Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimisation of water distribution network design: a critical review

This paper surveys the literature on the optimisation of water distribution network design. The water distribution network design (WDND) optimisation problem entails finding the type and diameter of each pipe in the network so that the total cost of the network is minimised without violating any hydraulic constraints. This is a difficult combinatorial optimisation problem, in which decision variables are discrete and both cost function and constraints are non-linear. Over the past 30 years, a large number of methods, especially in the field of (meta)heuristics, have been developed to solve this problem, most of which obtain good results on the available benchmark networks. In addition to outlining the basic features of each method, a detailed computational comparison is presented. The need for an adequate set of benchmark instances and the desirable properties of an instance set generator, are discussed. The paper concludes by pointing out some issues with the current state of the art in this domain and presents some suggestions for future research in this domain.
The possibility of application of the potentiometric sensor with all-solid-state electrodes containing lipid-polymer membranes for classification of tested black leaf teas in terms of their quality

Potentiometric sensor with all-solid-state electrodes containing lipid-polymer membranes for a set of black leaf teas coming from four countries (China, India, Sri Lanka and Kenia) classification was applied. The tested tea samples were reassigned to quality classes marked with num- bers I–V according to the information available. The sensor results of tea samples are presented in the form of radar charts. It was observed that, in the majority of cases samples with a similar radar chart shape belong to the same quality class. Based on principal component analysis and agglomerative hierarchical clustering analyses four groups of leaf tea samples and two sepa- rate items were distinguished. The first group was constituted by the highest quality Yunnan teas. The second group is made up of high-quality Indian teas and one tea sample of undeclared qual- ity. The third group includes tea samples of unknown quality assigned to quality Class IV. The last group distinguished is constituted by the sample of Indian Madras and Ceylon tea, which are imported for retail trade for blends. This grouping largely corresponds to the quality classes of tested tea samples. The obtained results suggest that potentiometric sensor with all-solid-state elec- trodes containing lipid-polymer membranes might be used for discrimination of black leaf teas of different quality and also for determination of quality class of an unknown tea sample.
Testing and assessment in counselling

Counselling has developed in recent years into a humanistic partnership in problem solving and growth. Consistent with this development is the client's participation in decisions regarding all aspects of the counselling relationship. Testing and assessment need to be included here. In this regard, clients need to realize that tests are no more than instruments for furnishing information about themselves, as well as a guide for the counselling process and future decision making. Ultimately, test use by counsellors must be seen as an adjunct to the entire counselling process. Test results provide descriptive and objective data which help the counsellor to assist clients better in making the choices that will affect their lives. In order to make the best use of available tests in a counselling relationship, the process of testing and the issues which surround the process must be examined.
Coordination and Management of COVID-19 in Africa through Health Operations and Technical Expertise Pillar: A Case Study from WHO AFRO One Year into Response

Background: following the importation of the first Coronavirus disease 2019 (COVID-19) case into Africa on 14 February 2020 in Egypt, the World Health Organisation (WHO) regional office for Africa (AFRO) activated a three-level incident management support team (IMST), with technical pillars, to coordinate planning, implementing, supervision, and monitoring of the situation and progress of implementation as well as response to the pandemic in the region. At WHO AFRO, one of the pillars was the health operations and technical expertise (HOTE) pillar with five sub-pillars: case management, infection prevention and control, risk communication and community engagement, laboratory, and emergency medical team (EMT). This paper documents the learnings (both positive and negative for consideration of change) from the activities of the HOTE pillar and recommends future actions for improving its coordination for future emergencies, especially for multi-country outbreaks or pandemic emergency responses. Method: we conducted a document review of the HOTE pillar coordination meetings’ minutes, reports, policy and strategy documents of the activities, and outcomes and feedback on updates on the HOTE pillar given at regular intervals to the Regional IMST. In addition, key informant interviews were conducted with 14 members of the HOTE sub pillar. Key Learnings: the pandemic response revealed that shared decision making, collaborative coordination, and planning have been significant in the COVID-19 response in Africa. The HOTE pillar’s response structure contributed to attaining the IMST objectives in the African region and translated to timely support for the WHO AFRO and the member states. However, while the coordination mechanism appeared robust, some challenges included duplication of coordination efforts, communication, documentation, and information management. Recommendations: we recommend streamlining the flow of information to better understand the challenges that countries face. There is a need to define the role and responsibilities of sub-pillar team members and provide new team members with information briefs to guide them on where and how to access internal information and work under the pillar. A unified documentation system is important and could help to strengthen intra-pillar collaboration and communication. Various indicators should be developed to constantly monitor the HOTE team’s deliverables, performance and its members.
Effect of WHO-SCC based intra-department mentoring program on quality of intrapartum care in public sector secondary hospitals in Andhra Pradesh, India: Pre-post mixed methods evaluation

Quality of intrapartum care is essential for improving pregnancy outcomes; several models for improving performance are tested, globally. Dakshata is one such WHO SCC-based national program—improving resources, providers’ competence, and accountability—in public sector secondary care hospitals of India. Andhra Pradesh state devised strategy of mentoring by the handpicked member from within the obstetric team, supported by external technical partner. We evaluated the effectiveness and assessed contextual factors to success of the program. We conducted pre and post mentoring mixed-method surveys to evaluate the change in evidence-based intrapartum and newborn care practices and stillbirth rates, across 23 of 38 eligible hospitals. We directly observed obstetric assessments and childbirth, extracted data from casesheets and registers, interviewed beneficiaries and conducted facility surveys. We in-depth interviewed stakeholders from state, district and facility managers, mentors and obstetric staff, and external managers for theory-driven qualitative assessment. After one year we found, average adherence to practices sustained high during admission (81%, 81%); improved during childbirth (78%, 86%; p = 0.016); moderate within one hour of birth (72%, 71%), and poor postpartum care before discharge (46% to 43%). Stillbirths reduced from 11(95% CI, 9–13) to 4(3–5) per 1000 births (p<0.001). Some practices did not improve even after sustained reinforcement. Commitment from state, engaging district officers, monitoring and feedback by external managers enabled supportive setting. The structured training and mentoring package, and periodic assessments delivered under supervision ensured the standards of mentoring. The mentoring model is acceptable, effective, less costly and scalable; appears sustainable if state commits to institutionalising a long-term mentoring with adequate monitoring. We conclude that the SCC-based mentoring and skill building program showed improvement in practices during childbirth while it sustained high levels of care during admission, but no improvement in postpartum care. The state needs to monitor and ensure continuous mentoring with required infrastructural support.
ANOTHER DERIVATION OF THE KARMARKAR DIRECTION FOR LINEAR PROGRAMMING

This paper provides another derivation of the Karmarkar direction for linear programming. It is strongly motivated by derivations of Gonzaga, but we show how the direction can be viewed as a steepest descent direction in the original feasible region corresponding to a metric different from the Euclidean one. We show that a fixed decrease in the potential function can be obtained by taking a step in this direction, as long as a certain assumption holds. We give an example showing that such a restriction is necessary, and discuss two ways to remove it.
effects of moderate intensity continuous and interval training on physical fitness in obesity

The objectives of this study were to study and compare the effects of moderate-intensity continuous and interval training on physical fitness in obesity. The participants were 48 male and female (fat % over 20 in men and 30 in women, aged between 20 – 25 years) and were randomized into three groups: 12 weeks of continuous along exercise (CA), 60-65% MHR (7 men and 8 women); 12 weeks interval exercise (IG), 60-65% MHR (208 - (0.7 x Age) alternate with 70-75% MHR every 1 minute (9 men and 8 women); and control group (CG) (8 men and 8 women). At baseline VO2max, total body weight, body fat percentage, and visceral fat data did not differ significantly between the three groups (p > 0.05). After 12 weeks of exercise intervention, both men and women participants in CA and IG had significantly improved in VO2max, total body weight, body fat percentage, no significantly in CG. There is no significant between CA and IG in every variable but significantly between CA and CG and between IG and CG in both men and women participants.
Bridging Gender Equality in the Workplace: The Role of Personal Branding and Self-Confidence

Considering the social psychological, social cognitive, and economic factors, this paper aims to examine how gender equality can be bridged in the workplace through the empowerment of women workers’ ability to craft and narrate personal branding and self-confidence to gain better condition in the workplace. A total of 200 questionnaires are distributed to 200 woman employees working in 10 manufacturing industries in 5 regions in West Java, Indonesia. The independent variable of the personal branding scale is adopted from Peter and Montoya, the self-confidence scale is that of Frendika, and the dependent variable scale of gender equality is taken from McKinsey Global Institute's (MGI's) Power of Parity. A five point-Likert rating scale (1=lowest – 5=highest) is applied for each item; SPSS v 25 is also employed to analyze the data collected using AMOS v 25. The result shows that personal branding which includes relevance, consistency, and visibility has a positive and significantly has more dominant effect than self-confidence to bridge gender equality in the workplace.
Deep Reinforcement Learning Ensemble for Detecting Anomaly in Telemetry Water Level Data

Water levels in rivers are measured by various devices installed mostly in remote locations along the rivers, and the collected data are then transmitted via telemetry systems to a data centre for further analysis and utilisation, including producing early warnings for risk situations. So, the data quality is essential. However, the devices in the telemetry station may malfunction and cause errors in the data, which can result in false alarms or missed true alarms. Finding these errors requires experienced humans with specialised knowledge, which is very time-consuming and also inconsistent. Thus, there is a need to develop an automated approach. In this paper, we firstly investigated the applicability of Deep Reinforcement Learning (DRL). The testing results show that whilst they are more accurate than some other machine learning models, particularly in identifying unknown anomalies, they lacked consistency. Therefore, we proposed an ensemble approach that combines DRL models to improve consistency and also accuracy. Compared with other models, including Multilayer Perceptrons (MLP) and Long Short-Term Memory (LSTM), our ensemble models are not only more accurate in most cases, but more importantly, more reliable.
Association of patient characteristics with clinical outcomes in a cohort of hospitalised patients with SARS-CoV-2 infection in a Greek referral centre for COVID-19

Abstract Patient-important outcomes related to coronavirus disease 2019 (COVID-19) continue to drive the pandemic response across the globe. Various prognostic factors for COVID-19 severity have emerged and their replication across different clinical settings providing health services is ongoing. We aimed to describe the clinical characteristics and their association with outcomes in patients hospitalised with COVID-19 in the University Hospital of Ioannina. We assessed a cohort of 681 consecutively hospitalised patients with COVID-19 from January 2020 to December 2021. Demographic data, underlying comorbidities, clinical presentation, biochemical markers, radiologic findings, COVID-19 treatment and outcome data were collected at the first day of hospitalisation and up to 90 days. Multivariable Cox regression analyses were performed to investigate the associations between clinical characteristics (hazard ratios (HRs) per standard deviation (s.d.)) with intubation and/or mortality status. The participants' mean age was 62.8 (s.d., 16.9) years and 57% were males. The most common comorbidities were hypertension (45%), cardiovascular disease (19%) and diabetes mellitus (21%). Patients usually presented with fever (81%), cough (50%) and dyspnoea (27%), while lymphopenia and increased inflammatory markers were the most common laboratory abnormalities. Overall, 55 patients (8%) were intubated, and 86 patients (13%) died. There were statistically significant positive associations between intubation or death with age (HR: 2.59; 95% CI 1.52–4.40), lactate dehydrogenase (HR: 1.44; 95% CI 1.04–1.98), pO2/FiO2 ratio < 100 mmHg (HR: 3.52; 95% CI 1.14–10.84), and inverse association with absolute lymphocyte count (HR: 0.54; 95% CI 0.33–0.87). These data might help to identify points for improvement in the management of COVID-19 patients.
Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network

Due to the high mobility of nodes and the complexity of the mission environment, mission-oriented UAV networks are not only subject to frequent topology changes, but also to the risk of being compromised, hijacked and corrupted. As a result, an operating UAV network is essentially a Byzantine distributed system whose physical structure and node trustworthiness change over time. How to implement the global management of UAV networks to achieve a rational allocation of UAV network resources and reconfiguration of trusted networks is a problem worthy of in-depth study. The method proposed in this paper introduces a lightweight storage blockchain in the UAV network through two-stage consensus, firstly performing data consensus on the local state records of the nodes, then performing decision consensus on the data consensus results using algorithms such as fuzzy K-Modes clustering and global trustworthiness assessment, and finally recording the decision consensus results into a new block as the new configuration information of the UAV network. A lightweight storage blockchain-assisted trusted zone routing protocol (BC_TZRP) is designed to dynamically and adaptively build configurable trusted networks in a way that the blockchain continuously adds new blocks. Using QualNet simulation experimental software, an experimental comparison between the classical routing protocol for mobile self-organizing networks and the traditional consensus algorithm for blockchains is conducted. The results show that the approach has significant advantages in terms of packet delivery rate, routing overhead and average end-to-end delay, and can effectively improve the overall working life and fault tolerance of the UAV network.
A 10-Bit 400-KS/s Low Noise Asynchronous SAR ADC with Dual-Domain Comparator for Input-Referred Noise Reduction

This paper presents a low noise 0.6-V 400-kS/s asynchronous successive approximation register (SAR) analog-to-digital converter (ADC) for input-referred noise reduction. A dual-domain comparator is proposed to optimize the power, noise, and sampling rate of the ADC in the 10-bit conversion. In order to optimize the figure of merits (FoM) of the ADC, the 10-bit conversion consists of a 7-bit coarse conversion with the double-tail dynamic comparator and a 3-bit fine conversion with the VCDL-based time-domain comparator. An asynchronous timing controller is also proposed to improve the ADC sampling rate and optimize the power consumption of the dual-domain comparator. The proposed SAR ADC is fabricated in 180-nm CMOS technology with an area of 0.836 mm2. At a 0.6-V supply voltage and a 400-kS/s sampling rate, the implemented SAR ADC achieves a signal-to-noise and distortion ratio (SNDR) of 56.59 dB and an effective number of bits (ENOB) of 9.16 bits. The peak values of DNL and INL are +0.47/−0.53 LSB and +0.92/−0.64 LSB, respectively. The FoM is 10.31 fJ/conversion step with a power consumption of 2.36 μW.
Modelling and Simulation of Interference Cancellation Receiver for MIMO Multicarrier CDMA based Cognitive Radio

In this paper, modeling and simulation of a receiver with interference cancellation technique for the downlink transmission of multiple input multiple output (MIMO) multicarrier code-division multiple access (MC-CDMA) based cognitive radio is investigated. Synchronous transmission of multiple users’ signals over frequency selective Rayleigh channel is considered. Linear MMSE frequency domain equalization is utilized to overcome channel effect. Orthogonal Variable Spreading Factor (OVSF) codes are used to support users of different data bit rates. An adaptive subcarrier deactivation method is proposed for deactivating a set of subcarriers used for downlink transmission by the cognitive radio base station that overlap with primary user’s transmission in order to avoid causing interference to the primary user. Orthogonality between the spreading codes is lost due to subcarrier deactivation which causes interference among secondary cognitive radio users. In this paper a modified Group-wise Successive Interference Cancellation (m-GSIC) technique is proposed to suppress the interference caused due to subcarrier deactivation. The simulation results show that the proposed interference cancellation technique results in better BER performance over conventional interference cancellation techniques
Optimization Design and Analysis of Bionic Friction Reducing Nozzle in Oil Shale High-Pressure Jet Mining

The borehole hydraulic mining method has unique advantages for underground oil shale exploitation. Breaking rock with a high-pressure water jet is a crucial step to ensure the smooth implementation of borehole hydraulic mining in oil shale. The hydraulic performance of the nozzle determines the efficiency and quality of high-pressure water jet technology. To obtain a superior hydraulic performance nozzle, based on the bionic non-smooth theory, a circular groove was selected as the bionic unit to design a bionic straight cone nozzle. The structural parameters of the circular groove include the groove depth, width, and slot pitch. The optimization objective was to minimize the pressure drop, where the fluid has the least resistance. A genetic algorithm was used to optimize the structural parameters of the circular grooves in the inlet and outlet sections of the bionic straight cone nozzle. The optimal structural parameters of the nozzle were as follows: the inlet diameter was 15 mm, the inlet length was 20 mm, the outlet diameter was 4 mm, the length-to-diameter ratio was 3, and the contraction angle was 30°. In addition, in the inlet section, the groove width, slot pitch, and groove depth were 3.9 mm, 5.2 mm, and 5.5 mm, respectively, and the number of circular grooves was 2. Moreover, in the outlet section, the groove width, slot pitch, and groove depth were 2.25 mm, 3 mm, and 5.5 mm, respectively, and the number of circular grooves was 2. The CFD numerical simulation results showed that under the same numerical simulation conditions, compared with the conventional straight cone nozzle, the bionic straight cone nozzle velocity increase rate could reach 13.45%. The research results can provide scientific and valuable references for borehole hydraulic mining of high-pressure water jets in oil shale drilling.
Bidirectional Flow Decision Tree for Reliable Remote Sensing Image Scene Classification

Remote sensing image scene classification (RSISC), which aims to classify scene categories for remote sensing imagery, has broad applications in various fields. Recent deep learning (DL) successes have led to a new wave of RSISC applications; however, they lack explainability and trustworthiness. Here, we propose a bidirectional flow decision tree (BFDT) module to create a reliable RS scene classification framework. Our algorithm combines BFDT and Convolutional Neural Networks (CNNs) to make the decision process easily interpretable. First, we extract multilevel feature information from the pretrained CNN model, which provides the basis for constructing the subsequent hierarchical structure. Then the model uses the discriminative nature of scene features at different levels to gradually refine similar subsets and learn the interclass hierarchy. Meanwhile, the last fully connected layer embeds decision rules for the decision tree from the bottom up. Finally, the cascading softmax loss is used to train and learn the depth features based on the hierarchical structure formed by the tree structure that contains rich remote sensing information. We also discovered that superclass results can be obtained well for unseen classes due to its unique tree structure hierarchical property, which results in our model having a good generalization effect. The experimental results align with theoretical predictions using three popular datasets. Our proposed framework provides explainable results, leading to correctable and trustworthy approaches.
A Comparison of the Egalitarian Parenting Relationship between America after WWI and China after WWII

A healthy parent-child relationship can be defined as the relationship where parents provide for their child's physical, emotional, and developmental needs. Egalitarian parenting is taken to be the representative of American image and the west and is after by other countries in the world. As comparison with the West, China, the representative of the eastern in the world is always criticized for the authoritarian parent-child relationship. This paper analyzes the development and performance of egalitarianism in children-rearing in America after WWI and China after WWII from four periods: authoritarian generation VS founding generation; depression generation VS turbulent construction generation; baby-boom generation VS Reform and opening up generation; info-child generation(1965-)vs (1994-), and investigates the reasons from the aspects of ideology and socialization.