Controlling Chaos in a Neural Network Based on the Phase Space Constraint

The chaotic neural network constructed with chaotic neurons exhibits very rich dynamic behaviors and has a nonperiodic associative memory. In the chaotic neural network, however, it is dicult to distinguish the stored patters from others, because the states of output of the network are in chaos. In order to apply the nonperiodic associative memory into information search and pattern identication, etc, it is necessary to control chaos in this chaotic neural network. In this paper, the phase space constraint method focused on the chaotic neural network is proposed. By analyzing the orbital of the network in phase space, we chose a part of states to be disturbed. In this way, the evolutional spaces of the strange attractors are constrained. The computer simulation proves that the chaos in the chaotic neural network can be controlled with above method and the network can converge in one of its stored patterns or their reverses which has the smallest Hamming distance with the initial state of the network. The work claries the application prospect of the associative dynamics of the chaotic neural network

RESTORING GUEST’S CONFIDENCE BY DELIVERING TRUE HOSPITALITY DURING COVID-19 PANDEMIC, CASE OF INTERCONTINENTAL ZHUHAI HOTEL, IHG

The coronavirus (COVID-19 pandemic) has unprecedented effects on the hospitality industry. Challenges resulting from the loss of demand caused by travel restrictions, national and local lockdowns, social distancing measures, and truncated hours of operation, and sudden business closures following governmental lockdown orders (Hall et al., 2020). This study used the case of InterContinental Zhuhai Hotel to investigate how to lead and deliver true hospitality to guests during COVID-19 pandemic. Insights were explored to restore guest’s confidence in luxury hotels in this unprecedented period. A purposeful sampling method is employed to investigate by using in-depth interviews with hoteliers. Based on thematic analysis of in-depth interviews with hoteliers about the ways to lead and deliver true hospitality to restore guest’s confidence during COVID-19 pandemic, 10 Cs were identified from this study: 1) Charismatic Leadership Driving all Teams Stepping out of Comfort Zone Courageously! 2) Comprehensive Delivering True Hospitality of Incredible and Caring Services 3) Conclusive and Strict Sanitary Measures 4) Caring Services and Careful Disinfected Measures by Front Office Team 5) Convincing Cleaning Standards by Rooms Services Team 6) Creative Culinary Produced by Food and Beverages Team 7) Changeable Sales Strategies but Unchangeable Spirit of True Hospitality by Sales Team 8) Communicative and Creative Marketing Communications by Marketing and Communication Team 9) Compassionate and Supportive Human Resources Team 10) Committed and Competent Teams Coping COVID-19 Pandemic Cooperatively The implications of the study are that these strategies are expected to impress and enhance guest experiences which are reflected on online ratings and comments. Based on the research results, a framework for leading and delivering true hospitality to restore guest’s confidence during COVID-19 pandemic was proposed. The practical Implications for hoteliers, leading and delivering true hospitality is a fundamental but powerful strategy to restore guest’s confidence and achieve sustainable competitive advantages in these fierce and harsh market conditions. For guests, delivering true hospitality of incredible and caring services that can meet and exceed their expectations can restore their confidence towards the hotel and further build loyalty to the hotel even during COVID-19 pandemic. The findings show practical insights to lead and deliver true hospitality to restore guest’s confidence in a luxury hotel. The study investigates one of the luxury hotels in Zhuhai, China, an area where research is sparse within the context of Asia and almost non-existent in Zhuhai, China. From this perspective the study contributes to the hotel management and service delivery of luxury hotels during health crisis literature. Keywords: Covid-19 Pandemic, Hotel Management, Services Delivery

Evaluating the ESL Reading Texts for Intermediate Learners of English from the Perspective of Students

In order to provide an evaluation of the suitability of reading texts from the perspective of students in university-based intensive English programme this study examined 53 international ESL intermediate learners perceptions of reading texts for a period of 14 weeks reading proficiency lessons Features evaluated include content readability exploitability and authenticity of the reading texts The participants responded to a textbook evaluation questionnaire to express their perceptions with reference to the features of the reading texts Results indicated the extent of appropriateness of the reading texts incorporated in the programme s reading textbook used by intermediate learners of English Further consideration must be given to text selection by including the aspect of authentic text presentatio

Ministry of Health National EMERGENCY OBSTETRIC AND NEWBORN CARE

III. Technical Support team from NMCHC

Novel Branches of (0,2) Theories

We show that recently proposed linear sigma models with torsion can be obtained from unconventional branches of conventional gauge theories. This observation puts models with log interactions on firm footing. If non-anomalous multiplets are integrated out, the resulting low-energy theory involves log interactions of neutral fields. For these cases, we find a sigma model geometry which is both non-toric and includes brane sources. These are heterotic sigma models with branes. Surprisingly, there are massive models with compact complex non-Kahler target spaces, which include brane/anti-brane sources. The simplest conformal models describe wrapped heterotic NS5-branes. We present examples of both types.Comment: 36 pages, LaTeX, 2 figures; typo in Appendix fixed; references added and additional minor change

Partial Covering Arrays: Algorithms and Asymptotics

A covering array $\mathsf{CA}(N;t,k,v)$ is an $N\times k$ array with entries in $\{1, 2, \ldots , v\}$, for which every $N\times t$ subarray contains each $t$-tuple of $\{1, 2, \ldots , v\}^t$ among its rows. Covering arrays find application in interaction testing, including software and hardware testing, advanced materials development, and biological systems. A central question is to determine or bound $\mathsf{CAN}(t,k,v)$, the minimum number $N$ of rows of a $\mathsf{CA}(N;t,k,v)$. The well known bound $\mathsf{CAN}(t,k,v)=O((t-1)v^t\log k)$ is not too far from being asymptotically optimal. Sensible relaxations of the covering requirement arise when (1) the set $\{1, 2, \ldots , v\}^t$ need only be contained among the rows of at least $(1-\epsilon)\binom{k}{t}$ of the $N\times t$ subarrays and (2) the rows of every $N\times t$ subarray need only contain a (large) subset of $\{1, 2, \ldots , v\}^t$. In this paper, using probabilistic methods, significant improvements on the covering array upper bound are established for both relaxations, and for the conjunction of the two. In each case, a randomized algorithm constructs such arrays in expected polynomial time

Minim Typing – A Rapid and Low Cost MLST Based Typing Tool for Klebsiella pneumoniae

Here we report a single nucleotide polymorphism (SNP) based genotyping method for Klebsiella pneumoniae utilising high-resolution melting (HRM) analysis of fragments within the multilocus sequence typing (MLST) loci. The approach is termed mini-MLST or Minim typing and it has previously been applied to Streptococcus pyogenes, Staphylococcus aureus and Enterococcus faecium. Six SNPs were derived from concatenated MLST sequences on the basis of maximisation of the Simpsons Index of Diversity (D). DNA fragments incorporating these SNPs and predicted to be suitable for HRM analysis were designed. Using the assumption that HRM alleles are defined by G+C content, Minim typing using six fragments was predicted to provide a D = 0.979 against known STs. The method was tested against 202 K. pneumoniae using a blinded approach in which the MLST analyses were performed after the HRM analyses. The HRM-based alleles were indeed in accordance with G+C content, and the Minim typing identified known STs and flagged new STs. The tonB MLST locus was determined to be very diverse, and the two Minim fragments located herein contribute greatly to the resolving power. However these fragments are refractory to amplification in a minority of isolates. Therefore, we assessed the performance of two additional formats: one using only the four fragments located outside the tonB gene (D = 0.929), and the other using HRM data from these four fragments in conjunction with sequencing of the tonB MLST fragment (D = 0.995). The HRM assays were developed on the Rotorgene 6000, and the method was shown to also be robust on the LightCycler 480, allowing a 384-well high through-put format. The assay provides rapid, robust and low-cost typing with fully portable results that can directly be related to current MLST data. Minim typing in combination with molecular screening for antibiotic resistance markers can be a powerful surveillance tool kit

Hybrid of swarm intelligent algorithms in medical applications

In this paper, we designed a hybrid of swarm intelligence algorithms to diagnose hepatitis, breast tissue, and dermatology conditions in patients with such infection. The effectiveness of hybrid swarm intelligent algorithms was studied since no single algorithm is effective in solving all types of problems. In this study, feed forward and Elman recurrent neural network (ERN) with swarm intelligent algorithms is used for the classification of the mentioned diseases. The capabilities of six (6) global optimization learning algorithms were studied and their performances in training as well as testing were compared. These algorithms include: hybrid of Cuckoo Search algorithm and Levenberg-Marquardt (LM) (CSLM), Cuckoo Search algorithm (CS) and backpropagation (BP) (CSBP), CS and ERN (CSERN), Artificial Bee Colony (ABC) and LM (ABCLM), ABC and BP (ABCBP), Genetic Algorithm (GA) and BP (GANN). Simulation comparative results indicated that the classification accuracy and run time of the CSLM outperform the CSERN, GANN, ABCBP, ABCLM, and CSBP in the breast tissue dataset. On the other hand, the CSERN performs better than the CSLM, GANN, ABCBP, ABCLM, and CSBP in both th

Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas