Deep Transfers of p-Class Tower Groups

Let p be a prime. For any finite p-group G, the deep transfers T H,G ' : H / H ' → G ' / G " from the maximal subgroups H of index (G:H) = p in G to the derived subgroup G ' are introduced as an innovative tool for identifying G uniquely by means of the family of kernels ùd(G) =(ker(T H,G ')) (G: H) = p. For all finite 3-groups G of coclass cc(G) = 1, the family ùd(G) is determined explicitly. The results are applied to the Galois groups G =Gal(F3 (∞)/ F) of the Hilbert 3-class towers of all real quadratic fields F = Q(√d) with fundamental discriminants d > 1, 3-class group Cl3(F) □ C3 × C3, and total 3-principalization in each of their four unramified cyclic cubic extensions E/F. A systematic statistical evaluation is given for the complete range 1 d 7, and a few exceptional cases are pointed out for 1 d 8.

Analysis and Design Innovation on Underground Gasifier for Medium-Deep Coal Seam

Over the past 80 years,dozens of underground coal gasification(UCG)mine field tests have been carried out around the world.However,in the early days,only a small number of shallow UCG projects in the former Soviet Union achieved commercialised production.In this century,a few pilot projects in Australia also achieved short-term small-scale commercialised production using modern UCG technology.However,the commercialisation of UCG,especially medium-deep UCG projects with good development prospects but difficult underground engineering conditions,has not progressed smoothly around the world.Considering investment economy,a single gasifier must realise a high daily output and accumulated output,as well as hold a long gasification tunnel to control a large number of coal resources.However,a long gasification tunnel can easily be affected by blockages and failure,for which the remedial solutions are difficult and expensive,which greatly restricts the investment economy.The design of the underground gasifier determines the success or failure of UCG projects,and it also requires the related petroleum engineering technology.Combining the advantages of the linear horizontal well(L-CRIP)and parallel horizontal well(P-CRIP),this paper proposes a new design scheme for an“inclined ladder”underground gasifier.That is to say,the combination of the main shaft of paired P-CRIP and multiple branch horizontal well gasification tunnels is adopted to realise the control of a large number of coal resources in a single gasifier.The completion of the main shaft by well cementation is beneficial for maintaining the integrity of the main shaft and the stability of the main structure.The branch horizontal well is used as the gasification tunnel,but the length and number of retracting injection points are limited,effectively reducing the probability of blockage or failure.The branch horizontal well spacing can be adjusted flexibly to avoid minor faults and large cracks,which is conducive to increasing the resource utilisation rate.In addition,for multi-layer thin coal seams or ultra-thick coal seams,a multi-layer gasifier sharing vertical well sections can be deployed,thereby saving investment on the vertical well sections.Through preliminary analysis,this gasifier design scheme can be realised in engineering,making it suitable for largescale deployment where it can increase the resource utilisation rate and ensure stable and controllable operations.The new gasifier has outstanding advantages in investment economy,and good prospects for application in the commercial UCG projects of medium-deep coal seams.

Insight to the Mode of Action of <i>Allium sativum</i>Leaf Agglutinin (ASAL) Expressing in T₃Rice Lines on Brown Planthopper

Brown planthopper, the sap sucking hemipteran pest, is one of the major contributors to the yield loss of rice through the world. To combat the situation researchers are interested identifying genes from plant origin having potentiality to develop hemipteran pest resistance. Interestingly, it was observed that rice plants expressing ASAL, a monocot mannose binding lectin, showed significant resistance to brown planthopper and green leafhopper. Additionally, antibiotic resistant marker gene free ASAL expressing rice lines were developed to overcome the biosafety issues. However, the basis behind the resistance against planthoppers is still not clearly understood. Ligand blot assay was performed with total BBMV protein from BPH and a ~56 kDa receptor protein was detected. LC MS/MS analysis revealed that the receptor protein is NADH quinone oxidoreductase (NQO), a key player in electron transport chain, insect defense response and male/female gametogenesis. Presumably interaction of ASAL with NQO may lead to toxicity and loss of fecundity among BPH feeding on ASAL expressing transgenic rice plants. These findings provide a stable scientific basis for considering these transgenic ASAL expressing rice plants as significant product for combating BPH attack associated yield loss of rice.

Preliminary Network Centric Therapy for Machine Learning Classification of Deep Brain Stimulation Status for the Treatment of Parkinson’s Disease with a Conformal Wearable and Wireless Inertial Sensor

The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Therapy is highly relevant to the treatment of Parkinson’s disease through deep brain stimulation. Originally wearable and wireless systems for quantifying Parkinson’s disease involved the use a smartphone to quantify hand tremor. Although originally novel, the smartphone has notable issues as a wearable application for quantifying movement disorder tremor. The smartphone has evolved in a pathway that has made the smartphone progressively more cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone utilizes an inertial sensor package that is not certified for medical analysis, and the trial data access a provisional Cloud computing environment through an email account. These concerns are resolved with the recent development of a conformal wearable and wireless inertial sensor system. This conformal wearable and wireless system mounts to the hand with the profile of a bandage by adhesive and accesses a secure Cloud computing environment through a segmented wireless connectivity strategy involving a smartphone and tablet. Additionally, the conformal wearable and wireless system is certified by the FDA of the United States of America for ascertaining medical grade inertial sensor data. These characteristics make the conformal wearable and wireless system uniquely suited for the quantification of Parkinson’s disease treatment through deep brain stimulation. Preliminary evaluation of the conformal wearable and wireless system is demonstrated through the differentiation of deep brain stimulation set to “On” and “Off” status. Based on the robustness of the acceleration signal, this signal was selected to quantify hand tremor for the prescribed deep brain stimulation settings. Machine learning classification using the Waikato Environment for Knowledge Analysis (WEKA) was applied using the multilayer perceptron neural network. The multilayer perceptron neural network achieved considerable classification accuracy for distinguishing between the deep brain stimulation system set to “On” and “Off” status through the quantified acceleration signal data obtained by this recently developed conformal wearable and wireless system. The research achievement establishes a progressive pathway to the future objective of achieving deep brain stimulation capabilities that promote closed-loop acquisition of configuration parameters that are uniquely optimized to the individual through extrinsic means of a highly conformal wearable and wireless inertial sensor system and machine learning with access to Cloud computing resources.

Geologic-Geophysical Indicators of the Deep Structure of Zones of Geothermal Anomalies for Allocation of Channels of the Deep Heat and Mass Transfer

On the basis of the analysis of field thermogeochemical data along abnormal zones of a thermal stream in the Bukhara-Khiva, oil-and-gas region of the Turan (Tegermen, Chagakul, Shimoly Alat, Beshtepa) was succeeded to obtain important data on a deep structure of sites. Data of gas-chemical and geothermal observations show about confinedness of abnormal concentration of methane to zones of the increased values of the temperature field the measured values of temperatures (Tegermen Square and others). On geoelectric section mines 2-D of inversion of the MT-field depth of 4000 m are lower, among very high-resistance the chemogenic and carbonate deposits of the Paleozoic is traced the subvertical carrying-out abnormal zone. This zone is identified as the channel of a deep heat and mass transfer with which hydrocarbon (HC) deposits are connected. It is shown that electro-investigation when using a geophysical complex can and has to become “advancing” at exploration by oil and gas.

First report of splenic rupture following deep enteroscopy

Splenic rupture is a rare complication of diagnostic and therapeutic gastrointestinal endoscopy procedures.Herein,we report for the first time a case of splenic rupture following therapeutic retrograde double-balloon enteroscopy,which occurred in an 85-year-old man who was treated for recurrent mid-intestinal bleeding that resulted from ileal angioectasia.This patient promptly underwent an operation and eventually recovered.

Silicon Deposition in Leaf Trichomes of Cucurbitaceae Horticultural Plants: A Short Report

Silicon deposition in leaf trichome of six horticultural Cucurbitaceae species, cucumber (Cucumis sativus), pumpkin (Cucurbita maxima), melon (Cucumis melo), watermelon (Citrullus lanatus), sponge gourd (Luffa cylindrica) and bottle gourd (Lagenaria siceraria var. hispida) was observed by an X-ray microanalyzer coupled with an environmental scanning electron microscope. The elements that presented in the surface of three or four leaves of the individual species were detected and mapped by the X-ray microanalyzer. In leaves of cucumber, pumpkin, and melon, high accumulation of silicon was detected in cells surrounding the bases of the trichome hair and the hair itself deposited calcium. On the other hand, in sponge gourd and bottle gourd, high accumulation of silicon was detected only in the hair. In watermelon leaves, silicon deposited both in the hair and in cells surrounding the bases of the hair. Thus, horticultural Cucurbitaceae plants have interspecific variation in the pattern of silicon deposition in leaf trichomes.

Deep structural research of the South China Sea: Progresses and directions

The South China Sea(SCS)is the hotspot of geological scientific research and nature resource exploration and development due to the potential for enormous hydrocarbon resource development and a complex formation and evolution process.The SCS has experienced complex geological processes including continental lithospheric breakup,seafloor spreading and oceanic crust subduction,which leads debates for decades.However,there are still no clear answers regarding to the following aspects:the crustal and Moho structure,the structure of the continent-ocean transition zone,the formation and evolution process and geodynamic mechanism,and deep processes and their coupling relationships with the petroliferous basins in the SCS.Under the guidance of the“Deep-Earth”science and technology innovation strategy of the Ministry of Natural Resources,deep structural and comprehensive geological research are carried out in the SCS.Geophysical investigations such as long array-large volume deep reflection seismic,gravity,magnetism and ocean bottom seismometer are carried out.The authors proposed that joint gravitymagnetic-seismic inversion should be used to obtain deep crustal information in the SCS and construct high resolution deep structural sections in different regions of the SCS.This paper systematically interpreted the formation and evolution of the SCS and explored the coupling relationship between deep structure and evolution of Mesozoic-Cenozoic basins in the SCS.It is of great significance for promoting the geosystem scientific research and resource exploration of the SCS.

Leaf Tissue Water Relations Are Associated with Drought-Induced Leaf Shedding in Tropical Montane Habitats

In tropical montane areas, water limitation is a common occurrence, and both pioneer and forests species experience water stress during the dry season. Adjustments of leaf area during periods of drought allow for the maintenance of the water supply and physiological functions of the remaining leaves. Here, we compared leaf blade water relations between pioneer and forest tree species. Leaf pressure-volume (P-V) curves were determined from samples taken prior to the dry season, to assess how leaves of the different species were adapted to prepare for and endure water deficits. The following parameters were calculated: osmotic potential at full (Ψπ(100)) and zero (Ψπ(0)) turgor, relative water content at zero turgor (RWC0), volumetric elastic modulus (&#949) as well as apoplasm (A) and symplasm (S) water content and their ratio (A/S). Although the pioneer and forest species occupied contrasting habitats, and both groups were clearly differentiated with respect to their water transport capability and water use efficiency, their leaf tissue water relations showed clear differences across species but not between the groups. Some species underwent leaf shedding and accumulated xylem embolisms during the dry season, and their leaves had high cell elasticity. Consequently, these species presented large cell volume changes with turgor loss. Conversely, species with rigid leaves were able to undergo lower leaf turgor with only small changes in cell volume during drought, which might aid to preserve leaf cell function, maintain water uptake, and consequently avoid accelerated leaf senescence and shedding during the dry season.

Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning

Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR),which are the important digital carriers for recording medical activities of patients.Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE.This study aims to improve the recognition accuracy of these two tasks by exploring deep learning methods.Methods This study discussed and built two application scenes of bidirectional long short-term memory combined conditional random field (BiLSTM-CRF) model for NER and MRE tasks.In the data preprocessing of both tasks,a GloVe word embedding model was used to vectorize words.In the NER task,a sequence labeling strategy was used to classify each word tag by the joint probability distribution through the CRF layer.In the MRE task,the medical entity relation category was predicted by transforming the classification problem of a single entity into a sequence classification problem and linking the feature combinations between entities also through the CRF layer.Results Through the validation on the I2B2 2010 public dataset,the BiLSTM-CRF models built in this study got much better results than the baseline methods in the two tasks,where the F1-measure was up to 0.88 in NER task and 0.78 in MRE task.Moreover,the model converged faster and avoided problems such as overfitting.Conclusion This study proved the good performance of deep learning on medical knowledge extraction.It also verified the feasibility of the BiLSTM-CRF model in different application scenarios,laying the foundation for the subsequent work in the EMR field.

Relationship between Leaf Micro- and Macro-Nutrients in Top Canopy Trees in a Mixed Forest in the Upper Rio Negro in the Amazon Region

The mixed forests of the upper Rio Negro at the northern of the Amazon basin grow in oxisol soils that are extremely infertile. These areas exhibit deficiencies in several macro-nutrients, and may also be characterized by the shortage or toxic excess of some micronutrients. The overall goal of this research is to collect more comprehensive information regarding the micronutrient composition of the upper Rio Negro forests as well as discern the relationship between leaf micro- and macro-nutrients that may contribute to the homeostasis and balance of the ionome. Firstly, the nutrient composition within the oxisol soil and leaf tissues of two top canopy tree species from the mixed forests was determined. We then analyzed the relationship between leaf micronutrient composition with N and P levels of the two species and that of species inhabiting the Amazon caatinga. Extractable soil Zn, B, Mn and Cu were very low in the mixed forest. In contrast, Fe and Al levels were potentially toxic. The analysis of leaf N/P ratios revealed for the first time the co-limitation of N and P in the mixed forest. This contrasts with species from the adjacent Amazon caatinga toposequence that are characterized by strong N limitation. All micronutrients within leaves of species inhabiting the mixed forest were also found to have low concentrations. Moreover, Fe and Al were detected at concentrations well below those reported for accumulator species. This suggested that leaf ion homeostasis was maintained under potentially toxic soil Fe and Al conditions. Leaf micronutrient (Fe, Zn and B) contents mirrored that of leaf N and P contents, and comparable Fe/N, Fe/P, Zn/N, Zn/P, B/N as well as B/P ratios were found across species and forest types. Therefore, forest species exhibited the capability to maintain leaf nutrient balances under soil conditions with deficient or toxic levels of micronutrients.

MicroRNA expression in the hippocampal CA1 region under deep hypothermic circulatory arrest

Using deep hypothermic circulatory arrest, thoracic aorta diseases and complex heart diseases can be subjected to corrective procedures. However, mechanisms underlying brain protection during deep hypothermic circulatory arrest are unclear. After piglet models underwent 60 minutes of deep hypothermic circulatory arrest at 14°C, expression of microRNAs(miRNAs) was analyzed in the hippocampus by microarray. Subsequently, TargetScan 6.2, RNA22 v2.0, miRWalk 2.0, and miRanda were used to predict potential targets, and gene ontology enrichment analysis was carried out to identify functional pathways involved. Quantitative reverse transcription-polymerase chain reaction was conducted to verify miRNA changes. Deep hypothermic circulatory arrest altered the expression of 35 miRNAs. Twenty-two miRNAs were significantly downregulated and thirteen miRNAs were significantly upregulated in the hippocampus after deep hypothermic circulatory arrest. Six out of eight targets among the differentially expressed miRNAs were enriched for neuronal projection(cyclin dependent kinase, CDK16 and SLC1 A2), central nervous system development(FOXO3, TYRO3, and SLC1 A2), ion transmembrane transporter activity(ATP2 B2 and SLC1 A2), and interleukin-6 receptor binding(IL6 R)– these are the key functional pathways involved in cerebral protection during deep hypothermic circulatory arrest. Quantitative reverse transcription-polymerase chain reaction confirmed the results of microarray analysis. Our experimental results illustrate a new role for transcriptional regulation in deep hypothermic circulatory arrest, and provide significant insight for the development of miRNAs to treat brain injuries. All procedures were approved by the Animal Care Committee of Xuanwu Hospital, Capital Medical University, China on March 1, 2017(approval No. XW-INI-AD2017-0112).

Petroleum geology controlled by extensive detachment thinning of continental margin crust: A case study of Baiyun sag in the deep-water area of northern South China Sea

The relationships between crustal stretching and thinning,basin structure and petroleum geology in Baiyun deep-water area were analyzed using large area 3D seismic,gravity,magnetic,ocean bottom seismic(OBS),deep-water exploration wells and integrated ocean drilling program(IODP).During the early syn-rifting period,deep-water area was a half-graben controlled by high angle faults influenced by the brittle extension of upper crust.In the mid syn-rifting period,this area was a broad-deep fault depression controlled by detachment faults undergone brittle-ductile deformation and differentiated extension in the crust.In the late syn-rifting period,this area experienced fault-sag transition due to saucer-shaped rheology change dominated by crustal ductile deformation.A broad-deep fault depression controlled by the large detachment faults penetrating through the crust is an important feature of deep-water basin.The study suggests that the broad-deep Baiyun sag provides great accommodation space for the development of massive deltaic-lacustrine deposition system and hydrocarbon source rocks.The differentiated lithospheric thinning also resulted in the different thermal subsidence during post-rifting period,and then controlled the development of continental shelf break and deep-water reservoir sedimentary environment.The high heat flow background caused by the strong thinning of lithosphere and the rise of mantle source resulted in particularities in the reservoir diagenesis,hydrocarbon generation process and accumulation of deep-water area in northern South China Sea.

Endoscopic and surgical resection of T1a/T1b esophageal neoplasms: A systematic review

AIM: To investigate potential therapeutic recommendations for endoscopic and surgical resection of T1a/ T1b esophageal neoplasms. METHODS: A thorough search of electronic databases MEDLINE, Embase, Pubmed and Cochrane Library, from 1997 up to January 2011 was performed. An analysis was carried out, pooling the effects of outcomes of 4241 patients enrolled in 80 retrospective studies. For comparisons across studies, each reporting on only one endoscopic method, we used a random effects meta-regression of the log-odds of the outcome of treatment in each study. "Neural networks" as a data mining technique was employed in order to establish a prediction model of lymph node status in superficial submucosal esophageal carcinoma. Another data mining technique, the "feature selection and root cause analysis", was used to identify the most impor-tant predictors of local recurrence and metachronous cancer development in endoscopically resected patients, and lymph node positivity in squamous carcinoma (SCC) and adenocarcinoma (ADC) separately in surgically resected patients. RESULTS: Endoscopically resected patients: Low grade dysplasia was observed in 4% of patients, high grade dysplasia in 14.6%, carcinoma in situ in 19%, mucosal cancer in 54%, and submucosal cancer in 16% of patients. There were no significant differences between endoscopic mucosal resection and endoscopic submucosal dissection (ESD) for the following parameters: complications, patients submitted to surgery, positive margins, lymph node positivity, local recurrence and metachronous cancer. With regard to piecemeal resection, ESD performed better since the number of cases was significantly less [coefficient: -7.709438, 95%CI: (-11.03803, -4.380844), P < 0.001]; hence local recurrence rates were significantly lower [coefficient: -4.033528, 95%CI: (-6.151498, -1.915559),P < 0.01]. A higher rate of esophageal stenosis was observed following ESD [coefficient: 7.322266, 95%CI: (3.810146, 10.83439), P < 0.001]. A significantly greater number of SCC patients were submitted to surgery (log-odds, ADC: -2.1206 ± 0.6249 vs SCC: 4.1356 ± 0.4038, P < 0.05). The odds for re-classification of tumor stage after endoscopic resection were 53% and 39% for ADC and SCC, respectively. Local tumor recurrence was best predicted by grade 3 differentiation and piecemeal resection, metachronous cancer development by the carcinoma in situ component, and lymph node positivity by lymphovascular invasion. With regard to surgically resected patients: Significant differences in patients with positive lymph nodes were observed between ADC and SCC [coefficient: 1.889569, 95%CI: (0.3945146, 3.384624), P<0.01). In contrast, lymphovascular and microvascular invasion and grade 3 patients between histologic types were comparable, the respective rank order of the predictors of lymph node positivity was: Grade 3, lymphovascular invasion (L+), microvascular invasion (V+), submucosal (Sm) 3 invasion, Sm2 invasion and Sm1 invasion. Histologic type (ADC/SCC) was not included in the model. The best predictors for SCC lymph node positivity were Sm3 invasion and (V+). For ADC, the most important predictor was (L+). CONCLUSION: Local tumor recurrence is predicted by grade 3, metachronous cancer by the carcinoma insitu component, and lymph node positivity by L+. T1b cancer should be treated with surgical resection.

Comparative evaluation of modified neem leaf, neem leaf and woodash extracts on soil fertility improvement, growth and yields of maize (<i>Zea mays</i>L.) and watermelon (<i>Citrullus lanatus</i>) (Sole and Intercrop)

Two field experiments were carried out at Akure (7oN, 5o101E) in the rainforest zone of Nigeria in 2006 and 2007 to determine the effectiveness of neem leaf, woodash and modified neem leaf extracts as fertilizer sources in improving soil fertility, growth and yield of maize (Zea mays L) and watermelon (Citrulus lanatus) sole and intercrop. There were six treatments namely, poultry manure, neem leaf extract (sole), woodash extract, modified neem leaf (neem leaf + woodash), NPK 15-15-15 and a control (no fertilizer nor extract), replicated three times and arranged in a randomized complete block design (RCB). The extracts (neem leaf, wood ash and modified neem leaf) were applied at 1200 litres per hectare each, NPK 15-15-15 at 300 kg/ha and poultry was applied at 6t/ha. The results showed that there were significant increases (P 2O), K, Ca, Mg, Na, O.M, P and N compared to NPK 15-15-15 and neem leaf extract. For instance, modified neem leaf extract increased soil pH (H2O), K, Ca, Mg, Na, O.M, P and N by 12.4%, 32.8%, 25%, 23.7%, 19.32%, 17.24% and 20% respectively compared to neem leaf extract under intercrop plot. The high soil K/Ca, K/Mg and P/Mg ratios in the NPK 15-15-15 fertilizer treatment led to an imbalance in the supply of P, K, Ca and Mg nutrients to maize and watermelon crops. The least values for growth, yield and soil parameters were recorded under the control treatment. In these experiments, modified neem leaf extract (woodash + neem leaf extracts) applied at 1200 litres/ha was the most effective in improving soil fertility, growth and yield of maize and watermelon (sole and intercrop) and could substitute for 6 tons per hectare of poultry manure and 300kg/ha of NPK 15-15-15 fertilizer.

Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review

BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algorithm similar to deep learning,has demonstrated its capability to recognise specific features that can detect pathological lesions.AIM To assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODS The databases PubMed,EMBASE,and the Web of Science and research books were systematically searched using related keywords.Studies analysing pathological anatomy,cellular,and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer,differentiating cancer from other lesions,or staging the lesion.The data were extracted as per a predefined extraction.The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed.The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTS A total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified.The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions(n=6),HCC from cirrhosis or development of new tumours(n=3),and HCC nuclei grading or segmentation(n=2).The CNNs showed satisfactory levels of accuracy.The studies aimed at detecting lesions(n=4),classification(n=5),and segmentation(n=2).Several methods were used to assess the accuracy of CNN models used.CONCLUSION The role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies.While a few limitations have been identified in these studies,overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liver cancers images.

Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

Diagnosis of Sugarcane White Leaf Disease Using the Highly Sensitive DNA Based Voltammetric Electrochemical Determination

A simple and highly sensitive analysis by electrochemical voltammetry has been developed for diagnosis of the most destructive crop disease in Thailand known as sugarcane white leaf (SCWL). Determination of the corresponding DNA interaction has been obtained from the voltammetric signals of electroactive redox methylene blue (MB) by means of cyclic and differential pulse voltammetry. In this study, a chitosan-modified glassy carbon electrode (GCE) was created by self-assembly to produce electrostatic platform for effective immobilization of the DNA. Fabrication of SCWL-DNA hybridization detection system was performed by immobilizing the ssDNA probe as a specific sensor onto chitosan-modified GCE. Hybridization of complementary DNA from the real samples could then be detected by its respective MB signal. This fabricated DNA probe sensor was shown to be capable for discriminative identification among the DNAs from SCWL plants, mosaic virus infected sugarcane and healthy sugarcane plants. Relationship between the specific hybridization signal and DNA target concentration was also observed under optimal condition. The detection limit of 4.709 ng/μl with the regression coefficient (R2) of 0.998 and overall RSD of 2.44% were obtained by response curve fit analysis. The actual SCWL-ssDNA immobilization and hybridizing event were subsequently confirmed by an observation under atomic force microscope. Thus these experiments demonstrate the first successful and effective DNA based voltammetric electrochemical determination for a verification of the specific pathogenic infection within plants from the real epidemic field.

Seasonal Difference of the Deep Currents in the Luzon Strait during October 2008-January 2009 and July-August 2009

Based on direct current measurements from two separated cruises in October 2008-January 2009 and July-August 2009, we obtained a valuable deep current observation of the Luzon Strait (LS). Rectified wavelet power spectra analysis (RWPSA) and the geostrophic current calculation are used to study the deep current. We find that the deep current differs in different seasons. The current is strongest in autumn (October-November) and weaker in summer (July-August) and in winter (December-January). The cyclonic and anti-cyclonic meander with different subtidal current directions plays an important role in the seasonal difference of the deep current in the LS. The observed seasonal difference of the deep current in the LS is connected with the deep current observed at the western boundary of the northern Philippine Basin and is also linked with the overflow near the central Bashi Channel and Luzon Trough. The RWPSA of the long observation suggests the dominant periods of 8 d, 19 d in the deep current. The dynamical cause of the resulting velocity distribution at 1850 and 1760 m is the pressure field and bottom topography steering. The observed deep current agrees well with the geostrophic current calculation.

Early identification of stroke through deep learning with multi-modal human speech and movement data

Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.