. Nine experimental compounds retrieved from Drug Bank online were downloaded and broken into fragments using Schrodinger power shell; the fragments were recombined to generate new ligand structures using BREED algorithm. The new compounds were docked with PfDODH crystal structure, after which the compounds were filtered with extensive drug-likeness and toxicity parameters. A 2D-QSAR model was built using the multiple linear regression method and externally validated. The compounds electronic behaviours were studied using DFT calculations. Structural investigation of the six designed compounds, which had lower binding energies than the standard inhibitors, showed that five of them had [1, 2, 4,] triazolo [1, 5-a] pyrimidin-7-amine moieties and interacted with essential residues at the PfDODH binding site. In addition to their drug-like and pharmacokinetic properties, they also showed minimal toxicities. The externally validated 2D-QSAR model with R² and Q² values of 0.6852 and 0.6691, confirmed the inhibitory prowess of these compounds against PfDODH. The DFT calculations showed regions of the molecules prone to electrophilic and nucleophilic attack. The current study thus provides insight into the development of a new set of potent PfDODH inhibitors.

Top performance in team sports attracts the attention from the general public. In particular, the best players became incredibly skilled and physically powerful, a fact that potentiates to deliver a product considered attractive, exciting and competitive. Not surprisingly, this is a very valuable product from an economic and social standpoint, thus, all sports professionals are extremely interested in developing new procedures to improve sports performance. Besides, the great interests of the various stakeholders (owners, CEO-s, agents, fans, media, coaches, players, families and friends) are one of the main reasons for this development of sports science umbrella and the accompanying sports industry. all their personal performances should be coordinated and put into function by the sports team. In this scientific and applied manuscript, we will deal primarily with the individual treatment of players in order to improve their personal performance and, consequently, team sport performance.

The Umbulan Water Supply Project is categorized by the Shipping Infrastructure Acceleration Committee in the list of Accelerated National Strategic Projects through Presidential Regulation Number 3 of 2016 concerning the Acceleration of the Implementation of National Strategic Projects, targeted to operate in mid-2019. This is what will be the focus of the stakeholders of the Umbulan Water Supply Project. This study was to identifying and analyzing networks among stakeholders. Method: This study used a qualitative approach with exploratory methods combined with meta-analysis identification design Identification of stakeholder mapping in the context of early detection of stakeholder involvement in the implementation of the Umbulan Water Supply Project at various levels starting from the National, Provincial (East Java), District/City (Pasuruan, Sidoarjo, Surabaya, to Gresik), Sub-District (Winongan, Gondang Wetan, and Pohtjentrek). The conclusion of this study was based on in-depth interviews and focus group discussions in describing the determination of stakeholders which were divided into two, namely primary stakeholder and secondary stakeholder, and outline the result of the indicators analysis on the stakeholder network of Umbulan Water Supply Project.

Progressive technologies and practices are shifting the possibilities of building design and improving work efficiency. Constantly changing site conditions require different procedures and designs that take into account these changing conditions, whether it is a design solution, a change in environmental conditions, or just sustainability factors. Adaptive building design offers opportunities to cope with changing factors to achieve the highest possible level of building quality. This case study deals with the topic of adaptive formwork design for building renovation, taking into account sustainability. Aim of the article is an investigation and demonstration of the building information modelling (BIM) environment used for the adaptive design of formwork elements for the building renovation in the context of sustainability. The object of the case study is a building in the center of Kosice, Slovakia. BIM environment allows prompt and correct adaptation of the formwork design to changing conditions of lighting, ventilation, heating and temperature during the design of the building.

Mass timber products, together with careful forestry management, could help decarbonize the construction industry. These products must be long-lasting, to safely store atmospheric carbon for decades or centuries, and multi-functional, to displace materials and equipment that are emissions-intensive. This paper shows how to optimize mass timber panels as heat-exchangers, suggesting how to eliminate insulation while simplifying HVAC systems. Test panels measured the heat-exchange in steady and transient conditions, when the ventilation was driven by a fan or by thermal buoyancy. The total heatexchange was predicted accurately by theory in all cases. Further investigation is needed to understand the possible heat-recovery effects at the exterior surface.

In this report, we demonstrate that it is possible to design epitope-based peptide vaccine candidates to counteract the novel China coronavirus (2019-nCoV) by using an approach similar to the one used in cancer neoantigen vaccination therapy. We identified multiepitope peptide vaccine candidates against 2019-nCov that can potentially trigger both CD4⁺ and CD8⁺ T cell immune response with increased efficiency due to the presence of CD4⁺ and CD8⁺ T cell epitopes and a cathepsin-sensitive linker. Furthermore, we suggest that the peptide design strategy should incorporate population-specific HLA alleles in order to optimize binding specificity of the peptides. We refer to this as populationalized vaccinomics.

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

Homogeneous water dispersions of MWCNTs were prepared by ultrasonication in the presence of an amphiphilic polystyrene-block-poly(acrylic acid) (PS-b-PAA) copolymer. The ability of PS-b-PAA to disperse and stabilize MWCTNs was investigated by UV-vis, SEM and zeta potential. It is shown that the copolymer can disperse nanotubes directly by sonication in water. The results show that the addition of a styrene block to PAA enhances the dispersion efficiency compared to pure PAA, possibly due to the nanotube affinity with the polystyrene moiety. Notably, the dispersions show an evident pH-responsive behavior, being MWCNTs reaggregation promoted in basic environment. Furthermore, composites obtained by drop casting display electrical conductivity responsive to pH variations, showing the potential of such materials for sensing applications.

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. An accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100–1000 fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria results in more drugs that could be validated in the biomedical literature than the list suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.

This paper describes the feasibility and probable benefits associated with greening the Tahliah Channel, a concrete drainage channel that was originally built to relieve urban flooding in Jeddah City, Saudi Arabia. It includes an estimation of irrigation needs for channel greening based on a standardized planting specification. The study also demonstrates alternative strategies for meeting the required irrigation demand, including water harvesting and graywater reuse on a residential scale. The study shows that greening Tahliah Channel is possible relying mainly on graywater reuse from the surrounding buildings. Also, the study shows that rainwater harvesting is not a reliable source for irrigation. Rather, it can cover only part of the irrigation needs (6%) and so can be used as a secondary supporting source. The positive results of this case study will be of interest to those in arid countries who are looking to upgrade and replace traditional, single function drainage infrastructure with more sustainable, green infrastructure systems. More specifically, the objectives of the study are consistent with the goals of the Saudi government’s ongoing initiative that advocates for more resilient and sustainable cities. (Vision 2030 year).

Nowadays, there are several electric vehicle (EV) on the market, due to the innovation of technology that promotes the new components such as battery, transmission, electric motors. This paper proposes a design approach for the configuration synthesis and simulation of the in-wheel-hub motor transmissions with the six-link mechanisms. The synthesis process shows 6 mechanisms with six members and eight joints, 15 new clutchless motor transmissions and 16 new clutched motor transmissions. A novel motor transmission in the feasibility of the synthesized configurations is selected as an example to analyze the working principle with operation modes and power flow paths. And, this design is modeled for the simulation process that generates the results of operation mode transition and energy regulation.

Polyurethanes are materials with a strong structure-property relationship. The goal of this research was to study the effect of a polyol blend composition of polyurethanes on its properties using a mixture design and setting mathematic models for each property. Water absorption, hydrolytic degradation, contact angle, tensile stretch, hardness and modulus were studied. Additionally, Thermal stability was studied by thermogravimetric analysis. Area under the curve was used to evaluate the effect of polyol blend composition on thermal stability and kinetics of water absorption and hydrolytic degradation. Least squares were used to calculate the regression coefficients. Models for the properties were significant, and lack of fit was not (P<0.05). Fit statistics suggest both good fitting and prediction. Water absorption, hydrolytic degradation and contact angle were mediated by the hydrophilic nature of the polyols. Tensile strength, modulus and hardness could be regulated by the molecular weight and hydroxyl index of the polyols. Regression of DTG curves from thermal analysis showed improvement of thermal stability with the increase of PCL and PE. An ANOVA test of the model terms demonstrated that three component effects on bulk properties like water absorption, hydrolytic degradation, hardness, tensile strength and modulus, and the PEG*PCL interaction with the contact angle, which is a surface property. Mixture design application allowed for an understanding of the structure-property relationship through mathematic models.

The paper presents the design stages of a single-phase Silicon Carbide bidirectional DC-AC converter. This includes the LCL filter design responsible to meet grid connection requirements. A 3kW laboratory prototype of the power converter is built employing a low-cost phase locked loop and its results are presented. The design of the low-cost phase locked loop and its implementation are depicted in some detail.

In this paper, several classes of rough Marcinkiewicz integral operators along surfaces of revolution on product spaces are investigated. We prove the Lp boundedness of these operators when their kernels functions belong to a class of functions related to a class of functions introduced by Grafakos-Stefanov. The results in this paper extend and improve several known results on Marcinkiewicz integrals.

In recent decades, the creation and availability of Voluntary Geographic Information (VGI) has changed the paradigm associated with the production of Geospatial Information (GI), since due to its free access, citizens can view, analyse, process, and validate this type of data. One of the most popular examples of VGI is the collaborative OpenStreetMap (OSM) project which covers a wide range of themes or characteristics associated with the real world. One of these themes is the feature “waterway” that represents watercourses. The quality of OSM data characteristics is an issue that has been published by many authors in recent years, particularly on the analysis of the completeness indicator. However, few references are found in literature to studies that analyse the completeness of OSM watercourses. This study presents an analysis of the variation over time in completeness/coverage of the OSM "waterway" feature in the epochs of 2014 and 2023 in a mountainous region included in the Mondego River basin, located in Inland of Portugal. The results show that the completeness of this OSM feature (waterway) has a slight increase, considering the amplitude of this period (nine years) and the fact that, nowadays, digital mobile devices enable access to satellite images, therefore allowing in an easy way the digitalization of entities or objects of the real world remotely. Regarding the indicator’s altitude, slope and location/proximity of settlements, it was not possible to observe their influence on the evolution of the completeness of this geographic entity (OSM waterway).

Fire is a serious security threat that can lead to casualties, property damage, and environmental damage. However, despite the availability of object detection algorithms, challenges persist in detecting fires and smoke. These challenges include slow convergence speed, poor performance in detecting small targets, and high computational cost limiting deployments. In this paper, Fire smoke and human detection based on ConvNeXt and Mixed encoder (FCM-DETR), an end-to-end object detection algorithm based on Deformable DETR, is proposed. Firstly, we introduce ConvNeXt to take the place of Resnet, which greatly reduces the amount of computation and improves the ability to extract irregular flame features. Secondly, to effectively process multi-scale features, the original encoder is decoupled into two modules. Then Mixed encoder, an innovative encoder structure, is proposed, resulting in excellent performance of multi-scale fire and smoke features fusion. What can’t be overlooked is that the encoder block is compatible with any DETR-based models. Finally, the convergence speed is accelerated and the mean of average precision is improved by applying a novel loss function called Powerful IoU v2. The experimental results indicate that our model achieves the best detection accuracy compared to other models, with mAP reaching 66.7%, mAPs and Accuracyfire achieving impressive 50.2% and 98.05%, respectively.

In this paper, we consider a fourth-order suspension bridge equation with nonlinear damping term |u_t|^m-2u_t and source term |u|^p-2u. We give necessary and sufficient condition for global existence and energy decay results without considering the relation between m and p. Moreover, when p>m, we give sufficient condition for finite time blow-up of solutions. The lower bound of the blow-up time T_max is also established. It worth to mention that our obtained results extend the recent results of Wang (J. Math. Anal. Appl., 2014) to the nonlinear damping case.

In order to correct attenuated millimeter-wavelength (Ka-band) radar data and address the problem of instability, an attenuation correction methodology (attenuation correction with variation trend constraint; VTC) was developed. Using synchronous observation conditions and multi-band radars, the VTC method adopts the variation trends of reflectivity in X-band radar data captured with wavelet transform as a constraint to adjust reflectivity factors of millimeter-wavelength radar. The correction was evaluated by comparing reflectivities obtained by millimeter-wavelength cloud radar and X-band weather radar. Experiments showed that attenuation was a major contributory factor in the different reflectivities of the two radars when relatively intense echoes exist, and the attenuation correction developed in this study significantly improved data quality for millimeter-wavelength radar. Reflectivity differences between the two radars were reduced and reflectivity correlations were enhanced. Errors caused by attenuation were eliminated, while variation details in the reflectivity factors were retained. The VTC method is superior to the bin-by-bin method in terms of correction amplitude and can be used for attenuation correction of shorter wavelength radar assisted by longer wavelength radar data.

Acid-producing bacteria in pit mud play a crucial role in the fermentation of Chinese liquor, significantly influencing its flavor, taste, and overall quality. This study investigates three strains of anaerobic acid-producing bacteria from pit mud to optimize both single and combined bacterial fermentations for enhanced acid production. Acid production levels were quantitatively measured using gas chromatography-mass spectrometry. The Design Expert software was employed to identify significant nutritional factors affecting the production of hexanoic and butyric acids. These factors were optimized through climbing tests and response surface analysis. The Box-Behnken Design (BBD) experimental design and variance analysis were used to determine the optimal levels of each significant factor. The SJ-1/SJ-3/SJ-8/enriched solution exhibited the highest acid production, with a total acid production of 2216.07±0.10 mg/100 ml. The yields of caproic acid and butyric acid were 801.94±0.11 mg/100 ml and 1167.33±0.14 mg/100 ml, respectively. The optimized fermentation medium for caproic and butyric acid production consisted of 5.1 g/L peptone, 5.04 g/L sodium acetate, and 4.45 g/L sodium butyrate, with a pH of 6.4. Under these conditions, the production of caproic acid and butyric acid reached 1172.73 mg/100 ml and 2382.45 mg/100 ml, respectively. These values represent significant increases (P<0.05) of approximately 46.24% for caproic acid and 104.09% for butyric acid compared to initial levels. The model predicts caproic and butyric acid production to be 1184.21 mg/100 ml and 2387.84 mg/100 ml, respectively, which closely aligns with experimental results. This indicates the model’s effectiveness and provides valuable insights for enhancing acidity in liquor production.

Background: Obesity remains a widely debated issue, often criticized for the limitations in its identification and classification. This study aims to compare two distinct systems for classifying obesity: Body Mass Index (BMI) and body fat percentage (BFP) as assessed by bioelectrical impedance analysis (BIA). By examining these measures, the study seeks to clarify how different metrics of body composition influence the identification of obesity-related risk factors. Methods: The study enrolled 1255 adults, comprising 471 males and 784 females, with a mean age of 36 ± 11.90 years. Participants exhibited varying degrees of weight status, including optimal weight, overweight, and obesity. Body composition analysis was conducted using the TANITA Body Composition Analyzer BC-418 MA III device (T5896, Tokyo, Japan), evaluating the following parameters: current weight, basal metabolic rate (BMR), adipose tissue (%), muscle mass (%), and hydration status (%). Results: Age and psychological factors like cravings, fatigue, stress, and compulsive eating were significant predictors of obesity in the BMI model but not in the BFP model. Additionally, having a family history of diabetes was protective in the BMI model (OR: 0.33) but increased risk in the BFP model (OR: 1.66). The BMI model demonstrates exceptional predictive ability (AUC=0.998). In contrast, the BFP model, while still performing well, exhibits a lower AUC (0.975), indicating slightly reduced discriminative power compared to the BMI model. Conclusions:BMI classification demonstrates superior predictive accuracy, specificity, and sensitivity. This suggests that BMI remains a more reliable measure for identifying obesity-related risk factors compared to the BFP model.

Beta thalassemia is an inherited blood disorder that results in inefficient erythropoiesis due to genetic mutation that leads to reduction or absence of the hemoglobin beta-globulin protein. Approximately 8.5% of UAE residents suffer from β-thalassemia, a significant health and financial problem. The treatment options available for β-Thalassemia major are limited and associated with a wide range of complications. β-thalassemia gene therapy is emerging as a potential novel treatment option that eliminates the complications caused by the current long-term treatment modalities and the associated economic burden. This paper reviews the scientific literature related to emerging gene-therapy for β-Thalassemia by analyzing all the articles published from January 2010 to December 2023 in the English language on Databases like PubMed, Scopus, ProQuest, and CINAHL. The use of gene therapy has demonstrated promising outcomes for a permanent cure of β-Thalassemia. ZYNTEGLO (Betibeglogene autotemcel) is the only cell-based gene therapy which was approved by the FDA on 19th August, 2018. To conclude, Gene therapy is an innovative solution. It demonstrates a promising future, but does come with its own set-backs and is something that must be tackled in-order to revolutionize it in the medical world. FDA approved ZYNTEGLO is a potentially one-time curative treatment for β-Thalassemia. Although cutting-edge, its use is limited because of the high cost; a price of $2.8million per patient.

Introduction: A plethora of biological molecules regulate chondrogenesis in the epiphyseal growth plate. Disruptions of quantity and function of these molecules can manifest clinically as stature abnormalities of various etiologies. Traditionally, the growth hormone/insulin-like growth factor 1 (IGF1) axis represents the etiological center of final stature attainment. Of note, little is known about the molecular events that dominate the growth of the craniofacial complex and its correlation with somatic stature. Aim: Given the paucity of the relevant data, this review discusses available information regarding potential applications of lateral cephalometric radiography as a potential clinical indicator of genetic short stature in children. Materials and Methods: A literature search was conducted in the PubMed electronic database using the key words: cephalometric analysis and short stature; cephalometric analysis and achondroplasia; cephalometric analysis and hypochondroplasia; cephalometric analysis and skeletal abnormalities; cephalometr* and SHOX; cephalometr* and CNP; cephalometr* and ACAN; cephalometr* and CNVs; cephalometr* and IHH; cephalometr* and FGFR3; cephalometr* and Noonan syndrome; cephalometr* and “Turner syndrome”; cephalometr* and achondroplasia. Results: In individuals with genetic syndromes causing short stature, linear growth of the craniofacial complex is confined, following the pattern of somatic short stature regardless of its etiology. The angular cephalometric measurements differ from the average height normal individuals and are suggestive of a posterior placement of the jaws and a vertical growth pattern of the face. Conclusion: The greater part of the existing literature regarding cephalometric measurements in short statured children with genetic syndromes provides qualitative data. Furthermore, cephalometric data for individuals affected with specific rare genetic conditions causing short stature, should be the focus of future studies. These quantitative data are required to potentially establish cut-off values to refer for genetic testing based on craniofacial phenotypes.

Volatile organic compounds (VOCs) released from plants have been correlated with disease-status. Analysis of VOCs using GC-MS is time-consuming, laboratory-based, and requires specialist training. Electronic nose devices (E-nose) provide a portable alternative. Three different E-nose devices were compared to assess how accurately they could detect Mango Twig Tip Dieback and Panama disease in banana. The devices were initially trained on known volatiles, then pure cultures of Pantoea sp., Staphylococcus sp., and Fusarium odoratissimum, and finally, on infected and healthy mango leaves and field-collected, infected banana pseudo-stems. The experiments were repeated three times with six replicates for each host-pathogen pair. The variation between healthy and infected host materials was evaluated by principal component and linear discriminant analysis, cross-validation and chemometric data analysis. GC-MS analysis was conducted contemporaneously and identified an 80% similarity between healthy and infected plant material. The portable C 320 was 100% successful in discriminating known volatiles but had a low capability in differentiating healthy and infected plant substrates. The advanced devices (PEN 3 / MSEM 160) successfully detected healthy and diseased samples with a high variance. The results suggest that E-nose devices are more sensitive and accurate in detecting changes of VOCs between healthy and infected plants compared to headspace GC-MS.

The purpose of this study is to examine the match-related performance (scores and penalties) of the athletes in the Junior EJC (European Judo Championship) and U23 EJC categories before and immediately after the Covid-19 quarantine, according to gender and weight category groups. Our sample was based on the junior and U23, male and females, 2019-2020 Junior European Judo Championships and 2019-2020 U23 European Judo Championships. A total of 1431 matches in these tournaments were analyzed: male (n=812) and female (n=619). The main findings of this study showed that most matches in both the male and female categories resulted in ippon (2019-2020 average: 64,35% for Junior male, 70,55% for U23 male and 69,3% for Junior female, 64,4% for U23 female). No significant differences (p &gt; 0.05) were observed between gender and age groups concerning the match scores. In the formation phase of the technique, considerations of high- and low-intensity actions, and analysis of the speed of athletes during matches in official competitions can help to better understand the dynamics of the variability of movements in judo matches. In addition, suggestions are presented to be applied to athletes in different age categories who are on the way to become Olympic athletes.

Panax notoginseng is a traditional Chinese medicine, and saponins are its main active ingredients. The content of saponins in flower of P. notoginseng is the highest in the whole plant. Minor ginsenosides have stronger pharmacological activity than main ginsenosides, but its content in nature is very low. We can use extracted total saponins from P. notoginseng flower as the substrate to produce minor ginsenosides by biotransformation method. In this study, we used the fungus Talaromyces flavus and its crude enzymes to transform the extracted total saponins of P. notoginseng flower. Through the qualitative and quantitative analysis of the transformation substrates and products, it was found that minor ginsenosides Gyp-XVII, ginsenosides C-Mc1, C-Mx1, F2, Rg3, 20(R)-Rg3, Rk1 and Rg5 were found in the products and the conversion rate of the major saponins in P. notoginseng flower ranged from 44.16 to 55.22%, the yields of the minor ginsenosides ranged from 0.05 to 3.24%. This study provided a good experimental idea, greatly increased the utilization rate of total saponins in P. notoginseng flower, and laid a foundation for promoting the wide application of minor saponins.

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

Evapotranspiration (ET) is a major component of water budget in Bottomland Hardwood Forests (BHF) and is driven by a complex intertwined suite of meteorological variables. The understanding of these interdependencies leading to seasonal variations in ET is crucial in better informing water resource management in the region. We used a structural equation modeling approach to analyze the drivers of ET using Eddy covariance water and heat flux data collected from a BHF, located in the Russel Sage Wildlife Management Area (RSWMA). It consists of mature closed-canopy deciduous hardwood trees with an average canopy height of 27 m. Factor analysis was used to characterize the shared variance among drivers and path analysis was used to quantify independent contributions of individual drivers. In our results, ET and net radiation (Rn) showed similar variability patterns with Vapor Pressure Deficit (VPD) and temperature in Spring, Summer, and Autumn seasons while they differ in Winter season. The path analysis shows that Rn has the strongest influence on ET variations via direct and indirect pathways. In the deciduous forests like BHFs, our results suggest ET is more energy-dependent during growing season (Spring and Summer) and early non-growing season (Autumn) while more temperature-dependent during winter season.

Transportation authorities aim to boost road safety by identifying risky locations and applying suitable safety measures. The Highway Safety Manual (HSM) is a vital resource for US transportation professionals, aiding in the creation of Safety Performance Functions (SPFs), which are predictive models for crashes. These models rely on Negative Binomial distribution-based regression and misinterpreting them due to unmet statistical assumptions can lead to erroneous conclusions, including inaccurately assessing crash rates or missing high-risk sites. The Florida Department of Transportation (FDOT) has introduced context classifications to HSM SPFs, complicating assumption violation identification. This study, part of an FDOT-sponsored project, investigates established statistical diagnostic tests to identify model violations and proposes a novel approach to determine optimal spatial regions for Empirical Bayes adjustment. This adjustment aligns HSM-SPFs with regression assumptions. The study employs a case study involving Florida roads. Results indicate that a 20-mile radius offers an optimal spatial sample size for modeling crashes of all injury levels, ensuring accurate assumptions. For severe injury crashes, which are less frequent and harder to predict, a 60-mile radius is suggested to fulfill statistical modeling assumptions. This methodology guides FDOT practitioners in assessing the conformity of HSM-SPFs with intended assumptions and determining appropriate region sizes.

During the development of horizontal wells in bottom water reservoirs, the strong heterogeneity of reservoir permeability leads to premature bottom water breakthrough at locations with high permeability in the horizontal wellbore, and the water content rises rapidly, which seriously affects production. In order to cope with this problem, a new technology has emerged in recent years that utilizes gravel filling to block the flow in the annulus between the horizontal well and the borehole, and utilizes the Inflow Control Device (ICD) completion tool to carry out segmental water control in horizontal wells. Unlike conventional horizontal well ICD completions that use packers for segmentation, gravel pack combined with ICD completions breaks the original seg-mentation routine and increases the complexity of production dynamic simulation. In this paper, the flow in different spatial dimensions such as reservoir, gravel packed layer, ICD completion section and horizontal wellbore is modeled separately, and the annular pressure at different locations is used as the solution variable for the coupled solution, which realizes the prediction of oil production, water production and water content of gravel pack combined with ICD completions of horizontal wells. The model was used to calculate the effects of different crude oil viscosities, different reservoir permeabilities, different permeabilities of gravel packed layers, and different development stages on the water control effects of gravel pack combined ICD completions and conventional ICD completions at field conditions. This study provides a basis for using gravel pack combined ICD completions in horizontal wells in bottom water reservoirs.

Conservation of tropical endemic amphibians largely suffers from Wallacean shortfall, a gap to which predictive species distribution models have contributed significantly to bridge by delineating probable distribution and the underlying suitable habitat within their distributional range. However, rarely is a prediction model ground-truthed to evaluate their predictive performance. Here we present a species distribution modeling approach using maximum entropy algorithm corrected for smaller sample size, in guiding explorative surveys aimed at optimizing survey effort and discovering unrecorded populations of Zhangixalus suffry, a rhacophorid tree-frog endemic to the northeastern part of the Indian subcontinent along with the factors limiting their distribution. With only 16 established historical locality data to model for (after spatial thinning to reduce autocorrelation) and a set of environmental predictors (climatic, topographic, and landscape composition), our model prediction enabled the successful discovery of seven new population records from unreported landscapes, extending its southernmost distributional limit over a considerable distance. The final composite distribution model combining all the locality records (n=23) predicted similar core areas of suitable habitat consistent with the known geographic distribution of the species but showed poor representation under existing coverage of Protected Area (PA) network in the Region with only 7% representation of suitable habitat under protection. Habitat suitability of a site was significantly governed by factors related to precipitation (precipitation seasonality and precipitation of the warmest quarter) and topographic factors that can influence it (elevation and aspect). This corroborates with the known ecology of Rhacophorid frogs, especially concerning their seasonal explosive reproductive strategy and foam nest-building behavior.Through this study, we propose explorative surveys guided by species distribution models to expedite unknown population discovery of rare, tropical endemic amphibians and using such taxa as surrogates in identifying conservation priority zones that can be directly applied to reserve design and conservation and management planning.

Magnetorheological elastomers (MREs) are a type of smart material that can change their mechanical properties in response to external magnetic fields. These unique properties make them ideal for various applications, including vibration control, noise reduction, and shock absorption. This paper presents an approach for modeling the impact behavior of MREs. The proposed model uses a combination of exponential functions arranged in a multi-layer Preisach model to capture the nonlinear behavior of MREs under impact loads. The model is trained using particle swarm optimization (PSO) and validated using experimental data from drop impact tests conducted on MRE samples under various magnetic field strengths. The results demonstrate that the proposed model can accurately predict the impact behavior of MREs, making it a useful tool for designing MRE-based devices that require precise control of their impact response. The model's response closely matches the experimental data, with a maximum prediction error of 10% or less. Furthermore, the interpolated model's response shows good agreement with the experimental data, with a maximum percentage error of less than 8.5%.

The developments of confirmed daily Sars-CoV-2 infections can be modeled a posteriori in a comparatively simple and satisfactory way by means of a special exponential function: its exponential coefficient varies exponentially with time itself. This property enables the function to directly simulate the „daily case“ curve fragments observed for the first waves of the current epidemics in a number of counties and countries. Linear combinations of two or more of the functions allow for the modeling of the complete curves observed in the vast majority of all regions for which case number developments have been tabulated.

Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America, and to enhance the utility of drought monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impacts information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and the colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and the warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and indicators are changing over time as the climate warms, and by recommending ways to strengthen the use of indices and indicators in drought decision-making.

The present study deals with the transplantation of mature Argan trees (Argania spinosa L. Skeels) and focuses on optimizing survival rates depending on the seasons, the type of shoot pruning applied to the trees and preconditioning treatments consisting of root pruningand watering for a few months before transplanting. So far, the transplantation of this species at the adult stage has not received enough attention in spite of its ecological and socio-economic importance for the regions where it thrives. The results obtained show that transplanting in summer, autumn and winter resulted in survival rates exceeding 50%. The Argan trees transplanted in spring hardly achieved 10% survival. However, the total cutback of the crown induced survival improvement that reached a rate greater than 70% for summer, autumn and winter, and 50% for spring. Watering alone or combined with root pruning for 6 months helped improve the recovery rate but only when carried out in autumn with transplantation in spring. On the other hand, when these pre-treatments were applied in the spring to transplant the trees in summer, the recovery rate was not improved.

Chip pad alignment inspection is of great importance in the industrial field. However, due to the fact that chip pads are usually small, problems such as misdetection and missed detection often occur. When applying deep learning methods for chip pad detection, it is necessary to ensure accurate detection of small target chips while meeting the requirements of lightweight detection models for industrial needs. To solve the above problems, this paper proposes a lightweight model based on improved YOLOv5s. Firstly, the feature extraction part is improved to increase the network's focus on the target. Secondly, the feature fusion layer is improved to double the resolution of the prediction head, and the context-aware network is designed to enhance the context-capture ability of key features of small targets. Finally, SIoU is adopted as the loss function to improve the speed and accuracy of the regression frame. The experimental results show that the improved YOLOv5s algorithm improves the detection accuracy by 2.3% and reduces the network parameters by 81.8% compared to the original algorithm. The improved algorithm is combined with image processing techniques to design correction methods for alignment anomalies and realize real-time alignment anomaly correction in industry.

The combination of encryption and digital watermarking technologies is a trend to achieve full lifecycle data protection. Recently, reversible data hiding in encrypted domain (RDHED) has greatly aroused the interests of many scholars. But the fixed order of first encryption and then watermarking makes this kind of algorithms unsuitable to many applications. Commutative encryption and watermarking (CEW) technology realizes the flexible combination of encryption and watermarking, and suits more applications. However, most existing CEW schemes for vector maps are not reversible and unsuitable to high precision maps. To solve this problem, here we propose a commutative encryption and reversible watermarking (CERW) algorithm for vector maps based on virtual coordinates which are uniformly distributed on the number axis. The CERW algorithm consists of virtual interval step based encryption scheme and coordinate difference based reversible watermarking scheme. In the encryption scheme, the map coordinates are moved randomly by multiples of virtual interval step defined as distance between two adjacent virtual coordinates. In the reversible watermarking scheme, difference expansion (DE) technique is used to embed the watermark bit into the coordinate difference computed based on the relative position of a map coordinate in a virtual interval. Since the relative position of a map coordinate in a virtual interval keeps unchanged during the coordinate scrambling encryption process, the watermarking operation and the encryption operation don’t interfere with each other and the commutativity between encryption and watermarking achieves. Results show that: proposed method has high security, high capacity and good invisibility. In addition, the algorithm is not only applicable to polyline and polygon vector data, but also can be applied to sparsely distributed point data which the traditional DE watermarking algorithm often fails to watermark.

The Marun-Keu complex plays a significant role in understanding the geological evolution of the Ural orogen; however, it remains poorly understood. This study aims to provide insights into the complex's age, protolith composition, rock formation conditions, and its position in the region's history. The zircons from the host granitic gneisses are characterized by magmatic cores with an age of 470 Ma and metamorphic rims with an age of approximately 370 Ma. We suggest that the metamorphic rims were formed during eclogite metamorphism and that the metagranitoids hosting the eclogites experienced eclogite metamorphism simultaneously with the basic and ultrabasic rocks that are common in this area. Heterogeneous zircons were also isolated from the selvage of a pegmatite vein, in which four domains are distinguished, two to three of which can be identified within single grains, as follows: 1) igneous cores with an age of approximately 470 Ma and the geochemical characteristics of zircon crystallized in basic rocks; 2) zircons recrystallized during eclogite metamorphism with geochemical characteristics intermediate between those of the magmatic cores and true eclogitic zircon; 3) pegmatitic zircon, exhibiting the most sharply differentiated REE spectra of all four domains, characterized by a prominent positive Ce anomaly and a weakly expressed negative Eu anomaly; and 4) eclogitic zircon, observed in the form of veins and rims, superimposed in relation to the other three domains. The age of the latter three domains is within the error range and is estimated to be approximately 370 Ma. This indicates that the processes of eclogite metamorphism and the formation of pegmatites occurred at approximately the same time in the studied area.

Ruminant animals naturally emit methane gas owing to anaerobic microbial fermentation in the rumen, and these gases are considered major contributors to global warming. Scientists worldwide are attempting to minimize methane emissions from ruminant animals. Some of these attempts include the manipulation of rumen microbes using antibiotics, synthetic chemicals, dietary interventions, probiotics, propionate enhancers, stimulation of acetogens, manipulation of rumination time, vaccination, and genetic selection of animals that produce low methane (CH4). The majority of synthetic additives are harmful to both beneficial rumen microbes and the host or only temporarily affect methanogenesis. Phytogenic feed additives (PFAs) have recently emerged as the best alternatives to antibiotics and synthetic chemicals because of growing public concerns regarding drug resistance and the negative impacts of antibiotics and synthetic chemicals on humans, livestock, and the environment. These additives reduce methane production and improve the volatile fatty acid profile. In this review, we provide an overview of PFA sources and how their bioactive components affect the rumen microbiome to reduce methane emissions. Additionally, we highlight the mechanisms of action of PFAs as a whole as well as some of their bioactive components.

Metabolites present in the hemolymph of honey bees play a crucial role in modulating the metamorphic process within the species. However, the precise alterations in metabolite composition, along with the associated variances and regulatory pathways implicated during the larvae-to-pupae metamorphosis of honey bees, remain incompletely elucidated. In this investigation, we gathered hemolymph samples from honey bee larvae at three distinct physiological stages-feeding, prepupal, and pupae and subjected them to metabolite analysis utilizing the liquid chromatography-mass spectrometry (LC-MS) technique. Employing principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), we identified significant differential metabolites and conducted a metabolic pathway analysis on those differentially up-regulated during the prepupal and pupae stages. Notably, metabolites up-regulated in the hemolymph of prepupal stage larvae primarily governed glucose metabolism and fat digestion and absorption, while those in pupae stage larvae were involved in regulating chitin and lipopolysaccharide precursor formation, as well as the biosynthesis of phenylalanine, tyrosine, and tryptophan. These findings bear significant implications for advancing our comprehension of the metamorphic processes in honey bees.

Viral contamination in the aquaculture industry is a significant challenge that requires further research. Emerging evidence suggests that long noncoding RNAs (lncRNAs) play a role in regulating various biological processes, including immunity. However, most studies have focused on model organisms, and little is known about non-coding RNAs in the immune response to viral hemorrhagic septicemia (VHS) in rainbow trout. In this study, we investigated the listing of non-coding RNAs (ncRNAs) in rainbow trout challenged with VHS virus. We explored their potential role in defense-like responses based on transcriptomic analysis of the virus-treated and control groups. As a result of this research, 4207 lncRNAs were identified, of which 182 lncRNAs were differentially expressed between treated and untreated samples. Among 7753 lncRNA transcripts, 1920 were novel intergenic (class code “u”), and 3748 transcripts were potentially novel isoform of lncRNAs. Gene ontology analysis revealed biological pathways associated with the stress response and immune signaling. Pairs of genes such as TP63-MSTRG.62, IL10-MSTRG.9783, IL10-MSTRG.9784, TK1-MSTRG.22826, and MSTRG.17881-GLI1 showed cis interactions, while DOCK10-MSTRG.30015, MSTRG.36357-LOC101268921, and MSTRG.34190-ENPP2 showed trans interactions. In this study, the role of lncRNAs in regulating the expression of the target genes was clearly identified. This finding is consistent with the regulatory processes of immune system responses mediated by cytokines, as well as the regulation of oxygen levels in response to viral infection. The information presented here highlights the various regulatory mechanisms of lncRNAs in the expression of immune system genes in fish treated with VHS virus.

European strategic policy directions towards a sustainable blue economy have strengthened interest in maritime investments, thus increasing sectoral competition for marine space. Emerging repercussions out of such a rising interest need to be handled by Marine Spatial Planning (MSP) as a means for: properly allocating marine space to diverse uses; managing conflicts and promoting synergies among them; and pursuing a multi-use perspective of this space. A critical stage of each MSP exercise is the exploration of land-based spatial/developmental directions and their current or potential repercussions in the marine environment in order for constraints but also perspectives in policy choices/maritime uses in the MSP context to be featured; and attainment of a successful coherence or coordination between land and marine spatial/developmental policies to be ensured, by analyzing Land-Sea-Interaction (LSI) and embedding it in the MSP. The latter is the focus of this paper aiming to illuminate critical issues emerging from land-based (terrestrial) policy frameworks for feeding MSP endeavors; and is accomplished in the Northern Aegean Sea, Greece. Towards this end, a multilevel approach is adopted, delving into diverse policy contexts at the local, regional and national/European level and aiming to attain more informed MSP decisions in the specific study region.

IBD, encompassing CD and UC, are characterized by chronic gastrointestinal inflammation due to abnormal immune responses to gut microflora. The bidirectional communication between the gut and the brain, known as the GBA, is disrupted in IBD, leading to neurobiological imbalances and affective symptoms. Systemic inflammation in IBD affects the brain's inflammatory response system, hormonal axis, and BBB integrity, influencing gut microbiota and clinical responses to probiotics. This review aims to explore the links between dysregulations in the GBA, serum biomarkers, and the development of MCI and NDDs. The objective is to identify potential correlations and propose future research directions to understand the impact of altered microbiomes and intestinal barrier functions on NDDs. Studies suggest a potential link between IBD and extraintestinal complications such as MCI and NDDs. Mechanisms include systemic inflammation, BBB dysfunction, autoimmune responses, GBA dysfunction, medication effects, and comorbidities. Serum biomarkers, including vitamin D3, C-reactive protein, high-sensitivity C-reactive protein, vitamin B12, homocysteine, serum amyloid, neuron-specific enolase, neurofilament light chain, S100 proteins, brain-derived neurotrophic factor, IL-6, LP-2 associated phospholipase, prostaglandin E2, IL-1β, and TNF-α, have been investigated for their potential to predict MCI but current results are lacking clarity.

In the process of oil and gas development and production, when the flow rate in the wellbore is large, the materials underground will be eroded, causing the operation to be unable to proceed smoothly and reducing the efficiency of oil and gas production. Therefore, optimizing the anti-erosion coating under high-speed flow conditions in wellbore is a very interesting thing. It can greatly improve operational efficiency. In this paper, laser cladding was used to prepare the new coating used in the PDC drill bit. Then, the effects of tungsten carbide and multi-layer graphene on the microstructure and properties of the tungsten carbide coating were studied. The research results indicate that, for new coatings, 60% tungsten carbide and 0.3% graphene are the optimal ratios. After adding WC, the hardness has significantly improved. However, when the WC content further increases more than 30%, the increase in hardness is relatively small. In addition, when the content of graphene is more than 0.3%, the branched structure became thicker, and not only the segregation of Cr, Si and W became very obvious again, but also the segregation of Fe occurred at the Ni enrichment site. The research results contribute to improving oil and gas production and operational efficiency under high-speed fluid flow conditions in the wellbore.

Although Eulerian approaches are standard in computational acoustics, they are less effective for certain classes of problems like bubble acoustics and combustion noise. A different approach for solving acoustic problems is to compute with individual particles following particle motion. In this paper, a Lagrangian approach to model sound propagation in moving fluid is presented and implemented numerically, using three meshfree methods to solve the Lagrangian acoustic perturbation equations (LAPE) in the time domain. The LAPE split the fluid dynamic equations into a set of hydrodynamic equations for the motion of fluid particles and perturbation equations for the acoustic quantities corresponding to each fluid particle. Then, three meshfree methods, the smoothed particle hydrodynamics (SPH) method, the corrective smoothed particle (CSP) method, and the generalized finite difference (GFD) method, are introduced to solve the LAPE and the linearized LAPE (LLAPE). The SPH and CSP methods are widely used meshfree methods, while the GFD method based on the Taylor series expansion can be easily extended to higher orders. Applications to modeling sound propagation in steady or unsteady fluids in motion are outlined, treating a number of different cases in one and two space dimensions. A comparison of the LAPE and the LLAPE using the three meshfree methods is also presented. The Lagrangian approach shows good agreement with exact solutions. The comparison indicates that the CSP and GFD method exhibit convergence in cases with different background flow. The GFD method is more accurate, while the CSP method can handle higher Courant numbers.

Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use were detected over the period of 1989 to 2013 and then, future sprawl of the province was forecasted by the years 2025 and 2037. The analysis results revealed that the area of urban land use was increased by more than 1.7 % from 36012.5 ha in 1989 to 59754.8 ha in 2013, and the area of Caspian Hyrcanian forestland was reduced by 31628 ha. The results also predicted an alarming increase in the rate of urban development in the province by the years 2025 and 2037, during which urban land use is predicted to develop 0.9 % and 1.38 %, respectively. The development pattern is expected to be uneven and scattered, without following any particular direction. The development will occur close to the existing or newly-formed urban basements as well as around major roads and commercial areas. This development, if not controlled, will lead to the loss of 13863 ha of Hyrcanian forests and if the trend continues, 21013 ha of Hyrcanian forests and 20208 ha of Barren/open lands are expected to be destroyed by the year 2037. In general, the proposed model is an efficient tool for the support of urban planning decisions and facilitates the process of sustainable development of cities by providing decision-makers with an overview on future development of cities where the growth rate is very fast.

Considering the multifaceted nature of neurodegenerative diseases like dementia and the neces-sity for interprofessional knowledge, this research extends its scope to encompass professionals with diverse levels of training and experience in dementia care. A need analysis for the project “My INdividual Digital EDucation.RUHR” (MINDED.RUHR) is conducted to develop an au-tomatized recommender system for individual learning content using AI. In this sub-study, the aim was to reveal didactic specialties, knowledge gaps and structural challenges of further edu-cation in dementia care of different health professions and to derive learning preference personae. Eight focus group interviews among nine health professions and up to six participants (n = 34) each took place to survey distinct didactic experiences and learning needs. The results reflect various learning preferences, with a propensity to multimedia, practical and interactive tasks. Health professions are used to digital education but show aversions against certain e-learning formats. The derived learning preference personae constitute profound blueprints for a us-er-centred digital education design process, aiming to establish personalized and representative further education in dementia care applicable to various individual preferences and structural workplace challenges of healthcare professions.

Early-onset colorectal cancer (EOCRC) is defined as a diagnosis of colorectal cancer (CRC) in individuals younger than 50 years of age. While overall CRC rates in the United States (US) decreased between 2001 and 2018, EOCRC rates have increased. This research project aims to evaluate the feasibility and acceptability of Time-Restricted Eating (TRE), Mindfulness, or TRE combined with Mindfulness among young to middle-aged adults at risk of EOCRC. Forty-eight participants will be randomly assigned to one of four groups: TRE, Mindfulness, TRE &amp; Mindfulness, or Control. Data on feasibility, adherence, and acceptability will be collected. Measures assessed at baseline and post-intervention will include body weight, body composition, dietary intake, physical activity, sleep behavior, circulating biomarkers, hair cortisol, and gut microbiome. Effects of the intervention on the following will be examined: 1) acceptability and feasibility; 2) body weight, body composition, and adherence to TRE; 3) circulating metabolic, inflammation, and oxidative stress biomarkers; 4) intestinal inflammation; and 5) the gut microbiome. TRE combined with Mindfulness holds promise for stress reduction and weight management among individuals at risk of EOCRC. The results of this pilot study will inform the design and development of larger trials aimed at preventing risk factors associated with EOCRC.

Synthesis of a novel series of chromen-3-yl-pyridine moieties. IR, NMR, and MS spectroscopy were used to confirm the structure of these novel compounds and study of antitumor activity of these compounds. The structure activity relationship investigation demonstrated that 2,4-diamino-5-(3-methoxyphenyl)-7-(2-oxo-2H-chromen-3-yl)-1,8-naphthyridine-3-carbonitrile (16), was more effective and naphthyridine-3-carbonitrile derivatives 17, 18 and pyrido[2,3-d]pyrimidine derivative 12, while compounds 5a,b, 9c, 11, 13 and 14 were moderate activity for antitumor activities.

Horse riding therapy, also known as hippotherapy, is a popular treatment for children with cerebral palsy (CP). However, the need for trained therapists, massive land use, and expensive maintenance of the horse ranch makes hippotherapy not affordable or even available for most patients in Indonesia. This problem motivates us to consider mechanical horse riding simulator machines to replace actual horse hippotherapy. However, most patients are children and are easily bored when asked to do monotonous activities for an extended period. The room setting also does not give the patient visual inputs that usually help motivates the children in real-horse hippotherapy activities. To solve this problem, we designed an exercise game (exergaming) software which we named \textit{Sirkus Apel}, providing the patients with fun activities while doing the therapy. We also design an inertial sensor-based controller that lets the patients control the in-game horse by their back movements, which also benefits CP patients. To make the visual input enjoyable to the user while also considering the user's safety, we built a convex mirror-based dome virtual reality to provide an immersive 3-D experience. We then project the game content to the dome to provide an immersive experience to the patients making it as if they are riding a real horse inside the game.

One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. This study presents a real-world implementation of a convolutional neural network (CNN) based Carebot Covid app to detect COVID-19 from chest X-ray (CXR) images. Our proposed model takes the form of a simple and intuitive application. Used CNN can be deployed as a STOW-RS prediction endpoint for direct implementation into DICOM viewers. The results of this study show that the deep learning model based on DenseNet and ResNet architecture can detect SARS-CoV-2 from CXR images with precision of 0.981, recall of 0.962 and AP of 0.993.

To design properly and optimizate liquid-assisted processes such as reactive infiltration for fabricating light weight and corrosion resistant SiC/TiSi2 composites, the interfacial phenomena taking place when liquid Si-rich Si-Ti alloys are in contact with glassy carbon (GC) were investigated for the first time by wetting tests performed by both the sessile and pendant drop methods at T = 1450°C. Specifically, two different Si-rich Si-Ti alloys were selected, and the obtained results in terms of contact angle values, spreading kinetics, reactivity, and developed interface microstructures were compared with experimental observations previously obtained for the liquid Si-rich Si-Ti eutectics processed under the same operating conditions. The increase of the Si content did not affected the final contact angle values. Contrarily, the final developed microstructure at the interface as well as the spreading kinetics were observed as weakly dependent on the composition. From the practical point of view, Si-Ti alloy compositions with a Si-content falling in the simple eutectic region of the phase diagram might be potentially used as infiltrant materials of C- and SiC-based composites.

Recently, Indium Tin Oxide, a highly transparent, well conductive, and CMOS-compatible material, has been paying strong attention to the thermo-optic controlled silicon photonics industry because it allows a miniature of the gap between the core silicon and the heater, thus enabling reducing the electric power consumption and enhancing the switching speed. In this article, we propose an ultralow loss and small-size ITO microheater for the phase shift tuning. The designated microheater is manipulated in realizing a numerical co-design of compact and high bandwidth three-mode converter and three-mode selective router. Simulation results illustrate the 3-dB bandwidth for the three-mode converter and three-mode selective router as much as 100-nm and 40-nm during crosstalk under -25 dB, respectively. Besides, co-designed devices attain relatively large fabrication tolerances corresponding to width and height tolerances of ±50 nm and ±5 nm. In addition, the proposed devices consumed less than 90 mW total power consumption and took a fast switching time below 8 μs. Moreover, both two co-designs can be integrated into an estimated compact footprint of 8 μm2160 μm. Such excellent performances demonstrate the attractive potential of ITO as low-loss thermo-optic phase shifters and open an alternative way for enabling ultrafast and high-speed mode division multiplexing systems and very large-scale photonic integrated circuits.

The importance of antibodies in health care and the biotechnology research and development demands not only knowledge of their experimental structures at high resolution, but also practical implementation of this knowledge for both effective and efficient design and production of antibody for its use in both medical and research applications. While the experimental wet-lab approach is usually costly, laborious and time-consuming, computational (dry-lab) approaches, in spite of their intrinsic limitations in comparison with its experimental (wet-lab) counterpart, provide a cheaper and faster alternative option. For the first time, this article reports a comprehensive set of structural electrostatic features extracted from experimentally determined antigen-antibody-related structures, including especially those structural electrostatic features at the interfaces of all experimentally determined antigen-antibody complex structures as of February 29, 2020, to facilitate effective and efficient machine learning-based computational antibody design using currently available experimental structures inside Protein Data Bank.

The Internet-of-Things (IoT) enables enterprises to obtain profits from data but triggers data protection questions and new types of cyber risk. Cyber risk regulations for the IoT however do not exist. The IoT risk is not included in the cyber security assessment standards, hence, often not visible to cyber security experts. This is concerning, because companies integrating IoT devices and services need to perform a self-assessment of its IoT cyber security posture. The outcome of such self-assessment needs to define a current and target state, prior to creating a transformation roadmap outlining tasks to achieve the stated target state. In this article, a comparative empirical analysis is performed of multiple cyber risk assessment approaches, to define a high-level potential target state for company integrating IoT devices and/or services. Defining a high-level potential target state represent is followed by a high-level transformation roadmap, describing how company can achieve their target state, based on their current state. The transformation roadmap is used to adapt IoT risk impact assessment with a Goal-Oriented Approach and the Internet of Things Micro Mart model.

The state of the grinding wheel in the grinding process directly affects the surface quality of the workpiece. The monitoring of the wear state of the grinding wheel can effectively identify the wear information of the grinding wheel and timely and effectively repair the grinding wheel. In this paper, a multi-sensor fusion precision grinding wheel wear intelligent monitoring system is established for typical difficult-to-machine aerospace material titanium alloy, and a multi-eigenvalue fusion algorithm based on improved support vector machine (SVM) is proposed. The extracted sound emission signal wavelet packet decomposition coefficient mean square value, force signal grinding force ratio, and vibration signal effective value are used as the input of the improved support vector machine, and the identification strategy is adjusted by the entropy weight evaluation method. The results show that the monitoring system established in this paper can effectively identify the wear state of the grinding wheel, and the monitoring accuracy can reach more than 92%.

Frequently Asked Questions (FAQs) systems rely on semantic symmetry similarity measuring between two sentences. To address the challenges of insufficient training data and limited domain-specific understanding in low-resource FAQs, this paper proposes a general framework, PDAMF-FAQ, to solve these issues. Firstly, we propose a paraphrasing-based data augmentation model that integrates syntactic information and edit vectors. Using a rule-based approach, it retrieves template sentences from a corpus and masks relevant words with special characters. Edit vectors between the original and paraphrase sentences are added to a pre-trained model's encoding layer to enhance the model's ability to learn the differences between the original and reference paraphrase sentences. Additionally, this paper presents a mixed-feature semantic matching model based on SimBERT. The model extracts keyword features from the text, replacing these keywords with special characters to construct intent features. These intent features, along with the user question and keyword features, are then concatenated to form the model's input. Experiments were conducted respectively on the paraphrasing-based data augmentation model, mixed-feature semantic matching model and their comprehensive application in a low-resource domain-specific FAQ. The experimental results show that the proposed framework effectively improve the performance of domain-specific FAQ system.

Recurrent Pregnancy Loss (RPL) affects 1-2% of women, and its triggering factors are unclear. Several studies have shown the vaginal, endometrial, and gut microbiota may play a role in RPL. A decrease in the quantity of Lactobacillus crispatus in local microbiota has been associated with an increase in local (vaginal and endometrial) inflammatory response and immune cell activation that leads to pregnancy loss. The inflammatory response may be triggered by gram-negative bacteria, lipopolysaccharides (LPS), viral infections, mycosis, or atypia (tumor growth). Bacterial structures and metabolites produced by microbiota could be involved in immune cell modulation and may be responsible for immune cell activation and molecular mimicry. Gut microbiota metabolic products may increase the amount of circulating pro-inflammatory lymphocytes, which, in turn, will migrate into vaginal or endometrial tissues. Local proinflammatory Th1 and Th17 subpopulations along with a decrease in Treg and tolerogenic NK cells are accountable for the increase in pregnancy loss. Local microbiota may modulate the local inflammatory response increasing pregnancy success. Analyzing local and gut microbiota may be necessary to characterize some RPL patients. Although oral supplementation of probiotics has not been shown to modify vaginal or endometrial microbiota, the metabolites produced by it may benefit patients. Lactobacillus crispatus transplantation into the vagina may enhance the required immune tolerogenic response to achieve a normal pregnancy. The effect of hormone stimulation and progesterone to maintain early pregnancy on microbiota has not been adequately studied, and more research is needed in this area. Well-designed clinical trials are needed to ascertain the benefit of microbiota modulation in RPL.

The intestinal microbiota, comprised of bacteria, archaea, and phages, inhabits the gastrointestinal tract of the organism. Male reproductive sterility is currently a prominent topic in medical research. Increasing research suggests that gut microbiota dysbiosis can result in various reproductive health problems. This article specifically investigates the impact of gut microbiota dysbiosis on male reproductive infertility development. Gut microbiota imbalances can disrupt the immune system and immune cell metabolism, affecting testicular growth and sperm production. This dysfunction can compromise the levels of hormones produced and secreted by the endocrine glands, affecting male reproductive health. Furthermore, imbalance of the gut microbiota can disrupt the gut-brain-reproductive axis, resulting in male reproductive infertility. This article will explore how the imbalance of the gut microbiota impacts male reproductive infertility through immune regulation, endocrine regulation, and interactions of the gut-brain-reproductive axis, concluding with recommendations for prevention and treatment.

Quantitative determination of the critical size of inclined coal pillar in old goaf water affected area is of great significance for water damage prevention and safe mining. The critical size of inclined waterproof coal pillar is derived by using theoretical analysis, numerical calculation and field engineering practice for the stability of the waterproof coal pillar in old goaf water affected area of 1303 working face of Dananhu I mine in Xinjiang region. Firstly, a force model of inclined waterproof coal pillar was established to reveal the law that the critical size of the coal pillar increases with the increase of coal seam inclination under the action of asymmetric load. Then, numerical simulation was applied to reveal the dynamic evolution process of plastic deformation-destabilization-destruction of section coal pillar under the influence of mining and single-side water pressure, and the critical size of waterproof coal pillar in study area was determined to be 19.09m. Finally, measures such as pumping and pressure relief and slurry reinforcement were adopted to reduce the deformation rate of the roadway on side of waterproof coal pillar, which ensured the stability of the coal pillar and roadway and the safe recovery of the working face.

Tack is one of the most important factors affecting the operability of prepregs, but it is still very difficult to obtain the change of prepreg tack with time at different temperatures. In this paper, a newly designed T-peel device is used to test the tack decay rate of prepregs at different temperatures. The obtained decay rate deviates less than 8.9% from the manufacturer's data. The maximum deviation of the ratio of tack decay speeds calculated by the Arrhenius equation from the experiments is not higher than -9.7%. It is proved that the present method can obtain the decay rate of tack of prepregs at different temperatures. This method is also applicable to the other two types of prepregs. The article proposes the handling life unit, an operational life measurement unit for prepregs, and establishes that the relationship between tack and remaining shelf life reduces prepreg management costs.

This study investigates the impact of specimen size on the mechanical properties of wood, specifically focusing on compression strength, elastic modulus, and Poisson's ratio. Compression tests were conducted using three different specimen sizes (20mm×20mm×30mm, 40mm×40mm×60mm, 60mm×90mm×90mm) in the longitudinal, radial, and tangential directions. Mechanical parameters, failure mechanisms, load-displacement curves, and stress-strain relationships were systematically analyzed for each size. The study also evaluated the influence of specimen size on the accuracy of finite element numerical simulations by utilizing the obtained mechanical parameters. The results reveal a significant correlation between compressive strength and specimen size, indicating a decrease in compressive strength with increasing specimen size. Conversely, elastic modulus and Poisson's ratio exhibit less sensitivity to variations in specimen size. Notably, parameters derived from small-sized specimens (20mm×20mm×30mm) exhibited substantial errors, while those obtained from medium-sized (40mm×40mm×60mm) and large-sized specimens (60mm×90mm×90mm) demonstrated greater reliability, providing precise results in finite element numerical simulations.

The AREB/ABF (ABA response element binding) proteins in plants are vital for plant stress responses, but the understanding of AREB/ABFs in orchid species, an important traditional medicinal and ornamental plants, is limited. Here, twelve AREB/ABF genes were identified within three orchids’ complete genome and classified into three groups (groups I, II, and III) via a phylogenetic analysis, which was further supported by the analysis of their conserved motifs and gene structures. The cis-elements analysis showed that hormone response elements followed by light and stress responses were widely rich in AREB/ABFs. A prediction analysis of orchid ABRE/ABF-mediated regulatory network was further constructed through cis-regulatory elements (CREs) analysis of their promoter regions and revealed that several transcriptional factor (TF) gene family were abundant as the potential regulators for orchid AREB/ABFs. Expression profile analysis based on public transcriptomic data showed that most AREB/ABF genes have distinct tissue-specific expression patterns in orchid plants. Additionally, DcaABI5 as the homolog of ABA INSENSITIVE 5 (ABI5) from Arabidopsis was selected for further analysis. The results showed that Arabidopsis transgenic overexpressing DcaABI5 could rescue the ABA insensitive phenotype in the mutant abi5. Collectively, these findings will provide valuable information on AREB/ABF genes in orchids.

Background: Burns are a common and severe medical emergency requiring immediate specialized care to minimize damage and prevent complications. Burn severity depends on depth, extent, and location, with more complex care needed for burns on critical areas or extensive burns. Nursing is essential in burn management, providing immediate care, adapting treatments, managing pain, preventing infections, and offering emotional support for recovery. Methods: Retrospective, observational study that analyzed burn cases treated at the Hospital Complex of Cáceres (Spain) 2018-2022. Inclusion criteria were based on ICD-10 codes for burns, excluding severe cases not treated in this service. Data were analyzed using descriptive statistics, Student's t-tests, Chi-square tests, and ANOVA. Results: 220 patients, with a mean age of 47 years and 60.9% male. Most burns (95.5%) affected the external body surface, with a mean hospital stay of 7.86 days. Medical treatment was provided to 75.5% of patients, and 24.5% required surgical intervention. Significant differences in treatment procedures were observed according to age, skin thickness, and burn degree. Older patients had more procedures and longer hospital stays. Excision and transfer procedures were more common in full-thickness and severe burns. Conclusions: The findings align with previous research on burn demographics and treatment approaches. Treatment differences by age and burn severity highlight the need for tailored interventions. The study underscores the importance of comprehensive burn management, including psychological support for improved long-term outcomes. Further research could explore the impact of socio-economic factors on burn incidence and treatment.

Background: Removable prostheses have seen a fundamental change recently because of advances in polymer materials, allowing improved durability and performance. Despite these advancements, notable differences still occur amongst various polymer materials and processing technologies, requiring a thorough grasp of their mechanical, physical, and therapeutic implications. Methods: Fours different processing techniques, all utilizing Polymethyl methacrylate (PMMA) as the main material, were used to construct complete dentures. The specimens underwent a mechanical test. Results: All the specimens broke under the influence of a certain force, rather than yielding through flow, as is characteristic for plastic materials. For each specimen, the maximum force (N) was recorded (figure 4), as well as the breaking energy, Conclusions: The best results were obtained in the case of the thermoplastic injection system and, surprisingly and contrary to those in the specialized literature, the worst results were recorded in the case of 3D printed samples.

The abstract should be a total of about 250 words and structured to contain the following headings: Background/Objectives, Methods, Results, Conclusions. Background/Objectives: Place the question addressed in a broad context and highlight the purpose of the study; Methods: Describe briefly the main methods or treatments applied. Include any relevant preregistration numbers, and species and strains of any animals used; Results: Summarize the article’s main findings; Conclusions: Indicate the main conclusions or interpretations. The abstract should be an objective representation of the article: it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions. Clinical trial abstracts should include items that the CONSORT group has identified as essential.

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding the performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving the computational efficiency. Finally, the simulation results demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.

Tumid Lupus Erythematosus has been the subject of heated debate as to its correct nosographic classification. In particular, its definition has changed over time, depending on the different studies performed. In this review we address the initial definition of TLE, the changes that have taken place in the understanding and placement within the classification of Lupus Erythematosus (LE), and focus on clinical, histopathological, immunophenotypical and differential diagnosis aspects.

Background: Patients with Inflammatory Bowel Disease (IBD) believe that diet is a key factor in the pathogenesis of their disease and in worsening their symptoms; they often follow restrictive diets that can lead to malnutrition, anxiety, and stress. Recent studies have found an association between eating disorders and IBD, particularly anorexia nervosa and ARFID (Avoidant Restrictive Food Intake Disorder). None of these studies report the association with orthorexia nervosa, that is the obsession with healthy and natural foods. Aim: to assess the risk of orthorexia nervosa in patients with IBD. Methods: 158 consecutive subjects were recruited, including 113 patients diagnosed with IBD and 45 controls. The Donini standardized questionnaire ORTO-15 was administered to assess the risk of orthorexia; clinical and demographic data were also collected. Results: patients with IBD have a prevalence of orthorexia of 77%, significantly higher than the 47% observed in the control group. In patients with IBD, the risk of orthorexia is associated with lower BMI and history of previous surgery. Conclusion: Many patients with IBD develop orthorexia nervosa that impacts the patient's psychological and social sphere, exposes them to a high risk of nutritional deficiencies, and affects quality of life. Further high quality studies are warranted to evaluate the clinical impact of orthorexia and its correlation with clinical features and frank eating disorders.

Spermatogenesis is critical for insect reproduction and is regulated by many different genes. In this study, we found that Forkhead transcription factor Fd59a functions as a key factor in spermatogenesis of Drosophila melanogaster. Fd59a contains a conversed Forkhead domain, and it is clustered to the FoxD subfamily with other FoxD members from some insect and vertebrate species. Mutation in Fd59a caused swelling in the apical region of the testis. More importantly, only a few mature sperm were present in the seminal vesicle of Fd59a mutant flies compared to the control flies, and the fertility of Fd59a2/2 mutant males was significantly lower than that of the control flies. Immunofluorescence staining showed that the homeostasis of testis stem cell niche in Fd59a2/2 mutant and Fd59a RNAi flies was disrupted, and apoptosis of sperm bundles was increased. Furthermore, results from RNA-sequencing and qRT-PCR suggest that Fd59a can regulate expression of genes related to reproductive process and cell death. Taken together, our results indicated that Fd59a plays a key role in the spermatogenesis of Drosophila.

For water-stressed regions like Iran improving the effectiveness and productivity of agricultural water-use is of utmost importance due to climate change and unsustainable demands. Therefore, a hydro-economic model has been developed here for the Zarrine River Basin with the central concept of that demands are value-sensitive functions, where quantities of water-uses at different locations and times have a changeable economic benefits. To do this, the potential crop yields and the surface and groundwater resources, especially Boukan Dam inflow are simulated using the hydrologic model, SWAT, based on predicted climatic scenarios i.e. quantile mapping-downscaled projections. Then, to allocate the agricultural water based on the agro- economic crop water productivity (AEWP) of crops, a basin-wide water management tool, MODSIM, is customized. Next, a simulation- optimization model has been developed using a coupled CSPSO-MODSIM, to optimize the total AEWP, considering climatic impact and crop pattern scenarios, for 2020-2038, 2050-2068 and 2080-2098 periods. Finally, the optimum crop pattern and crop water irrigation depths are presented for different RCPs and periods. The results indicated that this approach will improve considerably the AEWPs and decrease the agricultural water-use up to 40%. Thus, this integrated model is able to support water authorities and other stakeholder in a water-scarce basin, as is the study area.

Traditional flood hazard analyses often rely on univariate probability distributions; however, in many coastal catchments, flooding is the result of complex hydrodynamic interactions between multiple drivers. For example, synoptic meteorological conditions can produce considerable rainfall-runoff, while also generating wind-driven elevated sea levels. When these drivers interact in space and time, they can exacerbate flood impacts; this phenomenon is known as compound flooding. In this paper, we build a Bayesian Network based on Gaussian copulas to generate the equivalent of 500 years of daily stochastic boundary conditions for a coastal watershed in Southeast Texas. In doing so, we overcome many of the limitations of conventional univariate approaches and are able to probabilistically represent compound floods caused by riverine and coastal interactions. We calculate the resulting water levels using a 1D steady-state hydraulic model and find that flood stages in the catchment are strongly affected by backwater effects from tributary inflows and downstream water levels. By comparing with a bathtub modeling approach, we show that simplifying the multivariate dependence between flood drivers can lead to an underestimation of flood impacts, highlighting that accounting for multivariate dependence is critical for the accurate representation of flood risk in coastal catchments prone to compound events.

This research contributes to the overall debate on education for sustainable development (ESD) by shed- ding lights on the contributing role of formal education to the contemporaneous dynamics of literacy, labor market participation and poverty reduction in Africa, with a focus on Burkina Faso. The study uses a semi-parametric recursive trivariate probit modeling approach, and data from the 2014 National Survey on Household Living Conditions in Burkina Faso. The results show that the embraced systemic approach in this analysis is statistically signicant as shown by the 95% condence intervals on the three correlation coeffcients in the model. Furthermore, education does improve literacy skills, however improved literacy skills in itself does not guaranty active labor market participation in Burkina Faso. Active labor market participation seem to be affected by labor market rates of return, and individual reservation wage (or income). When labor market rate of return is short of high literacy skilled individuals' reservation wage, then the natural response is a choice of inactivity in the labor market, by the later group. Simultaneously however, it is found that active labor market participation leads to poverty reduction; therefore, in addition to new industrial policies for structural transformation of the economy, policy makers in Burkina Faso should consider education and minimum wage reforms to give highly literate household members the incentive to be active in the labor market.

The definition of UTC in 1972 balanced the needs of the time and frequency community for a universal time scale that was traceable to the definition of frequency in the SI system of units with the needs of the astronomical community for a time scale that was closely linked to UT1, a time scale related to the rotation of Earth. The balance between these two considerations has changed very significantly since 1972, and there is widespread agreement that the method of relating UTC to UT1 should be changed by increasing the maximum tolerance between the two scales to more than the current limit of 0.9 s. I propose to realize this decision by means of an algorithmic adjustment process based on the variation in the length of the day over the previous centuries. I use these historical data to extrapolate the length of the day for a century into the future, and to compute a periodic rate adjustment to UTC based on this extrapolation. The adjustment process will not use time steps. The magnitude of the periodic rate adjustment will be re-considered every century, but the basic adjustment algorithm will not change. I suggest a maximum tolerance between UT1 and UTC that is large enough to absorb the irregular and unpredictable variations in the length of the day with respect to the long-term average increase in this value but plays no role in determining the parameters of the adjustment process that is proposed. I also propose that the adjustment be implemented at 12 UTC on 1 January since that is closest to a universal holiday in all time zones.

This article is about a framework for determining the degree of realism of any given 1 passenger ride motion discomfort measurement formula. After providing some context and reviewing 2 evidence of deficiency in currently popular ride motion discomfort measurement formulas, the article 3 outlines the research program that needs to be carried out in order to establish such a framework. 4 The research begins with gathering recordings of uncomfortable ride motion episodes encountered in 5 a chosen type of passenger transport service. It then has test subjects compare the episodes via a ride 6 motion simulator and adjust their amplitudes pair wise until they cause equal discomfort. It explains 7 how to take the pair wise amplitude adjustments and determine amplitude adjustments that bring all 8 of the motion episode recordings to a common level of discomfort so that they form a normalized set. 9 Then, the lower the scatter of the scores assigned by any given discomfort measurement formula to 10 the members of that set, the more realistic that formula will be for the chosen service.

Let Vn(d) denote the least number such that every collection of nd-cubes with total volume 1 in d-dimensional (Euclidean) space can be packed parallelly into some d-box of volume Vn(d). We show that V3(d)=r1−dd if d≥11 and V3(d)=1r+1rd+1r−rd+1 if 2≤d≤10, where r is the only solution of the equation 2(d−1)kd+dkd−1=1 on 22,1 and (k+1)d(1−k)d−1dk2+d+k−1=kddkd+1+dkd+kd+1 on 22,1, respectively. The maximum volume is achieved by hypercubes with edges x, y, z such that x=2rd+1−1/d, y=z=rx if d≥11, and x=rd+(1r−r)d+1−1/d, y=rx, z=(1r−r)x if 2≤d≤10. We also proved that only for dimensions less than 11 there are two different maximum packings, and for all dimensions greater than 10 the maximum packing has the two smallest cubes the same.

Continuing the investigation already started (See Farcaş, A.-A.; Bende, A. PCCP 2024, 26, 14937), the light absorption and charge-transfer properties of the dopamine (DA), dopamine zwitterion (called as dopamine-semi-quinone or DsQ) and dopamine-o-quinone (DoQ) adsorbed on the graphene nanoparticle surface have been investigated using the ground state and linear-response time-dependent density functional theories, considering the B97X-D3BJ/def2-TZVPP level of theory. In terms of the strength of molecular adsorption on the surface, the DsQ form has 50 % higher binding energy than that for the DA or DoQ cases (-20.24 kcal/mol vs. -30.41 kcal/mol). The results obtained for electronically excited states and UV-Vis absorption spectra show that the photochemical behavior of DsQ is more similar to DA than that observed for DoQ. Of the three systems analyzed, the DsQ-based complex showed the most active CT phenomenon, both in terms of the number of CT-like states and the amount of charge transferred. Of the first thirty electronically excited states computed for the DsQ case, eleven are purely CT-type and nine are mixed CT and localized (or Frenkel) excitations. By varying the adsorption distance between the molecule and the surface vertically, the amount of charge transfer obtained for DA decreases significantly as the distance increases, for DoQ it remains stable, while for DsQ there are states for which little change is observed, while for others there is a significant change. Furthermore, the mechanistic compilation of the electron orbital diagrams of the individual components cannot describe in detail the nature of the excitations inside the complex.

The focus of this work lies in the construction of monitoring system designed in the form of a patch using a long-lasting potentiometric electrolyte concentration sensor from metal electrodes, in a galvanic cell configuration Mg(s)|NaCl(aq)|Cu(s). Initially, a mathematical analysis was conducted on the expected sensor output through the Nernst equation. The experimental phase involved conducting controlled tests in the laboratory using various molar concentrations of sodium chloride (NaCl) in aqueous solution. Results showed that the electrolyte sensor allows for monitoring electrolyte levels in NaCl in the range of 0.02 to 1 M/L, with electrode lifespan exceeding 10 days. On the other hand, when patch is using human sweat during controlled exercise routines, it showed the sensor's capability for monitoring changes in the subjects' hydration levels in real time. The sensor output potential is greater than 1.7 volts when the sensor electrodes are in contact with human sweat, which is equivalent to measuring molar concentrations at normal hydration and moderately high hydration levels. Sensor results indicate that the system can function in a self-powered manner without external batteries. Additionally, the extended lifespan of its electrodes allows for continuous monitoring of imbalances in the human body, showcasing the system capability for long-term health assessment.

Small peptides (SPs), ranging from 5 to 100 amino acids, play integral roles in plants due to their diverse functions. Despite their low abundance and small molecular weight, SPs intricately regulate critical aspects of plant life, including cell division, growth, differentiation, flowering, fruiting, maturation, and stress responses. As vital mediators of intercellular signaling, SPs have garnered significant attention in plant biology research. This comprehensive review delves into SPs' structure, classification, and identification, providing a detailed understanding of their significance. Additionally, we summarize recent findings on the biological functions and signaling pathways of prominent SPs that regulate plant growth and development. The review also offers a forward-looking perspective on future research directions in peptide signaling pathways.

Pulmonary Embolism (PE) is the obstruction of blood arteries in the lungs by a blood clot. The mortality risk for PE is approximately 30%. Detecting pulmonary embolism in the segmental arteries of the lung is more challenging than in the main arteries and is very prone to being missed. A computer-based approach was created in this work to automatically identify pulmonary embolism in the segmental arteries using computed tomography images. The system's infrastructure includes an enhanced Mask R-CNN deep neural network that has been trained with images containing PE. The network accurately identifies the location of the pulmonary embolism on the computed tomography picture and successfully extracts its borders. The investigation was conducted by generating a local dataset. The study's performance was evaluated using pulmonary embolisms identified manually by the expert radiologist. The sensitivity, specificity, accuracy, dice coefficient, and Jaccard index values for 2130 pictures were 96.4%, 93.5%, 96.1%, 0.96, and 0.90, respectively. The improved Mask R-CNN model has demonstrated superior performance when compared to the traditional Mask R-CNN and U-Net models. This high-performance technology is designed to identify pulmonary embolism and will aid professionals in assessing the size of the embolism.

This project designed a set of network security mechanisms for cloud applications in VANETs. At present, cloud computing is one of the government’s development priorities in the industry. We divide cloud into public, private, and hybrid clouds in this project. Vehicles or passengers can access road conditions or public transport information through the public cloud; public transportation can access current driving records and users can access relevant enterprise information on the private cloud and hybrid cloud is a combination of public and private clouds. Both cloud computing or VANETs require network and information security. At present, many researches on VANETs security only focus on message communication, and neglect information storage. Researches on cloud computing security only focus on information protection, and neglect user privacy and anonymity. This project designed a set of network and information security mechanisms in line with the requirements of Confidentiality, Authentication, Non-repudiation, Conditional Anonymity, and Conditional Untraceability. This project primarily needs to achieve the following: 1.an authentication mechanism to verify the identity of each other between the passenger and the vehicle and to verify the identity with Single Sign-On; 2. vehicle or user privacy and anonymity, which need to be able to replace the anonymous ID and related parameters for the vehicle or the user; 3. a private communication mechanism, which enables any vehicle or user to communicate privately; and 4. an information security encryption method, which can encrypt the information on a cloud server to avoid unauthorized access by internal personnel or hackers.

Such rapidly changing external conditions as the COVID-19 pandemic and the war in Ukraine, being pure crisis phenomena, not only determine the geopolitical and geoeconomic situation on the global markets, but also cause structural changes in individual countries. Under these conditions, the level of food self-sufficiency becomes particularly important, as it is one of the factors determining the food security of any country. The aim of the research is to assess Ukraine's food self-sufficiency compared to the European Union and Poland. The self-sufficiency ratio (SSR) index was used to assess food self-sufficiency. This is the ratio of the volume of production of the most important products of plant and animal origin to the volume of domestic consumption. Domestic consumption was defined as the volume of domestic production increased by the volume of imports, reduced by the volume of exports and also included changes in domestic stock levels. The surveyed time frame is 2019–2021. The analyzed data were taken from the State Statistics Service of Ukraine, FAOSTAT, USDA and Statistics Poland. A comparative analysis of self-sufficiency for the main agri-food sectors of Ukraine, the EU, and Poland showed significant differences. Taking into account the production potential and food self-sufficiency indicators of Ukraine, Ukraine's possible accession to the EU can be expected to have a major impact on the EU's supply and demand situation, as it may pose a threat to many EU agri-food sectors.

Objective. The present study examined the potential use of computed tomography panoramic mandibular indices on cone beam CT (CBCT) for the assessment of bone density in postmenopausal women with low bone mass. Study design. We enrolled 104 postmenopausal women who performed dual energy X-ray absorptiometry (DXA) using a DXA scanner and mental foramen region CBCT using NewTom VGi EVO Cone Beam 3D system. We assessed the relationship between DXA parameters: lumbar, femoral neck and total hip T score, bone mineral density (BMD), and lumbar trabecular bone score (TBS) and the panoramic mandibular indices: computed tomography mandibular index superior (CTI(S)), computed tomography mandibular index inferior (CTI(I)) and computed tomography mental index (CTMI). Results. We found important correlations between the CTI(I), CTI(S) and CTMI and bone mass, quantitative evaluated as BMD and T-score and also quality using TBS score. Conclusion. CBCT measured indices CTI(S), CTI(I), CTMI are useful in assessing patients with low bone mass.

Polycystic ovary syndrome (PCOS) is a common condition in reproductive-age women. Women with PCOS are at higher risk of pregnancy complications including gestational diabetes (GDM). The prevalence of PCOS and GDM varies according to the diagnostic criteria used. The mechanism for the occurrence of GDM in women with PCOS is still unclear. Materials &amp; Methods- A systematic search of the electronic database was done using keywords like PCOS, and GDM to include studies between April 2008 and March 2023 where uniform diagnostic criteria were used. Studies were excluded with high-risk of bias after the assessment of bias, and meta-analysis was done with relative risks and confidence intervals. Results &amp; Discussion-Out of 1896 search results, 95 were selected for full-text. The quality of the studies was scrutinized and a total of 28 studies were included as they fulfilled the inclusion criteria. The incidence of GDM in women with PCOS was found to be 10.55 % (2.9- 54.9%) from pooled data involving 1,280,245. The prevalence of PCOS in women diagnosed with GDM was found from pooled data of 36,351 women in retrospective studies to be 2.47% (1.5- 50.1%). Selection predominantly included cohort studies, most commonly from China. The meta-analysis reported the risk of GDM is higher in women with PCOS than in those without PCOS (HR: 1.59, 95% CI: 1.27–1.91, p &lt; 0.001). Family history of diabetes, ethnicity, pre-pregnancy BMI, insulin resistance, gestational weight gain, use of assisted reproductive techniques, and multifetal gestation were the risk factors for GDM in women with PCOS. Conclusion- The prevalence of GDM in patients with PCOS is high, but the causality is complex. The newer predictive models are promising in clarifying the causative relationships, yet use various parameters at different cut-offs. There is a need for the development of universally acceptable parameters for the early prediction of GDM in women with PCOS.

Iron tailings are solid waste generated after the mining of iron mines, which have caused serious damage to the surrounding ecology and environment and have attracted widespread attention. By analyzing the formation causes, main components, and environmental effect mechanisms of iron tailings, the article systematically elaborates on the degree and mechanism of the harm of iron tailings to water bodies, atmosphere, soil, and ecosystems, and evaluates its impact on human health and sustainable development of society. On this basis, specific prevention and control measures have been proposed to reduce iron tailings emissions, realize resource recycling, and restore ecosystems. Research shows that iron tailings containing heavy metals can widely pollute the environment through dissolution and migration, and have significant biological toxicity effects. The research results can provide a theoretical reference for decision-making departments to formulate scientific and reasonable iron tailings disposal and ecological protection policies. It is recommended to strengthen specific research on the infiltration of toxic and harmful components in tailings in the future, and develop efficient and stable solidification technology, and vegetation-microbial joint restoration models to further reduce the ecological and environmental risks of iron tailings.

In the contemporary accelerated pace of life, emphasizing the importance of sleep quality is essential for enhancing overall well-being and health. This study investigates the link between poor sleep quality and key factors affecting wellness, such as mental health and eating disorders. Through a cross-sectional analysis involving 407 participants, utilizing established measures including the Depression Anxiety Stress Scale (DASS-21), the Eating Disorder Examination Questionnaire Short (EDE-QS), and the single-item Sleep Quality Scale (SQS), data was collected and analyzed using SPSS v28 and R-Statistics. The findings reveal a significant correlation (p&lt;0.05) between DASS-21, EDE-QS, and SQS, indicating that individuals experiencing poor sleep quality exhibit higher levels of depression, anxiety, and stress. Furthermore, multinomial logistic regression analysis highlights low sleep quality as a risk factor for both mental health (OR: 1.071, 95% CI: 1.042, 1.102, p&lt;0.05, low vs. high sleep quality), and eating disorders (OR: 1.047, 95% CI: 1.004, 1.092, p&lt;0.05, low vs. high sleep quality). Overall, these results underscore the critical role of sleep quality in mental health and suggest insomnia as a predictive factor for both poor mental well-being and disordered eating habits. Further research through randomized controlled trials is warranted to validate the findings of this cross-sectional study.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

The attitude dynamics and orbit dynamics of large rigid space structures are coupled with each other under gravity gradient, which may affect the stability of the large rigid space structures. In this paper, the gravity gradient stability of a large rigid space structure is studied under both small and large disturbances. Based on the rigid body dynamics and orbit dynamics, an accurate dynamic model without any linearization of the large rigid space structure is established via the natural coordinate formulation (NCF), which is able to describe the large overall motions of the structures. By using the generalized-α algorithm, the gravity gradient stability of the large rigid space structure is simulated and analyzed via various examples, including the influence of large disturbance angles, the positions at the stabilization and unstabilization regions. Finally, the relationship between spinning stability and gravity gradient stability is also investigated via a large spinning space structure.

Gene regulation is vital for cellular function and homeostasis, involving diverse mechanisms controlling specific gene products and contributing to tissue-specific gene expression. Dysregulation leads to diseases, emphasizing the need for understanding these mechanisms. Non-coding regions, notably enhancers and super-enhancers (SEs), act as crucial gene regulators, orchestrating transcriptional activity via transcription factors (TFs). SEs, with high activity, are pivotal in cell identity and disease progression. Current computational approaches focus on individual regulators like TFs and miRNAs, neglecting SE interactions. We emphasize incorporating SEs into models to enhance understanding. Experimental studies have recently linked miRNAs and TFs to SEs. Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) provides valuable SE information. In this review, we categorize computational methods leveraging TF and miRNA data into three areas and explore challenges integrating enhancers/SEs. These include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators, including SEs. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in identifying therapeutic targets and disease biomarkers. We believe that understanding the role of ChIP-Seq data in predicting the effect of miRNA on gene regulation is crucial to construct statistical/computational model that would tackle these challenges.

Establishment of protected areas (PAs) is one of the key global conservation strategies that currently cover approximately 15% of the earth’s land surface. Globally, PA networks are designed to curb the growing anthropogenic pressures in areas with high biological diversity. Despite the importance of PAs in conserving the vanishing biodiversity and unique habitats, many of them are in critical condition due to poor governance thus functioning below the expected level. Moreover, in many developing countries, the PA coverage is below the global standard. Recognizing their contemporary role in conservation, governments have recently agreed to expand the global PA coverage to 17% by the year 2020 (Aichi target 11). This book with eight chapters from different regions of the world provides an overview of the PAs governance, institutional mechanisms, conservation benefits, limitations and challenges associated with their respective policy discourse, integrated management, and functional attributes. Protected areas expect to to play an important role in the long rn in conservation and protection of biodiversity and ecosystems particularly in countries where population pressure and habitat loss are high. Regular intervention, political commitment, and effective governance are essential for the sustainability of PAs across the world. Here, we also attempted to shed some light on future development clues for the sustainable management and monitoring of PAs worldwide.

This paper presents a piezoresistive pressure sensor with a shield layer for improved stability. Compared with the conventional piezoresistive pressure sensors, p-type piezoresistors are covered by an n-type shield layer, which is formed by ion implantation. The proposed pressure sensors have been successfully fabricated by bulk micromachining techniques. The impact of electrical field on piezoresistors is studied by simulation. The temperature drift of the pressure sensor has been investigated by both simulation and experimental measurement. Characteristics of developed pressure sensors are tested from -40 C to 125 C. A sensitivity of 0.022 mV/V/KPa and a maximum non-linearity of 0.085% FS are measured for the fabricated sensor in a pressure range of 1 MPa. The temperature coefficients of resistance of shielded piezoresistors are found to be smaller than those of un-shielded ones. It is demonstrated that the shield layer is able to reduce the drift caused by electrical field and ambient temperature variation.

In Cameroon, Aedes mosquitoes transmit various arboviruses, posing significant health risks. We aimed to characterize the Aedes virome in southwestern Cameroon and identify potential core viruses which might be associated with vector competence. A total of 398 Aedes mosquitoes were collected from four locations (Bafoussam, Buea, Edea and Yaounde). Aedes albopictus dominated all sites except for Bafoussam where Aedes africanus prevailed. Metagenomic analyses of mosquitoes grouped per species into 54 pools revealed notable differences in eukaryotic viromes between Ae. africanus and Ae. albopictus, with the former exhibiting greater richness and diversity. Thirty-seven eukaryotic virus species from 16 families were identified, including six novel viruses with near complete genome sequences. Seven viruses were further quantified in individual mosquitoes via qRT-PCR. Although none of them could be identified as core viruses, Guangzhou sobemo-like virus and Bafoussam mosquito solemovirus virus were highly prevalent regionally in Ae. albopictus and Ae. africanus, respectively. This study highlights the diverse eukaryotic virome of Aedes species in southwestern Cameroon. Despite their shared Genus, Aedes species exhibit limited viral sharing, with varying viral abundance and prevalence across locations. Ae. africanus, an understudied vector, harbors a rich and diverse virome, suggesting potential implications for arbovirus vector competence.