Search results for: evaluation capacity building

Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Aziza Al-Sawafi

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Introduction: The evidence of family interventions in schizophrenia is robust primarily in high-income settings. However, they have been adapted to other settings and cultures to improve effectiveness and acceptability. In Oman, there is limited integration of psychosocial interventions in the treatment of schizophrenia. Therefore, the adaptation of family intervention to the Omani culture may facilitate its uptake. Most service users in Oman live with their families outside the healthcare system, and nothing is known about their experience, needs, or resources. Furthermore, understanding caregivers' and mental health professionals' preferences, perceptions, and experience is a fundamental element in the process of intervention development. Therefore, this study aims to develop a culturally sensitive, feasible, and acceptable family intervention for relatives living with schizophrenia in Oman. Method: The Medical Research Council's framework for the evaluation of complex health care interventions provided the conceptual structure for the study. The development phase was carried out, which involved three stages: 1) systematically reviewing the available literature regarding culturally adapted family interventions in the Arab world 2) In-depth interviews with caregivers to explore their experience and perceived needs and preferences regarding intervention 3) A focus group study involving health professionals to explore the acceptability and feasibility of delivering the family intervention in the Omani context. Data synthesis determined the design of the proposed intervention according to the findings obtained from the previous stages. Results: Stage one: The systematic review found limited evidence of culturally-adapted family interventions in the Arab region. However, the cultural adaptation process was comprehensive, and the implementation was reported to be feasible and acceptable. Stage two: The experience of family caregivers illuminated four main themes: burden, stigma, violence, and family needs. Burdens of care included objective and subjective burdens, positive feelings, and coping mechanisms. Caregivers gave their opinion about the content and preference of the intervention from their personal experiences. Stage three: mental health professionals discussed the delivery system of the intervention from a clinical standpoint concerning issues and barriers to implementation. They recommended modifications to the components of the intervention to ensure its acceptability and feasibility in the local setting. Data synthesis was carried out, and the intervention was designed. Conclusion: This study provides evidence of the potential applicability and acceptability of a culturally sensitive family intervention for families of individuals with schizophrenia in Oman. However, more work needs to be done to test the feasibility of the study and overcome the practical challenges.

Keywords: cultural-adaptation, family intervention, Oman, schizophrenia

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Authors: Sidramappa Gaddnakeri, Lokanath Malligawad

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Any kind of research on production technology of individual crop / commodity /breed has not brought sustainability or stability in crop production. The sustainability of the system over years depends on the maintenance of the soil health. Organic production system includes use of organic manures, biofertilizers, green manuring for nutrient supply and biopesticides for plant protection helps to sustain the productivity even under adverse climatic condition. The study was initiated to evaluate the performance of different cropping systems under organic, inorganic and integrated production systems at The Institute of Organic Farming, University of Agricultural Sciences, Dharwad (Karnataka-India) under ICAR Network Project on Organic Farming. The trial was conducted for four years (2013-14 to 2016-17) on fixed site. Five cropping systems viz., sequence cropping of cowpea – safflower, greengram– rabi sorghum, maize-bengalgram, sole cropping of pigeonpea and intercropping of groundnut + cotton were evaluated under six nutrient management practices. The nutrient management practices are NM1 (100% Organic farming (Organic manures equivalent to 100% N (Cereals/cotton) or 100% P2O5 (Legumes), NM2 (75% Organic farming (Organic manures equivalent to 75% N (Cereals/cotton) or 100% P2O5 (Legumes) + Cow urine and Vermi-wash application), NM3 (Integrated farming (50% Organic + 50% Inorganic nutrients, NM4 (Integrated farming (75% Organic + 25% Inorganic nutrients, NM5 (100% Inorganic farming (Recommended dose of inorganic fertilizers)) and NM6 (Recommended dose of inorganic fertilizers + Recommended rate of farm yard manure (FYM). Among the cropping systems evaluated for different production systems indicated that the Groundnut + Hybrid cotton (2:1) intercropping system found more remunerative as compared to Sole pigeonpea cropping system, Greengram-Sorghum sequence cropping system, Maize-Chickpea sequence cropping system and Cowpea-Safflower sequence cropping system irrespective of the production systems. Production practices involving application of recommended rates of fertilizers + recommended rates of organic manures (Farmyard manure) produced higher net monetary returns and higher B:C ratio as compared to integrated production system involving application of 50 % organics + 50 % inorganic and application of 75 % organics + 25 % inorganic and organic production system only Both the two organic production systems viz., 100 % Organic production system (Organic manures equivalent to 100 % N (Cereals/cotton) or 100 % P2O5 (Legumes) and 75 % Organic production system (Organic manures equivalent to 75 % N (Cereals) or 100 % P2O5 (Legumes) + Cow urine and Vermi-wash application) are found to be on par. Further, integrated production system involving application of organic manures and inorganic fertilizers found more beneficial over organic production systems.

Keywords: cropping systems, production systems, cowpea, safflower, greengram, pigeonpea, groundnut, cotton

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Authors: Vidhya Venugopal, Jeremiah Chinnadurai, Rebekah A. I. Lucas, Tord Kjellstrom, Bruno Lemke

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Introduction: The effects of climate change (CC) are largely discussed across the globe in terms of impacts on the environment and the general population, but the impacts on workers remain largely unexplored. The predicted rise in temperatures and heat events in the CC scenario have health implications on millions of workers in physically exerting jobs. The current health and productivity risks associated with heat exposures are characterized, future risk estimates as temperature rises and recommendations towards developing protective and preventive occupational health and safety guidelines for India are discussed. Methodology: Cross-sectional studies were conducted in several occupational sectors with workers engaged in moderate to heavy labor (n=1580). Quantitative data on heat exposures (WBGT°C), physiological heat strain indicators viz., Core temperature (CBT), Urine specific gravity (USG), Sweat rate (SwR) and qualitative data on heat-related health symptoms and productivity losses were collected. Data were analyzed for associations between heat exposures, health and productivity outcomes related to heat stress. Findings: Heat conditions exceeded the Threshold Limit Value (TLV) for safe manual work in 66% of the workers across several sectors (Avg.WBGT of 28.7°C±3.1°C). Widespread concerns about heat-related health outcomes (86%) were prevalent among workers exposed to high TLVs, with excessive sweating, fatigue and tiredness being commonly reported by workers. The heat stress indicators, core temperature (14%), Sweat rate (8%) and USG (9%), were above normal levels in the study population. A significant association was found between rise in Core Temperatures and WBGT exposures (p=0.000179) Elevated USG and SwR in the worker population indicate moderate dehydration, with potential risks of developing heat-related illnesses. In a steel industry with high heat exposures, an alarming 9% prevalence of kidney/urogenital anomalies was observed in a young workforce. Heat exposures above TLVs were associated with significantly increased odds of various adverse health outcomes (OR=2.43, 95% CI 1.88 to 3.13, p-value = <0.0001) and productivity losses (OR=1.79, 95% CI 1.32 to 2.4, p-value = 0.0002). Rough estimates for the number of workers who would be subjected to higher than TLV levels in the various RCP scenarios are RCP2.6 =79%, RCP4.5 & RCP6 = 81% and at RCP 8.5 = 85%. Rising temperatures due to CC has the capacity to further reduce already compromised health and productivity by subjecting the workers to increased heat exposures in the RCP scenarios are of concern for the country’s occupational health and economy. Conclusion: The findings of this study clearly identify that health protection from hot weather will become increasingly necessary in the Indian subcontinent and understanding the various adaptation techniques needs urgent attention. Further research with a multi-targeted approach to develop strategies for implementing interventions to protect the millions of workers is imperative. Approaches to include health aspects of climate change within sectoral and climate change specific policies should be encouraged, via a number of mechanisms, such as the “Health in All Policies” approach to avert adverse health and productivity consequences as climate change proceeds.

Keywords: heat stress, occupational health, productivity loss, heat strain, adverse health outcomes

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Authors: Rachel Buchanan, Rebecca LaRiviere

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Context: The ongoing effects of colonialism continue to be experienced through paternalistic policies and funding processes that cause disjuncture between and across Indigenous early childhood programming on-reserve and in urban and Northern settings in Canada. While various educational organizations and social service providers have risen to address these challenges in the short, medium and long term, there continues to be a lack in nation-wide cohesive, culturally grounded, and meaningful early learning programming for Indigenous children in Canada. Indigenous-centered early learning programs tend to face one of two scaling dilemmas: their program goals are too prescriptive to enable the program to be meaningfully replicated in different cultural/ community settings, or their program goals are too broad to be meaningfully adapted to the unique cultural and contextual needs and desires of Indigenous communities (the “franchise approach”). There are over 600 First Nations communities in Canada representing more than 50 Nations and languages. Consequently, Indigenous early learning programming cannot be applied with a universal or “one size fits all” approach. Sustainable and comprehensive programming must be responsive to each community context, building upon existing strengths and assets to avoid program duplication and irrelevance. Thesis: Community-driven and culturally adapted early childhood programming is critical but cannot be achieved on a large scale within traditional program models that are constrained by prescriptive overarching program goals. Principles, rather than goals, are an effective way to navigate and evaluate complex and dynamic systems. Principles guide an intervention to be adaptable, flexible and scalable. The Martin Family Initiative (MFI) ’s Early Years program engages a principles-based approach to programming. As will be discussed in this paper, this approach enables the program to catalyze existing community-based strengths and organizational assets toward bridging gaps across and disjuncture between Indigenous early learning programs, as well as to scale programming in sustainable, context-responsive and dynamic ways. This paper argues that using a principles-driven and adaptive scaling approach, the Early Years model establishes important learnings for culturally adapted Indigenous early learning programming in Canada. Methodology: The Early Years has leveraged this approach to develop an array of programming with partner organizations and communities across the country. The Early Years began as a singular pilot project in one First Nation. In just three years, it has expanded to five different regions and community organizations. In each context, the program supports the partner organization through different means and to different ends, the extent to which is determined in partnership with each community-based organization: in some cases, this means supporting the organization to build home visiting programming from the ground-up; in others, it means offering organization-specific culturally adapted early learning resources to support the programming that already exists in communities. Principles underpin but do not define the practices of the program in each of these relationships. This paper will explore numerous examples of principles-based adaptability with the context of the Early Years, concluding that the program model offers theadaptability and dynamism necessary to respond to unique and ever-evolving community contexts and needs of Indigenous children today.

Keywords: culturally adapted programming, indigenous early learning, principles-based approach, program scaling

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Authors: Almaz R. Gazizov, Sergey S. Kharintsev, Myakzyum Kh. Salakhov

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This work deals with background signal suppression in tip-enhanced near-field optical microscopy (TENOM). The background appears because an optical signal is detected not only from the subwavelength area beneath the tip but also from a wider diffraction-limited area of laser’s waist that might contain another substance. The background can be reduced by using a taper probe with a grating on its lateral surface where an external illumination causes surface plasmon excitation. It requires the grating with parameters perfectly matched with a given incident light for effective light coupling. This work is devoted to an analysis of the light-grating coupling and a quest of grating parameters to enhance a near-field light beneath the tip apex. The aim of this work is to find the figure of merit of plasmon excitation depending on grating period and location of grating in respect to the apex. In our consideration the metallic grating on the lateral surface of the tapered plasmonic probe is illuminated by a plane wave, the electric field is perpendicular to the sample surface. Theoretical model of efficiency of plasmon excitation and propagation toward the apex is tested by fdtd-based numerical simulation. An electric field of the incident light is enhanced on the grating by every single slit due to lightning rod effect. Hence, grating causes amplitude and phase modulation of the incident field in various ways depending on geometry and material of grating. The phase-modulating grating on the probe is a sort of metasurface that provides manipulation by spatial frequencies of the incident field. The spatial frequency-dependent electric field is found from the angular spectrum decomposition. If one of the components satisfies the phase-matching condition then one can readily calculate the figure of merit of plasmon excitation, defined as a ratio of the intensities of the surface mode and the incident light. During propagation towards the apex, surface wave undergoes losses in probe material, radiation losses, and mode compression. There is an optimal location of the grating in respect to the apex. One finds the value by matching quadratic law of mode compression and the exponential law of light extinction. Finally, performed theoretical analysis and numerical simulations of plasmon excitation demonstrate that various surface waves can be effectively excited by using the overtones of a period of the grating or by phase modulation of the incident field. The gratings with such periods are easy to fabricate. Tapered probe with the grating effectively enhances and localizes the incident field at the sample.

Keywords: angular spectrum decomposition, efficiency, grating, surface plasmon, taper nanoantenna

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Authors: Mahshid Arabi

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In recent years, remarkable advancements in the fields of artificial intelligence and machine learning have led to the development of facial recognition technologies. These technologies are now employed in a wide range of applications, including security, surveillance, healthcare, and education. In the field of education, the identification of university entrance exam candidates has been one of the fundamental challenges. Traditional methods such as using ID cards and handwritten signatures are not only inefficient and prone to fraud but also susceptible to errors. In this context, utilizing advanced technologies like facial recognition can be an effective and efficient solution to increase the accuracy and reliability of identity verification in entrance exams. This article examines the use of FaceMatch software for recognizing the faces of university entrance exam candidates in Iran. The main objective of this research is to evaluate the efficiency and accuracy of FaceMatch software in identifying university entrance exam candidates to prevent fraud and ensure the authenticity of individuals' identities. Additionally, this research investigates the advantages and challenges of using this technology in Iran's educational systems. This research was conducted using an experimental method and random sampling. In this study, 1000 university entrance exam candidates in Iran were selected as samples. The facial images of these candidates were processed and analyzed using FaceMatch software. The software's accuracy and efficiency were evaluated using various metrics, including accuracy rate, error rate, and processing time. The research results indicated that FaceMatch software could accurately identify candidates with a precision of 98.5%. The software's error rate was less than 1.5%, demonstrating its high efficiency in facial recognition. Additionally, the average processing time for each candidate's image was less than 2 seconds, indicating the software's high efficiency. Statistical evaluation of the results using precise statistical tests, including analysis of variance (ANOVA) and t-test, showed that the observed differences were significant, and the software's accuracy in identity verification is high. The findings of this research suggest that FaceMatch software can be effectively used as a tool for identifying university entrance exam candidates in Iran. This technology not only enhances security and prevents fraud but also simplifies and streamlines the exam administration process. However, challenges such as preserving candidates' privacy and the costs of implementation must also be considered. The use of facial recognition technology with FaceMatch software in Iran's educational systems can be an effective solution for preventing fraud and ensuring the authenticity of university entrance exam candidates' identities. Given the promising results of this research, it is recommended that this technology be more widely implemented and utilized in the country's educational systems.

Keywords: facial recognition, FaceMatch software, Iran, university entrance exam

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Authors: Saddam Hussain, Ghadeer Mohsen Albadrani

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Pakistan is one of the most vulnerable countries to climate change. Being a country where 23% of the country’s GDP relies on agriculture, this is a serious cause of concern. Introducing industrial hemp in Pakistan can help build climate resilience in the agricultural sector of the country, as hemp has recently emerged as a sustainable, eco-friendly, resource-efficient, and climate-resilient crop globally. Hemp has the potential to absorb huge amounts of CO₂, nourish the soil, and be used to create various biodegradable and eco-friendly products. Hemp is twice as effective as trees at absorbing and locking up carbon, with 1 hectare (2.5 acres) of hemp reckoned to absorb 8 to 22 tonnes of CO₂ a year, more than any woodland. Along with its high carbon-sequestration ability, it produces higher biomass and can be successfully grown as a cover crop. Hemp can grow in almost all soil conditions and does not require pesticides. It has fast-growing qualities and needs only 120 days to be ready for harvest. Compared with cotton, hemp requires 50% less water to grow and can produce three times higher fiber yield with a lower ecological footprint. Recently, the Government of Pakistan has allowed the cultivation of industrial hemp for industrial and medicinal purposes, making it possible for hemp to be reinserted into the country’s economy. Pakistan’s agro-climatic and edaphic conditions are well-suitable to produce industrial hemp, and its cultivation can bring economic benefits to the country. Pakistan can enter global markets as a new exporter of hemp products. The production of hemp in Pakistan can be most exciting to the workforce, especially for farmers participating in hemp markets. The minimum production cost of hemp makes it affordable to small holding farmers, especially those who need their cropping system to be as highly sustainable as possible. Dr. Saddam Hussain is leading the first pilot project of Industrial Hemp in Pakistan. In the past three years, he has been able to recruit high-impact research grants on industrial hemp as Principal Investigator. He has already screened the non-toxic hemp genotypes, tested the adaptability of exotic material in various agroecological conditions, formulated the production agronomy, and successfully developed the complete value chain. He has developed prototypes (fabric, denim, knitwear) using hemp fibre in collaboration with industrial partners and has optimized the indigenous fibre processing techniques. In this lecture, Dr. Hussain will talk on hemp agronomy and its complete fibre value chain. He will discuss the current progress, and will highlight the major challenges and future research direction on hemp research.

Keywords: industrial hemp, agricultural sustainability, agronomic evaluation, hemp value chain

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Authors: Kailash Chandra, Devanshu Gupta

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The Himalayan Mountains of India fall under two biogeographic zones Trans Himalaya (TH) and Himalaya and seven biotic provinces (TH-Ladakh Mountains, TH-Tibetan Plateau, TH-Sikkim, North-West Himalaya, West Himalaya, Central Himalaya, and East Himalaya). Because of the extreme environment and altitudinal variations, unique physiography, varied ecological conditions, and different vegetations, the Himalaya exhibit a rich assemblage of life, both flora, and fauna, further subjected to the impacts of climate change. To the authors’ best knowledge, there is no comprehensive account except for sporadic faunal investigations, to assess or interpret the insect diversity and their biogeographic distribution in Indian Himalaya (IH), one of the biodiversity hotspots. Therefore, in this paper, a compelling review of the extensive knowledge of insect diversity of IH is presented for the first time to the best of our knowledge. The inventory of the known insect species of IH was compiled from the exploration cum faunal-study data ready with the zoological survey of India, Kolkata as well as from the information published in the scientific literature till date. The species were listed with their valid names with their distribution in seven biotic provinces of IH. The insect fauna of IH represents about 38% of the identified insect diversity of India. The interpretation of data provided significant information in detecting possible gap areas in the taxonomic representation of different insect orders. Archaeognatha, Zygentoma, Ephemeroptera, Phasmida, Embioptera, Psocoptera, Phthiraptera, Strepsiptera, Megaloptera, Raphidioptera, Siphonaptera, and Mecoptera need revisions, and it is required to collect more samples from remote areas of the region. Scope for finding new taxa even in the most diverse orders, Coleoptera, Lepidoptera, Hymenoptera, Diptera, and Hemiptera cannot be overlooked. Exploration of cold deserts of Trans Himalaya and East Himalaya (Arunachal Pradesh) may result in a good number of new species from these regions. The most notable data was that many of the species recorded from Himalaya are still known from their type localities only, so there is an urgency to revisit and resurvey those collection localities for the evaluation of the status of those species. It is also required to assess and monitor the impact of climate change on the diversity of insects inhabiting in the fragile Himalayan ecosystem. DNA barcoding especially pests and biological control agents to solve the problems of identification in species complexes is also the need of the hour. In a nutshell, it can be concluded that the inventory of insects of this region is extensive but is far from final as every year hundreds of new species are described.

Keywords: catalog, climate change, diversity, DNA barcoding

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Authors: Perizat Aitmaganbet, Kerbez Kimatova, Gulmira Umarova

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As a result of medical check-up conducted in the framework of this research study an evaluation of the health status of the population living in the oil-producing regions, namely Sarkul and Kenkiyak villages in Aktobe was examined. With the help of the Spearman correlation, the connection between the level of hazard chemical elements in the atmosphere and the health of population living in the regions of oil and gas industry was estimated. Background & Objective. The oil and gas resource-extraction industries play an important role in improving the economic conditions of the Republic of Kazakhstan, especially for the oil-producing administrative regions. However, environmental problems may adversely affect the health of people living in that area. Thus, the aim of the study is to evaluate the exposure to negative environmental factors of the adult population living in Sarkul and Kenkiyak villages, the oil and gas producing areas in the Aktobe region. Methods. After conducting medical check-up among the population of Sarkul and Kenkiyak villages. A single cross-sectional study was conducted. The population consisted of randomly sampled 372 adults (181 males and 191 females). Also, atmospheric air probes were taken to measure the level of hazardous chemical elements in the air. The nonparametric method of the Spearman correlation analysis was performed between the mean concentration of substances exceeding the Maximum Permissible Concentration and the classes of newly diagnosed diseases. Selection and analysis of air samples were carried out according to the developed research protocol; the qualitative-quantitative analysis was carried out on the Gas analyzer HANK-4 apparatus. Findings. The medical examination of the population identified the following diseases: the first two dominant were diseases of the circulatory and digestive systems, in the 3rd place - diseases of the genitourinary system, and the nervous system and diseases of the ear and mastoid process were on the fourth and fifth places. Moreover, significant pollution of atmospheric air by carbon monoxide (MPC-5,0 mg/m3), benzapyrene (MPC-1mg/m3), dust (MPC-0,5 mg/m3) and phenol (МРС-0,035mg/m3) were identified in places. Correlation dependencies between these pollutants of air and the diseases of the population were established, as a result of diseases of the circulatory system (r = 0,7), ear and mastoid process (r = 0,7), nervous system (r = 0,6) and digestive organs(r = 0,6 ); between the concentration of carbon monoxide and diseases of the circulatory system (r = 0.6), the digestive system(r = 0.6), the genitourinary system (r = 0.6) and the musculoskeletal system; between nitric oxide and diseases of the digestive system (r = 0,7) and the circulatory system (r = 0,6); between benzopyrene and diseases of the digestive system (r = 0,6), the genitourinary system (r = 0,6) and the nervous system (r = 0,4). Conclusion. The positive correlation was found between air pollution and the health of the population living in Sarkul and Kenkiyak villages. To enhance the reliability of the results we are going to continue this study further.

Keywords: atmospheric air, chemical substances, oil and gas, public health

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Authors: Brogan Guest, Amy Donaldson-Perrott

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The Master’s of Physician Associate Studies (MPAS) programme at St George’s, University of London (SGUL), is an intensive two-year course that trains students to become physician associates (PAs). PAs are generalized healthcare providers who work in primary and secondary care across the UK. PA programmes face the difficult task of preparing students to become safe medical providers in two short years. Our goal is to teach students to develop clinical reasoning early on in their studies and historically, this has been done predominantly though problem-based learning (PBL). We have had an increase concern about student engagement in PBL and difficulty recruiting facilitators to maintain the low student to facilitator ratio required in PBL. To address this issue, we created ‘Clinical Application of Anatomy and Physiology (CAAP)’. These peer-led, interactive, problem-based, small group sessions were designed to facilitate students’ clinical reasoning skills. The sessions were designed using the concept of Team-Based Learning (TBL). Students were divided into small groups and each completed a pre-session quiz consisting of difficult questions devised to assess students’ application of medical knowledge. The quiz was completed in small groups and they were not permitted access of external resources. After the quiz, students worked through a series of openended, clinical tasks using all available resources. They worked at their own pace and the session was peer-led, rather than facilitator-driven. For a group of 35 students, there were two facilitators who observed the sessions. The sessions utilised an infinite space whiteboard software. Each group member was encouraged to actively participate and work together to complete the 15-20 tasks. The session ran for 2 hours and concluded with a post-session quiz, identical to the pre-session quiz. We obtained subjective feedback from students on their experience with CAAP and evaluated the objective benefit of the sessions through the quiz results. Qualitative feedback from students was generally positive with students feeling the sessions increased engagement, clinical understanding, and confidence. They found the small group aspect beneficial and the technology easy to use and intuitive. They also liked the benefit of building a resource for their future revision, something unique to CAAP compared to PBL, which out students participate in weekly. Preliminary quiz results showed improvement from pre- and post- session; however, further statistical analysis will occur once all sessions are complete (final session to run December 2022) to determine significance. As a post-graduate healthcare professional programme, we have a strong focus on self-directed learning. Whilst PBL has been a mainstay in our curriculum since its inception, there are limitations and concerns about its future in view of student engagement and facilitator availability. Whilst CAAP is not TBL, it draws on the benefits of peer-led, small group work with pre- and post- team-based quizzes. The pilot of these sessions has shown that students are engaged by CAAP, and they can make significant progress in clinical reasoning in a short amount of time. This can be achieved with a high student to facilitator ratio.

Keywords: problem based learning, team based learning, active learning, peer-to-peer teaching, engagement

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Authors: Daniela Velichová

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Plenty of ideas on how to adopt active learning methods in education are available nowadays. Mathematics is a subject where the active involvement of students is required in particular in order to achieve desirable results regarding sustainable knowledge and deep understanding. The present article is based on the outcomes of an Erasmus+ project DrIVE-MATH, that was aimed at developing a novel and integrated framework to teach maths classes in engineering courses at the university level. It is fundamental for students from the early years of their academic life to have agile minds. They must be prepared to adapt to their future working environments, where enterprises’ views are always evolving, where all collaborate in teams, and relations between peers are thought for the well-being of the whole - workers and company profit. This reality imposes new requirements on higher education in terms of adaptation of different pedagogical methods, such as project-based and active-learning methods used within the course curricula. Active learning methodologies are regarded as an effective way to prepare students to meet the challenges posed by enterprises and to help them in building critical thinking, analytic reasoning, and insight to the solved complex problems from different perspectives. Fostering learning-by-doing activities in the pedagogical process can help students to achieve learning independence, as they could acquire deeper conceptual understanding by experimenting with the abstract concept in a more interesting, useful, and meaningful way. Clear information about learning outcomes and goals might help students to take more responsibility for their learning results. Active learning methods implemented by the project team members in their teaching practice, eduScrum and Jigsaw in particular, proved to provide better scientific and soft skills support to students than classical teaching methods. EduScrum method enables teachers to generate a working environment that stimulates students' working habits and self-initiative as they become aware of their responsibilities within the team, their own acquired knowledge, and their abilities to solve problems independently, though in collaboration with other team members. This method enhances collaborative learning, as students are working in teams towards a common goal - knowledge acquisition, while they are interacting with each other and evaluated individually. Teams consisting of 4-5 students work together on a list of problems - sprint; each member is responsible for solving one of them, while the group leader – a master, is responsible for the whole team. A similar principle is behind the Jigsaw technique, where the classroom activity makes students dependent on each other to succeed. Students are divided into groups, and assignments are split into pieces, which need to be assembled by the whole group to complete the (Jigsaw) puzzle. In this paper, analysis of students’ perceptions concerning the achievement of deeper conceptual understanding in mathematics and the development of soft skills, such as self-motivation, critical thinking, flexibility, leadership, responsibility, teamwork, negotiation, and conflict management, is presented. Some new challenges are discussed as brought by introducing active learning methods in the basic mathematics courses. A few examples of sprints developed and used in teaching basic maths courses at technical universities are presented in addition.

Keywords: active learning methods, collaborative learning, conceptual understanding, eduScrum, Jigsaw, soft skills

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Authors: Jagmeet S. Kanwal, Julia F. Langley

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Music can heal the body, but a mechanistic understanding of this phenomenon is lacking. This study explores the effects of music presentation on neurologic and physiologic responses leading to metabolic changes in the human body. The mind and body co-exist in a corporeal entity and within this framework, sickness ensues when the mind-body balance goes awry. It is further hypothesized that music has the capacity to directly reset this balance. Two lines of inquiry taken together can provide a mechanistic understanding of this phenomenon 1) Empirical evidence for a sound-sensitive pressure sensor system in the body, and 2) The notion of a “healing center” within the brain that is activated by specific patterns of sounds. From an acoustics perspective, music is spatially distributed as pressure waves ranging from a few cm to several meters in wavelength. These waves interact and propagate in three-dimensions in unique ways, depending on the wavelength. Furthermore, music creates dynamically changing wave-fronts. Frequencies between 200 Hz and 1 kHz generate wavelengths that range from 5'6" to 1 foot. These dimensions are in the range of the body size of most people making it plausible that these pressure waves can geometrically interact with the body surface and create distinct patterns of pressure stimulation across the skin surface. For humans, short wavelength, high frequency (> 200 Hz) sounds are best received via cochlear receptors. For low frequency (< 200 Hz), long wavelength sound vibrations, however, the whole body may act as an ideal receiver. A vast array of highly sensitive pressure receptors (Pacinian corpuscles) is present just beneath the skin surface, as well as in the tendons, bones, several organs in the abdomen, and the sexual organs. Per the available empirical evidence, these receptors contribute to music perception by allowing the whole body to function as a sound receiver, and knowledge of how they function is essential to fully understanding the therapeutic effect of music. Neuroscientific studies have established that music stimulates the limbic system that can trigger states of anxiety, arousal, fear, and other emotions. These emotional states of brain activity play a crucial role in filtering top-down feedback from thoughts and bottom-up sensory inputs to the autonomic system, which automatically regulates bodily functions. Music likely exerts its pleasurable and healing effects by enhancing functional and effective connectivity and feedback mechanisms between brain regions that mediate reward, autonomic, and cognitive processing. Stimulation of pressure receptors under the skin by low-frequency music-induced sensations can activate multiple centers in the brain, including the amygdala, the cingulate cortex, and nucleus accumbens. Melodies in music in the low (< 600 Hz) frequency range may augment auditory inputs after convergence of the pressure-sensitive inputs from the vagus nerve onto emotive processing regions within the limbic system. The integration of music-generated auditory and somato-visceral inputs may lead to a synergistic input to the brain that promotes healing. Thus, music can literally heal humans through “touch” as it energizes the brain’s autonomic system for restoring homeostasis.

Keywords: acoustics, brain, music healing, pressure receptors

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Authors: Anna-Akrivi Thomatou, Anastasios Zotos, Eleni C. Mazarakioti, Efthimios Kokkotos, Achilleas Kontogeorgos, Athanasios Ladavos, Angelos Patakas

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It is well known that accurate determination of geographic origin to confront mislabeling and adulteration of foods is considered as a critical issue worldwide not only for the consumers, but also for producers and industries. Among agricultural products, rice (Oryza sativa L.) is the world’s third largest crop, providing food for more than half of the world’s population. Consequently, the quality and safety of rice products play an important role in people’s life and health. Despite the fact that rice is predominantly produced in Asian countries, rice cultivation in Greece is of significant importance, contributing to national agricultural sector income. More than 25,000 acres are cultivated in Greece, while rice exports to other countries consist the 0,5% of the global rice trade. Although several techniques are available in order to provide information about the geographical origin of rice, little data exist regarding the ability of these methodologies to discriminate rice production from Greece. Thus, the aim of this study is the comparative evaluation of stable isotope ratio methodology regarding its discriminative ability for geographical origin determination of rice samples produced in Greece compared to those from three other Asian countries namely Korea, China and Philippines. In total eighty (80) samples were collected from selected fields of Central Macedonia (Greece), during October of 2021. The light element (C, N, S) isotope ratios were measured using Isotope Ratio Mass Spectrometry (IRMS) and the results obtained were analyzed using chemometric techniques, including principal components analysis (PCA). Results indicated that the 𝜹 15N and 𝜹 34S values of rice produced in Greece were more markedly influenced by geographical origin compared to the 𝜹 13C. In particular, 𝜹 34S values in rice originating from Greece was -1.98 ± 1.71 compared to 2.10 ± 1.87, 4.41 ± 0.88 and 9.02 ± 0.75 for Korea, China and Philippines respectively. Among stable isotope ratios studied, values of 𝜹 34S seem to be the more appropriate isotope marker to discriminate rice geographic origin between the studied areas. These results imply the significant capability of stable isotope ratio methodology for effective geographical origin discrimination of rice, providing a valuable insight into the control of improper or fraudulent labeling. Acknowledgement: This research has been financed by the Public Investment Programme/General Secretariat for Research and Innovation, under the call “YPOERGO 3, code 2018SE01300000: project title: ‘Elaboration and implementation of methodology for authenticity and geographical origin assessment of agricultural products.

Keywords: geographical origin, authenticity, rice, isotope ratio mass spectrometry

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Authors: Michael Kenomore, Mohamed Hassan, Amjad Shah, Hom Dhakal

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Shale gas reservoirs have been of greater importance compared to shale oil reservoirs since 2009 and with the current nature of the oil market, understanding the technical and economic performance of shale gas reservoirs is of importance. Using the Barnett shale as a case study, an economic model was developed to quantify the effect of finding and development costs and gas prices on the basins in the Barnett shale using net present value as an evaluation parameter. A rate of return of 20% and a payback period of 60 months or less was used as the investment hurdle in the model. The Barnett was split into four basins (Strawn Basin, Ouachita Folded Belt, Forth-worth Syncline and Bend-arch Basin) with analysis conducted on each of the basin to provide a holistic outlook. The dataset consisted of only horizontal wells that started production from 2008 to at most 2015 with 1835 wells coming from the strawn basin, 137 wells from the Ouachita folded belt, 55 wells from the bend-arch basin and 724 wells from the forth-worth syncline. The data was analyzed initially on Microsoft Excel to determine the estimated ultimate recoverable (EUR). The range of EUR from each basin were loaded in the Palisade Risk software and a log normal distribution typical of Barnett shale wells was fitted to the dataset. Monte Carlo simulation was then carried out over a 1000 iterations to obtain a cumulative distribution plot showing the probabilistic distribution of EUR for each basin. From the cumulative distribution plot, the P10, P50 and P90 EUR values for each basin were used in the economic model. Gas production from an individual well with a EUR similar to the calculated EUR was chosen and rescaled to fit the calculated EUR values for each basin at the respective percentiles i.e. P10, P50 and P90. The rescaled production was entered into the economic model to determine the effect of the finding and development cost and gas price on the net present value (10% discount rate/year) as well as also determine the scenario that satisfied the proposed investment hurdle. The finding and development costs used in this paper (assumed to consist only of the drilling and completion costs) were £1 million, £2 million and £4 million while the gas price was varied from $2/MCF-$13/MCF based on Henry Hub spot prices from 2008-2015. One of the major findings in this study was that wells in the bend-arch basin were least economic, higher gas prices are needed in basins containing non-core counties and 90% of the Barnet shale wells were not economic at all finding and development costs irrespective of the gas price in all the basins. This study helps to determine the percentage of wells that are economic at different range of costs and gas prices, determine the basins that are most economic and the wells that satisfy the investment hurdle.

Keywords: shale gas, Barnett shale, unconventional gas, estimated ultimate recoverable

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: Sakinah Idris, Femke Ten Hoeve, Kirstin Greaves-Lord

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Background: Social skills interventions are considered to be efficacious if social skills are improved as a result of an intervention. Nevertheless, the objective assessment of social skills is hindered by a lack of sensitive and validated measures. To measure the change in social skills after an intervention, questionnaires reported by parents, clinicians and/or teachers are commonly used. Observations are the most ecologically valid method of assessing improvements in social skills after an intervention. For this purpose, The Program for the Educational and Enrichment of Relational Skills (PEERS) was developed for adolescents, in order to teach them the age-appropriate skills needed to participate in society. It is an evidence-based intervention for adolescents with ASD that taught ecologically valid social skills techniques. Objectives: The current study aims to describe the development and psychometric evaluation of the Dutch Contextual Assessment of Social Skills (CASS), an observational outcome measure of social skills for adolescents with Autism Spectrum Disorder (ASD). Methods: 64 adolescents (M = 14.68, SD = 1.41, 71% boys) with ASD performed the CASS before and after a social skills intervention (i.e. PEERS or the active control condition). Each adolescent completed a 3-minute conversation with a confederate. The conversation was prompt as a natural introduction between two-unfamiliar, similar ages, opposite-sex peers who meet for the first time. The adolescent and the confederate completed a brief questionnaire about the conversation (Conversation Rating Scale). Results: Results indicated sufficient psychometric properties. The Dutch CASS has a high level of internal consistency (Cronbach's α coefficients = 0.84). Data supported the convergent validity (i.e., significant correlated with the Social Skills Improvement System (SSiS). The Dutch CASS did not significantly correlate with the autistic mannerism subscale from Social Responsiveness Scale (SRS), thus proved the divergent validity. Based on scorings made by raters who were kept blind to the time points, reliable change index was computed to assess the change in social skills. With regard to the content validity, only the learning objectives of the first two meetings of PEERS about conversational skills relatively matched with rating domains of the CASS. Due to this underrepresentation, we found an existing observational measure (TOPICC) that covers some of the other learning objectives of PEERS. TOPICC covers 22% of the learning objectives of PEERS about conversational skills, meanwhile, CASS is 45%. Unfortunately, 33% of the learning objectives of PEERS was not covered by CASS or TOPICC. Conclusion: Recommendations are made to improve the psychometric properties and content validity of the Dutch CASS.

Keywords: autism spectrum disorder, observational, PEERS, social skills

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Authors: M. Bahari Mehrabani, Hua-Peng Chen

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Management and maintenance of coastal defence structures during the expected life cycle have become a real challenge for decision makers and engineers. Accurate evaluation of the current condition and future performance of flood defence structures is essential for effective practical maintenance strategies on the basis of available field inspection data. Moreover, as coastal defence structures age, it becomes more challenging to implement maintenance and management plans to avoid structural failure. Therefore, condition inspection data are essential for assessing damage and forecasting deterioration of ageing flood defence structures in order to keep the structures in an acceptable condition. The inspection data for flood defence structures are often collected using discrete visual condition rating schemes. In order to evaluate future condition of the structure, a probabilistic deterioration model needs to be utilised. However, existing deterioration models may not provide a reliable prediction of performance deterioration for a long period due to uncertainties. To tackle the limitation, a time-dependent condition-based model associated with a transition probability needs to be developed on the basis of condition grade scheme for flood defences. This paper presents a probabilistic method for predicting future performance deterioration of coastal flood defence structures based on condition grading inspection data and deterioration curves estimated by expert judgement. In condition-based deterioration modelling, the main task is to estimate transition probability matrices. The deterioration process of the structure related to the transition states is modelled according to Markov chain process, and a reliability-based approach is used to estimate the probability of structural failure. Visual inspection data according to the United Kingdom Condition Assessment Manual are used to obtain the initial condition grade curve of the coastal flood defences. The initial curves then modified in order to develop transition probabilities through non-linear regression based optimisation algorithms. The Monte Carlo simulations are then used to evaluate the future performance of the structure on the basis of the estimated transition probabilities. Finally, a case study is given to demonstrate the applicability of the proposed method under no-maintenance and medium-maintenance scenarios. Results show that the proposed method can provide an effective predictive model for various situations in terms of available condition grading data. The proposed model also provides useful information on time-dependent probability of failure in coastal flood defences.

Keywords: condition grading, flood defense, performance assessment, stochastic deterioration modelling

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Authors: Joel Niklaus, Matthias Sturmer

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The publication of judicial proceedings is a cornerstone of many democracies. It enables the court system to be made accountable by ensuring that justice is made in accordance with the laws. Equally important is privacy, as a fundamental human right (Article 12 in the Declaration of Human Rights). Therefore, it is important that the parties (especially minors, victims, or witnesses) involved in these court decisions be anonymized securely. Today, the anonymization of court decisions in Switzerland is performed either manually or semi-automatically using primitive software. While much research has been conducted on anonymization for tabular data, the literature on anonymization for unstructured text documents is thin and virtually non-existent for court decisions. In 2019, it has been shown that manual anonymization is not secure enough. In 21 of 25 attempted Swiss federal court decisions related to pharmaceutical companies, pharmaceuticals, and legal parties involved could be manually re-identified. This was achieved by linking the decisions with external databases using regular expressions. An automated re-identification system serves as an automated test for the safety of existing anonymizations and thus promotes the right to privacy. Manual anonymization is very expensive (recurring annual costs of over CHF 20M in Switzerland alone, according to an estimation). Consequently, many Swiss courts only publish a fraction of their decisions. An automated anonymization system reduces these costs substantially, further leading to more capacity for publishing court decisions much more comprehensively. For the re-identification system, topic modeling with latent dirichlet allocation is used to cluster an amount of over 500K Swiss court decisions into meaningful related categories. A comprehensive knowledge base with publicly available data (such as social media, newspapers, government documents, geographical information systems, business registers, online address books, obituary portal, web archive, etc.) is constructed to serve as an information hub for re-identifications. For the actual re-identification, a general-purpose language model is fine-tuned on the respective part of the knowledge base for each category of court decisions separately. The input to the model is the court decision to be re-identified, and the output is a probability distribution over named entities constituting possible re-identifications. For the anonymization system, named entity recognition (NER) is used to recognize the tokens that need to be anonymized. Since the focus lies on Swiss court decisions in German, a corpus for Swiss legal texts will be built for training the NER model. The recognized named entities are replaced by the category determined by the NER model and an identifier to preserve context. This work is part of an ongoing research project conducted by an interdisciplinary research consortium. Both a legal analysis and the implementation of the proposed system design ESRA will be performed within the next three years. This study introduces the system design of ESRA, an end-to-end system for re-identification and anonymization of Swiss court decisions. Firstly, the re-identification system tests the safety of existing anonymizations and thus promotes privacy. Secondly, the anonymization system substantially reduces the costs of manual anonymization of court decisions and thus introduces a more comprehensive publication practice.

Keywords: artificial intelligence, courts, legal tech, named entity recognition, natural language processing, ·privacy, topic modeling

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Authors: Sharon K. Mahlangu, Mustapha Mohammed, Felix D. Dakora

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Rural households in Africa depend largely on legumes as a source of high-protein food due to N₂-fixation by rhizobia when they infect plant roots. However, the legume/rhizobia symbiosis can exhibit some level of specificity such that some legumes may be selectively nodulated by only a particular group of rhizobia. In contrast, some legumes are highly promiscuous and are nodulated by a wide range of rhizobia. Little is known about the nodulation promiscuity of bacterial symbionts from wild legumes such as Aspalathus linearis, especially if they can nodulate cultivated grain legumes such as cowpea and Kersting’s groundnut. Determining the host range of the symbionts of wild legumes can potentially reveal novel rhizobial strains that can be used to increase nitrogen fixation in cultivated legumes. In this study, bacteria were isolated and tested for their ability to induce root nodules on their homologous hosts. Seeds were surface-sterilized with alcohol and sodium hypochlorite and planted in sterile sand contained in plastic pots. The pot surface was covered with sterile non-absorbent cotton wool to avoid contamination. The plants were watered with nitrogen-free nutrient solution and sterile water in alternation. Three replicate pots were used per isolate. The plants were grown for 90 days in a naturally-lit glasshouse and assessed for nodulation (nodule number and nodule biomass) and shoot biomass. Seven isolates from each of Kersting’s groundnut and cowpea and two from Rooibos tea plants were tested for their ability to nodulate soybean, mung bean, common bean and Bambara groundnut. The results showed that of the isolates from cowpea, where VUSA55 and VUSA42 could nodulate all test host plants, followed by VUSA48 which nodulated cowpea, Bambara groundnut and soybean. The two isolates from Rooibos tea plants nodulated Bambara groundnut, soybean and common bean. However, isolate L1R3.3.1 also nodulated mung bean. There was a greater accumulation of shoot biomass when cowpea isolate VUSA55 nodulated common bean. Isolate VUSA55 produced the highest shoot biomass, followed by VUSA42 and VUSA48. The two Kersting’s groundnut isolates, MGSA131 and MGSA110, accumulated average shoot biomass. In contrast, the two Rooibos tea isolates induced a higher accumulation of biomass in Bambara groundnut, followed by common bean. The results suggest that inoculating these agriculturally important grain legumes with cowpea isolates can contribute to improved soil fertility, especially soil nitrogen levels.

Keywords: legumes, nitrogen fixation, nodulation, rhizobia

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Authors: Weikang Gong, Chunhua Li

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Dynamics plays an essential role in function exertion of proteins. Elastic network model (ENM), a harmonic potential-based and cost-effective computational method, is a valuable and efficient tool for characterizing the intrinsic dynamical properties encoded in biomacromolecule structures and has been widely used to detect the large-amplitude collective motions of proteins. Gaussian network model (GNM) and anisotropic network model (ANM) are the two often-used ENM models. In recent years, many ENM variants have been proposed. Here, we propose a small but effective modification (denoted as modified mENM) to the multiscale ENM (mENM) where fitting weights of Kirchhoff/Hessian matrixes with the least square method (LSM) is modified since it neglects the details of pairwise interactions. Then we perform its comparisons with the original mENM, traditional ENM, and parameter-free ENM (pfENM) on reproducing dynamical properties for the six representative proteins whose molecular dynamics (MD) trajectories are available in http://mmb.pcb.ub.es/MoDEL/. In the results, for B-factor prediction, mENM achieves the best performance among the four ENM models. Additionally, it is noted that with the weights of the multiscale Kirchhoff/Hessian matrixes modified, interestingly, the modified mGNM/mANM still has a much better performance than the corresponding traditional ENM and pfENM models. As to dynamical cross-correlation map (DCCM) calculation, taking the data obtained from MD trajectories as the standard, mENM performs the worst while the results produced by the modified mENM and pfENM models are close to those from MD trajectories with the latter a little better than the former. Generally, ANMs perform better than the corresponding GNMs except for the mENM. Thus, pfANM and the modified mANM, especially the former, have an excellent performance in dynamical cross-correlation calculation. Compared with GNMs (except for mGNM), the corresponding ANMs can capture quite a number of positive correlations for the residue pairs nearly largest distances apart, which is maybe due to the anisotropy consideration in ANMs. Furtherly, encouragingly the modified mANM displays the best performance in capturing the functional motional modes, followed by pfANM and traditional ANM models, while mANM fails in all the cases. This suggests that the consideration of long-range interactions is critical for ANM models to produce protein functional motions. Based on the analyses, the modified mENM is a promising method in capturing multiple dynamical characteristics encoded in protein structures. This work is helpful for strengthening the understanding of the elastic network model and provides a valuable guide for researchers to utilize the model to explore protein dynamics.

Keywords: elastic network model, ENM, multiscale ENM, molecular dynamics, parameter-free ENM, protein structure

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Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

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Authors: Hana Al-Baghaoi, Kumar Shiva Gubbiyappa, Mayuren Candasamy, Kiruthiga Perumal Vijayaraman

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Acne is a chronic inflammatory disease of the sebaceous glands characterized by areas of skin with seborrhea, comedones, papules, pustules, nodules, and possibly scarring. Propionibacterium acnes (P. acnes), plays a key role in the pathogenesis of acne. Their colonization and proliferation trigger the host’s inflammatory response leading to the production of pro-inflammatory cytokines such as interleukin-8 (IL-8) and tumour necrosis factor-α (TNF-α). The usage of honey and natural compounds to treat skin ailments has strong support in the current trend of drug discovery. The present study was carried out evaluate antimicrobial and anti-inflammatory potential of Doani Sidr honey and its fractions against P. acnes and to screen madecassoside alone and in combination with fractions of honey. The broth dilution method was used to assess the antibacterial activity. Also, ultra structural changes in cell morphology were studied before and after exposure to Sidr honey using transmission electron microscopy (TEM). The three non-toxic concentrations of the samples were investigated for suppression of cytokines IL 8 and TNF α by testing the cell supernatants in the co-culture of the human peripheral blood mononuclear cells (hPBMCs) heat killed P. acnes using enzyme immunoassay kits (ELISA). Results obtained was evaluated by statistical analysis using Graph Pad Prism 5 software. The Doani Sidr honey and polysaccharide fractions were able to inhibit the growth of P. acnes with a noteworthy minimum inhibitory concentration (MIC) value of 18% (w/v) and 29% (w/v), respectively. The proximity of MIC and MBC values indicates that Doani Sidr honey had bactericidal effect against P. acnes which is confirmed by TEM analysis. TEM images of P. acnes after treatment with Doani Sidr honey showed completely physical membrane damage and lysis of cells; whereas non honey treated cells (control) did not show any damage. In addition, Doani Sidr honey and its fractions significantly inhibited (> 90%) of secretion of pro-inflammatory cytokines like TNF α and IL 8 by hPBMCs pretreated with heat-killed P. acnes. However, no significant inhibition was detected for madecassoside at its highest concentration tested. Our results suggested that Doani Sidr honey possesses both antimicrobial and anti-inflammatory effects against P. acnes and can possibly be used as therapeutic agents for acne. Furthermore, polysaccharide fraction derived from Doani Sidr honey showed potent inhibitory effect toward P. acnes. Hence, we hypothesize that fraction prepared from Sidr honey might be contributing to the antimicrobial and anti-inflammatory activity. Therefore, this polysaccharide fraction of Doani Sidr honey needs to be further explored and characterized for various phytochemicals which are contributing to antimicrobial and anti-inflammatory properties.

Keywords: Doani sidr honey, Propionibacterium acnes, IL-8, TNF alpha

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Authors: Grigoria Athanasaki, Veerarajan Vimala, A. M. Kannan, Louis Cindrella

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Energy usage has been increased throughout the years, leading to severe environmental impacts. Since the majority of the energy is currently produced from fossil fuels, there is a global need for clean energy solutions. Proton Exchange Membrane Fuel Cells (PEMFCs) offer a very promising solution for transportation applications because of their solid configuration and low temperature operations, which allows them to start quickly. One of the main components of PEMFCs is the Gas Diffusion Layer (GDL), which manages water and gas transport and shows direct influence on the fuel cell performance. In this work, a novel dual-layer GDL with gradient porosity was prepared, using polyethylene glycol (PEG) as pore former, to improve the gas diffusion and water management in the system. The microporous layer (MPL) of the fabricated GDL consists of carbon powder PUREBLACK, sodium dodecyl sulfate as a surfactant, 34% wt. PTFE and the gradient porosity was created by applying one layer using 30% wt. PEG on the carbon substrate, followed by a second layer without using any pore former. The total carbon loading of the microporous layer is ~ 3 mg.cm-2. For the assembly of the catalyst layer, Nafion membrane (Ion Power, Nafion Membrane NR211) and Pt/C electrocatalyst (46.1% wt.) were used. The catalyst ink was deposited on the membrane via microspraying technique. The Pt loading is ~ 0.4 mg.cm-2, and the active area is 5 cm2. The sample was ex-situ characterized via wetting angle measurement, Scanning Electron Microscopy (SEM), and Pore Size Distribution (PSD) to evaluate its characteristics. Furthermore, for the performance evaluation in-situ characterization via Fuel Cell Testing using H2/O2 and H2/air as reactants, under 50, 60, 80, and 100% relative humidity (RH), took place. The results were compared to a single layer GDL, fabricated with the same carbon powder and loading as the dual layer GDL, and a commercially available GDL with MPL (AvCarb2120). The findings reveal high hydrophobic properties of the microporous layer of the GDL for both PUREBLACK based samples, while the commercial GDL demonstrates hydrophilic behavior. The dual layer GDL shows high and stable fuel cell performance under all the RH conditions, whereas the single layer manifests a drop in performance at high RH in both oxygen and air, caused by catalyst flooding. The commercial GDL shows very low and unstable performance, possibly because of its hydrophilic character and thinner microporous layer. In conclusion, the dual layer GDL with PEG appears to have improved gas diffusion and water management in the fuel cell system. Due to its increasing porosity from the catalyst layer to the carbon substrate, it allows easier access of the reactant gases from the flow channels to the catalyst layer, and more efficient water removal from the catalyst layer, leading to higher performance and stability.

Keywords: gas diffusion layer, microporous layer, proton exchange membrane fuel cells, relative humidity

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Authors: Mishak Rahul, Naveen Kumar, Bharath Kumar

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Conceived as a high-end engine to revolutionise sustainable agri-food production, the intelligent crop circle (ICC) aims to incorporate the Internet of Things (IoT), blockchain technology and artificial intelligence (AI) to bolster resource efficiency and prevent waste, increase the volume of production and bring about sustainable solutions with long-term ecosystem conservation as the guiding principle. The operating principle of the ICC relies on bringing together multidisciplinary bottom-up collaborations between producers, researchers and consumers. Key elements of the framework include IoT-based smart sensors for sensing soil moisture, temperature, humidity, nutrient and air quality, which provide short-interval and timely data; blockchain technology for data storage on a private chain, which maintains data integrity, traceability and transparency; and AI-based predictive analysis, which actively predicts resource utilisation, plant growth and environment. This data and AI insights are built into the ICC platform, which uses the resulting DSS (Decision Support System) outlined as help in decision making, delivered through an easy-touse mobile app or web-based interface. Farmers are assumed to use such a decision-making aid behind the power of the logic informed by the data pool. Building on existing data available in the farm management systems, the ICC platform is easily interoperable with other IoT devices. ICC facilitates connections and information sharing in real-time between users, including farmers, researchers and industrial partners, enabling them to cooperate in farming innovation and knowledge exchange. Moreover, ICC supports sustainable practice in agriculture by integrating gamification techniques to stimulate farm adopters, deploying VR technologies to model and visualise 3D farm environments and farm conditions, framing the field scenarios using VR headsets and Real-Time 3D engines, and leveraging edge technologies to facilitate secure and fast communication and collaboration between users involved. And through allowing blockchain-based marketplaces, ICC offers traceability from farm to fork – that is: from producer to consumer. It empowers informed decision-making through tailor-made recommendations generated by means of AI-driven analysis and technology democratisation, enabling small-scale and resource-limited farmers to get their voice heard. It connects with traditional knowledge, brings together multi-stakeholder interactions as well as establishes a participatory ecosystem to incentivise continuous growth and development towards more sustainable agro-ecological food systems. This integrated approach leverages the power of emerging technologies to provide sustainable solutions for a resilient food system, ensuring sustainable agriculture worldwide.

Keywords: blockchain, internet of things, artificial intelligence, decision support system, virtual reality, gamification, traceability, sustainable agriculture

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Authors: Rhiannon Barker, Gregory Hartwell, Matt Egan, Karen Lock

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Background: Evidence suggests that mental health (MH) issues in children and young people (CYP) in the UK are on the rise. Of particular concern is data that indicates that the pandemic, together with the impact of school closures, have accentuated already pronounced inequalities; children from families on low incomes or from black and minority ethnic groups are reportedly more likely to have been adversely impacted. This study aimed to help identify specific support which may facilitate the building of a positive school climate and protect student mental health, particularly in the wake of school closures following the pandemic. It has important implications for integrated working between schools and statutory health services. Methods: The research comprised of three parts; scoping, case studies, and a stakeholder workshop to explore and consolidate results. The scoping phase included a literature review alongside interviews with a range of stakeholders from government, academia, and the third sector. Case studies were then conducted in two London state schools. Results: Our research identified how student MH was being impacted by a range of factors located at different system levels, both internal to the school and in the wider community. School climate, relating both to a shared system of beliefs and values, as well as broader factors including style of leadership, teaching, discipline, safety, and relationships -all played a role in the experience of school life and, consequently, the MH of both students and staff. Participants highlighted the importance of a whole school approach and ensuring that support for student MH was not separated from academic achievement, as well as the importance of identifying and applying universal measuring systems to establish levels of MH need. Our findings suggest that a school’s climate is influenced by the style and strength of its leadership, while this school climate - together with mechanisms put in place to respond to MH needs (both statutory and non-statutory) - plays a key role in supporting student MH. Implications: Schools in England have a responsibility to decide on the nature of MH support provided for their students, and there is no requirement for them to report centrally on the form this provision takes. The reality on the ground, as our study suggests, is that MH provision varies significantly between schools, particularly in relation to ‘lower’ levels of need which are not covered by statutory requirements. A valid concern may be that in the huge raft of possible options schools have to support CYP wellbeing, too much is left to chance. Work to support schools in rebuilding their cultures post-lockdowns must include the means to identify and promote appropriate tools and techniques to facilitate regular measurement of student MH. This will help establish both the scale of the problem and monitor the effectiveness of the response. A strong vision from a school’s leadership team that emphasises the importance of student wellbeing, running alongside (but not overshadowed by) academic attainment, should help shape a school climate to promote beneficial MH outcomes. The sector should also be provided with support to improve the consistency and efficacy of MH provision in schools across the country.

Keywords: mental health, schools, young people, whole-school culture

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Authors: Delphine Ribes Lemay, Valentine Bernasconi, André Andrade, Lara DéFayes, Mathieu Salzmann, FréDéRic Kaplan, Nicolas Henchoz

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Digitization and preservation of large heritage induce high maintenance costs to keep up with the technical standards and ensure sustainable access. Creating impactful usage is instrumental to justify the resources for long-term preservation. The Museum für Gestaltung of Zurich holds one of the biggest poster collections of the world from which 52’000 were digitised. In the process of building a digital installation to valorize the collection, one objective was to develop an algorithm capable of predicting the next poster to show according to the ones already displayed. The work presented here describes the steps to build an algorithm able to automatically create sequences of posters reflecting associations performed by curator and professional designers. The exposed challenge finds similarities with the domain of song playlist algorithms. Recently, artificial intelligence techniques and more specifically, deep-learning algorithms have been used to facilitate their generations. Promising results were found thanks to Recurrent Neural Networks (RNN) trained on manually generated playlist and paired with clusters of extracted features from songs. We used the same principles to create the proposed algorithm but applied to a challenging medium, posters. First, a convolutional autoencoder was trained to extract features of the posters. The 52’000 digital posters were used as a training set. Poster features were then clustered. Next, an RNN learned to predict the next cluster according to the previous ones. RNN training set was composed of poster sequences extracted from a collection of books from the Gestaltung Museum of Zurich dedicated to displaying posters. Finally, within the predicted cluster, the poster with the best proximity compared to the previous poster is selected. The mean square distance between features of posters was used to compute the proximity. To validate the predictive model, we compared sequences of 15 posters produced by our model to randomly and manually generated sequences. Manual sequences were created by a professional graphic designer. We asked 21 participants working as professional graphic designers to sort the sequences from the one with the strongest graphic line to the one with the weakest and to motivate their answer with a short description. The sequences produced by the designer were ranked first 60%, second 25% and third 15% of the time. The sequences produced by our predictive model were ranked first 25%, second 45% and third 30% of the time. The sequences produced randomly were ranked first 15%, second 29%, and third 55% of the time. Compared to designer sequences, and as reported by participants, model and random sequences lacked thematic continuity. According to the results, the proposed model is able to generate better poster sequencing compared to random sampling. Eventually, our algorithm is sometimes able to outperform a professional designer. As a next step, the proposed algorithm should include a possibility to create sequences according to a selected theme. To conclude, this work shows the potentiality of artificial intelligence techniques to learn from existing content and provide a tool to curate large sets of data, with a permanent renewal of the presented content.

Keywords: Artificial Intelligence, Digital Humanities, serendipity, design research

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Authors: Hui-Ling Yang, Kuei-Ru Chou

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Background: Studies show that cognitive training can effectively delay cognitive failure. However, there are several gaps in the previous studies of cognitive training in mild cognitive impairment: 1) previous studies enrolled mostly healthy older adults, with few recruiting older adults with cognitive impairment; 2) they also had limited generalizability and lacked long-term follow-up data and measurements of the activities of daily living functional impact. Moreover, only 37% were randomized controlled trials (RCT). 3) Limited cognitive training has been specifically developed for mild cognitive impairment. Objective: This study sought to investigate the changes in cognitive function, activities of daily living and degree of depressive symptoms in older adults with mild cognitive impairment after cognitive training. Methods: This double-blind randomized controlled study has a 2-arm parallel group design. Study subjects are older adults diagnosed with mild cognitive impairment in residential care facilities. 124 subjects will be randomized by the permuted block randomization, into intervention group (Cognitive training, CT), or active control group (Passive information activities, PIA). Therapeutic adherence, sample attrition rate, medication compliance and adverse events will be monitored during the study period, and missing data analyzed using intent-to-treat analysis (ITT). Results: Training sessions of the CT group are 45 minutes/day, 3 days/week, for 12 weeks (36 sessions each). The training of active control group is the same as CT group (45min/day, 3days/week, for 12 weeks, for a total of 36 sessions). The primary outcome is cognitive function, using the Mini-Mental Status Examination (MMSE); the secondary outcome indicators are: 1) activities of daily living, using the Lawton’s Instrumental Activities of Daily Living (IADLs) and 2) degree of depressive symptoms, using the Geriatric Depression Scale-Short form (GDS-SF). Latent growth curve modeling will be used in the repeated measures statistical analysis to estimate the trajectory of improvement by examining the rate and pattern of change in cognitive functions, activities of daily living and degree of depressive symptoms for intervention efficacy over time, and the effects will be evaluated immediate post-test, 3 months, 6 months and one year after the last session. Conclusions: We constructed a rigorous CT program adhering to the Consolidated Standards of Reporting Trials (CONSORT) reporting guidelines. We expect to determine the improvement in cognitive function, activities of daily living and degree of depressive symptoms of older adults with mild cognitive impairment after using the CT.

Keywords: mild cognitive impairment, cognitive training, randomized controlled study

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Authors: Charles Davidson

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This paper presents the Stoplight Analysis System of Partnering Organizations Readiness, offering a structured framework to evaluate conflict resolution collaboration feasibility, especially crucial in conflict areas, employing a colour-coded approach and specific assessment points, with implications for more informed decision-making and improved outcomes in peacebuilding initiatives. Derived from at total of 40 years of practical peacebuilding experience from the project’s two researchers as well as interviews of various other peacebuilding actors, this paper introduces the Stoplight Analysis System of Partnering Organizations Readiness, a comprehensive framework designed to facilitate effective collaboration in international/local peacebuilding partnerships by evaluating the readiness of both potential partner organisations and the location of the proposed project. ^The system employs a colour-coded approach, categorising potential partnerships into three distinct indicators: Red (no-go), Yellow (requires further research), and Green (promising, go ahead). Within each category, specific points are identified for assessment, guiding decision-makers in evaluating the feasibility and potential success of collaboration. The Red category signals significant barriers, prompting an immediate stoppage in the consideration of partnership. The Yellow category encourages deeper investigation to determine whether potential issues can be mitigated, while the Green category signifies organisations deemed ready for collaboration. This systematic and structured approach empowers decision-makers to make informed choices, enhancing the likelihood of successful and mutually beneficial partnerships. Methodologically, this paper utilised interviews from peacebuilders from around the globe, scholarly research of extant strategies, and a collaborative review of programming from the project’s two authors from their own time in the field. This method as a formalised model has been employed for the past two years across a litany of partnership considerations, and has been adjusted according to its field experimentation. This research holds significant importance in the field of conflict resolution as it provides a systematic and structured approach to peacebuilding partnership evaluation. In conflict-affected regions, where the dynamics are complex and challenging, the Stoplight Analysis System offers decision-makers a practical tool to assess the readiness of partnering organisations. This approach can enhance the efficiency of conflict resolution efforts by ensuring that resources are directed towards partnerships with a higher likelihood of success, ultimately contributing to more effective and sustainable peacebuilding outcomes.

Keywords: collaboration, conflict resolution, partnerships, peacebuilding

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Authors: S. Trafela, X. Xua, K. Zuzek Rozmana

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In this study, we used an accurate and precise method to measure the electrochemically active surface areas (Aecsa) of nickel electrodes. Calculated Aecsa is really important for the evaluation of an electro-catalyst’s activity in electrochemical reaction of different organic compounds. The method involves the electrochemical formation of Ni(OH)₂ and NiOOH in the presence of adsorbed oxalate in alkaline media. The studies were carried out using cyclic voltammetry with polycrystalline nickel as a reference material and electrodeposited nickel nanowires, homogeneous and heterogeneous nickel films. From cyclic voltammograms, the charge (Q) values for the formation of Ni(OH)₂ and NiOOH surface oxides were calculated under various conditions. At sufficiently fast potential scan rates (200 mV s⁻¹), the adsorbed oxalate limits the growth of the surface hydroxides to a monolayer. Although the Ni(OH)₂/NiOOH oxidation peak overlaps with the oxygen evolution reaction, in the reverse scan, the NiOOH/ Ni(OH)₂ reduction peak is well-separated from other electrochemical processes and can be easily integrated. The values of these integrals were used to correlate experimentally measured charge density with an electrochemically active surface layer. The Aecsa of the nickel nanowires, homogeneous and heterogeneous nickel films were calculated to be Aecsa-NiNWs = 4.2066 ± 0.0472 cm², Aecsa-homNi = 1.7175 ± 0.0503 cm² and Aecsa-hetNi = 2.1862 ± 0.0154 cm². These valuable results were expanded and used in electrochemical studies of formaldehyde oxidation. As mentioned nickel nanowires, heterogeneous and homogeneous nickel films were used as simple and efficient sensor for formaldehyde detection. For this purpose, electrodeposited nickel electrodes were modified in 0.1 mol L⁻¹ solution of KOH in order to expect electrochemical activity towards formaldehyde. The investigation of the electrochemical behavior of formaldehyde oxidation in 0.1 mol L⁻¹ NaOH solution at the surface of modified nickel nanowires, homogeneous and heterogeneous nickel films were carried out by means of electrochemical techniques such as cyclic voltammetric and chronoamperometric methods. From investigations of effect of different formaldehyde concentrations (from 0.001 to 0.1 mol L⁻¹) on electrochemical signal - current we provided catalysis mechanism of formaldehyde oxidation, detection limit and sensitivity of nickel electrodes. The results indicated that nickel electrodes participate directly in the electrocatalytic oxidation of formaldehyde. In the overall reaction, formaldehyde in alkaline aqueous solution exists predominantly in form of CH₂(OH)O⁻, which is oxidized to CH₂(O)O⁻. Taking into account the determined (Aecsa) values we have been able to calculate the sensitivities: 7 mA mol L⁻¹ cm⁻² for nickel nanowires, 3.5 mA mol L⁻¹ cm⁻² for heterogeneous nickel film and 2 mA mol L⁻¹ cm⁻² for heterogeneous nickel film. The detection limit was 0.2 mM for nickel nanowires, 0.5 mM for porous Ni film and 0.8 mM for homogeneous Ni film. All of these results make nickel electrodes capable for further applications.

Keywords: electrochemically active surface areas, nickel electrodes, formaldehyde, electrocatalytic oxidation

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