Search results for: community- based approach

Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

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Authors: Anne Tetens, Susan Andersen, Lars Ole Bonde, Pia Jeppesen, Katrine Rich Madsen

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Background: The World Health Organization (WHO) has emphasized the critical need for feasible and effective school-based mental health promotion interventions. High-quality music education in school has been suggested to promote well-being, inclusion, and positive relations, which are essential for children’s mental health. This study explores the potential of choir singing as a distinct approach to enhance children’s mental health within the school setting. ‘Everyone Can Sing’ is a class-based mental health promotion intervention for children in grades 0-3 (ages 5-10) in Danish primary school, which integrates choir singing into the students’ normal school schedule twice a week to promote mental health through the increase of school well-being, class coherence and social inclusion. The intervention uses trained choir leaders to lead the lessons in close collaboration with the class teacher, placing a distinct emphasis on well-being and the inclusive aspect of musical expression through body and voice. Aim: The aim of the study is to evaluate the feasibility of the Everyone Can Sing intervention with the specific objective to assess implementation and changes in mental health parameters, including school well-being, class coherence and social inclusion. Methodologies: The study is a feasibility study of a one-year intervention, which started in January 2024 and is being implemented in grades 0-3 (ages 5-10) across three different Danish primary schools. It is designed according to a mixed methods approach, including both quantitative and qualitative methods. Baseline questionnaires were obtained from students, parents and teachers, and follow-up is planned at 12 months. Participant observations of class choir and individual and group interviews with students, teachers, choir leaders, and school management are collected during the intervention period. The study uses the validated ‘Strengths and Difficulties Questionnaire’ for parent- and teacher-reports. The student questionnaire, which assesses school well-being, class coherence, social inclusion and indicators of mental health, was developed and validated for this study. Participant observations and interviews provide in-depth insights into the implementation process and participants’ experiences of the mental health-promoting potential of the intervention. Findings: The study included 41 classes across three schools (N=904) and questionnaire data from students (n=845, = 93%), teachers (n=890, = 98%), and parents (n=608, = 67%) at baseline. Follow-up data will be obtained in January 2025. While collection and analyses of data are still ongoing, preliminary implementation findings based on interviews and observations indicate high levels of engagement and acceptability. At 6 months into the intervention period, the study protocol is on track and suggests that the intervention is well-received. Further findings and analyses will be presented. The final results of the study will be used to decide whether the AKS intervention should proceed to a future, full-size effectiveness trial, return to refinement of the intervention or the evaluation design, or stop. Contributions: This study will provide valuable insights into new approaches to school-based mental health promotion initiatives. If feasible, the vision is to implement the intervention or elements of it in primary schools across all five Danish regions, potentially lowering the mental health burden.

Keywords: child mental health, early childhood, mental health promotion, mixed methods research, school-based intervention.

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Syed Asif Hassan, Tabrej Khan

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Multiple sclerosis (MS) is a chronic demyelinating autoimmune disorder, of the central nervous system (CNS). In the present scenario, the current therapies either do not halt the progression of the disease or have side effects which limit the usage of current Disease Modifying Therapies (DMTs) for a longer period of time. Therefore, keeping the current treatment failure schema, we are focusing on screening novel analogues of the available DMTs that specifically bind and inhibit the Sphingosine1-phosphate receptor1 (S1PR1) thereby hindering the lymphocyte propagation toward CNS. The novel drug-like analogs molecule will decrease the frequency of relapses (recurrence of the symptoms associated with MS) with higher efficacy and lower toxicity to human system. In this study, an integrated approach involving ligand-based virtual screening protocol (Ultrafast Shape Recognition with CREDO Atom Types (USRCAT)) to identify the non-toxic drug like analogs of the approved DMTs were employed. The potency of the drug-like analog molecules to cross the Blood Brain Barrier (BBB) was estimated. Besides, molecular docking and simulation using Auto Dock Vina 1.1.2 and GOLD 3.01 were performed using the X-ray crystal structure of Mtb LprG protein to calculate the affinity and specificity of the analogs with the given LprG protein. The docking results were further confirmed by DSX (DrugScore eXtented), a robust program to evaluate the binding energy of ligands bound to the ligand binding domain of the Mtb LprG lipoprotein. The ligand, which has a higher hypothetical affinity, also has greater negative value. Further, the non-specific ligands were screened out using the structural filter proposed by Baell and Holloway. Based on the USRCAT, Lipinski’s values, toxicity and BBB analysis, the drug-like analogs of fingolimod and BG-12 showed that RTL and CHEMBL1771640, respectively are non-toxic and permeable to BBB. The successful docking and DSX analysis showed that RTL and CHEMBL1771640 could bind to the binding pocket of S1PR1 receptor protein of human with greater affinity than as compared to their parent compound (Fingolimod). In this study, we also found that all the drug-like analogs of the standard MS drugs passed the Bell and Holloway filter.

Keywords: antagonist, binding affinity, chemotherapeutics, drug-like, multiple sclerosis, S1PR1 receptor protein

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Authors: Syed Khurram Mahmud, Athar Naveed, Shoaib Arif

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Beamforming is a signal processing technique extensively utilized in wireless communications and radars for desired signal intensification and interference signal minimization through spatial selectivity. In this paper, we present a method for calculation of optimal weight vectors for smart antenna array, to achieve a directive pattern during transmission and selective reception in interference prone environment. In proposed scheme, Mutual Information (MI) extrema are evaluated through an energy constrained objective function, which is based on a-priori information of interference source and desired array factor. Signal to Interference plus Noise Ratio (SINR) performance is evaluated for both transmission and reception. In our scheme, MI is presented as an index to identify trade-off between information gain, SINR, illumination time and spatial selectivity in an energy constrained optimization problem. The employed method yields lesser computational complexity, which is presented through comparative analysis with conventional methods in vogue. MI based beamforming offers enhancement of signal integrity in degraded environment while reducing computational intricacy and correlating key performance indicators.

Keywords: beamforming, interference, mutual information, wireless communications

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Authors: Abdelhalim. N. Mohammed, Mohammed. Y. Esmail

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In the course of recent decades, medical imaging has been dominated by the use of costly film media for review and archival of medical investigation, however due to developments in networks technologies and common acceptance of a standard digital imaging and communication in medicine (DICOM) another approach in light of World Wide Web was produced. Web technologies successfully used in telemedicine applications, the combination of web technologies together with DICOM used to design a web-based and open source DICOM viewer. The Web server allowance to inquiry and recovery of images and the images viewed/manipulated inside a Web browser without need for any preinstalling software. The dynamic site page for medical images visualization and processing created by using JavaScript and HTML5 advancements. The XAMPP ‘apache server’ is used to create a local web server for testing and deployment of the dynamic site. The web-based viewer connected to multiples devices through local area network (LAN) to distribute the images inside healthcare facilities. The system offers a few focal points over ordinary picture archiving and communication systems (PACS): easy to introduce, maintain and independently platforms that allow images to display and manipulated efficiently, the system also user-friendly and easy to integrate with an existing system that have already been making use of web technologies. The wavelet-based image compression technique on which 2-D discrete wavelet transform used to decompose the image then wavelet coefficients are transmitted by entropy encoding after threshold to decrease transmission time, stockpiling cost and capacity. The performance of compression was estimated by using images quality metrics such as mean square error ‘MSE’, peak signal to noise ratio ‘PSNR’ and compression ratio ‘CR’ that achieved (83.86%) when ‘coif3’ wavelet filter is used.

Keywords: DICOM, discrete wavelet transform, PACS, HIS, LAN

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Authors: Emmanuel Billy

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This work is in the CABRISS project (H2020 projects) which aims at developing innovative cost-effective methods for the extraction of materials from the different sources of PV waste: Si based panels, thin film panels or Si water diluted slurries. Aluminum, silicon, indium, and silver will especially be extracted from these wastes in order to constitute materials feedstock which can be used later in a closed-loop process. The extraction of metals from silicon solar cells is often an energy-intensive process. It requires either smelting or leaching at elevated temperature, or the use of large quantities of strong acids or bases that require energy to produce. The energy input equates to a significant cost and an associated CO2 footprint, both of which it would be desirable to reduce. Thus there is a need to develop more energy-efficient and environmentally-compatible processes. Thus, ‘ionometallurgy’ could offer a new set of environmentally-benign process for metallurgy. This work demonstrates that ionic liquids provide one such method since they can be used to dissolve and recover silver. The overall process associates leaching, recovery and the possibility to re-use the solution in closed-loop process. This study aims to evaluate and compare different ionic liquids to leach and recover silver. An electrochemical analysis is first implemented to define the best system for the Ag dissolution. Effects of temperature, concentration and oxidizing agent are evaluated by this approach. Further, a comparative study between conventional approach (nitric acid, thiourea) and the ionic liquids (Cu and Al) focused on the leaching efficiency is conducted. A specific attention has been paid to the selection of the Ionic Liquids. Electrolytes composed of chelating anions are used to facilitate the lixiviation (Cl, Br, I,), avoid problems dealing with solubility issues of metallic species and of classical additional ligands. This approach reduces the cost of the process and facilitates the re-use of the leaching medium. To define the most suitable ionic liquids, electrochemical experiments have been carried out to evaluate the oxidation potential of silver include in the crystalline solar cells. Then, chemical dissolution of metals for crystalline solar cells have been performed for the most promising ionic liquids. After the chemical dissolution, electrodeposition has been performed to recover silver under a metallic form.

Keywords: electrodeposition, ionometallurgy, leaching, recycling, silver

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Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

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Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

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Authors: Amirhossen Zare Rahvard

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Freemium games based on the gacha mechanic have proven highly successful in recent years - games with simple graphics and simple gameplay systems but with a highly profitable market. Attempts at developing gacha games have even been made in Iran. Since gacha games are both profitable and easy to develop, they seem to be a suitable starting point for establishing a video game market in underdeveloped countries. This article aims to review the gacha games' approach to gaining revenue by studying the case of Arknights game in order to draw an outline of how simple games have led to great markets.

Keywords: gacha games, game’s economy, underdeveloped countries and games, arkngihts

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Authors: Ladda Thiamwong, Renata Komalasari

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Falls are the leading cause of disability and hospitalization in low-income older adults. Fear of falling is present in 20% to 85 % of older adults and has been identified as an independent risk factor of fall risk, activity restriction, and loss of independence. About 12% of American older adults have subjective cognitive decline. Cognitive impairment is also an established factor of fall risk. However, it is unclear whether measures of fear of falling and subjective cognitive decline have the greatest association with fall risk in low-income older adults. The aim of this study was to evaluate the association between fear of falling, subjective cognitive decline-functional performance (SCD-FP), and fall risk using simple screening tools. In this cross-section study, we collected data from community-dwelling older adults 60 years or older in low-income settings in Central Florida, and 86 participants were included in the data analysis. Fear of falling was assessed by the Short Fall Efficacy Scale- International (Short FES-I) with seven items. Subjective cognitive decline-functional performance (SCD-FP) was assessed by a self-reported experience of worsening or more frequent confusion or memory loss in the past 12 months and its functional implications. Fall risk was evaluated by the Centers for Disease Control and Prevention (CDC)'s Stay Independent checklist with 12 items. The majority of participants were female, and more than half of the participants were African American. More than half of the participants had a higher school degree or higher, and less than 20% had no financial problems. Less than 30% of the participants perceived their general health as very good- excellent. More than half of the participants lived alone, and less than 15% lived with a partner or spouse. About 60% of the participants had hypertension, 40% had diabetes, 16% had cancer, and 50% had arthritis. About 30% of the participants had difficulty walking up ten steps without resting, more than 40% felt unsteady when walking, and 30% had been advised to use a cane or walker to get around safely. Regression analysis showed that fall risk was associated with fear of falling ( = .524, p <.001) and subjective cognitive decline-functional performance ( = .465, p =.027). The structure coefficient showed that fear of falling (rs2 = .922) was a stronger predictor of fall risk than subjective cognitive decline-functional performance (rs2= .200). Fear of falling and subjective cognitive decline-functional performance are growing public health issues, and addressing those issues is a public priority. Proactive screening for fear of falling and subjective cognitive decline-functional performance is critical in fall prevention. A combination of all three self-reported tools (Short FES-I, SCD-FP, and CDC's Stay Independent checklist) takes less than 5 minutes to complete. Primary care providers or public health professionals should consider including these tools to screen fear of falling and subjective cognitive decline-functional performance as part of fall risk assessment, especially in low-income settings. Thus, encouraging older adults and healthcare professionals to discuss fear of falling, subjective cognitive decline, and fall risk during routine medical office visits.

Keywords: falls, fall risk, fear of falling, cognition, subjective cognitive decline, low-income, older adults, community, screening, nursing, primary care

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Authors: Man-Yun Liu, Emily Chia-Yu Su

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Alzheimer's disease (AD) is the public health crisis of the 21st century. AD is a degenerative brain disease and the most common cause of dementia, a costly disease on the healthcare system. Unfortunately, the cause of AD is poorly understood, furthermore; the treatments of AD so far can only alleviate symptoms rather cure or stop the progress of the disease. Currently, there are several ways to diagnose AD; medical imaging can be used to distinguish between AD, other dementias, and early onset AD, and cerebrospinal fluid (CSF). Compared with other diagnostic tools, blood (plasma) test has advantages as an approach to population-based disease screening because it is simpler, less invasive also cost effective. In our study, we used blood biomarkers dataset of The Alzheimer’s disease Neuroimaging Initiative (ADNI) which was funded by National Institutes of Health (NIH) to do data analysis and develop a prediction model. We used independent analysis of datasets to identify plasma protein biomarkers predicting early onset AD. Firstly, to compare the basic demographic statistics between the cohorts, we used SAS Enterprise Guide to do data preprocessing and statistical analysis. Secondly, we used logistic regression, neural network, decision tree to validate biomarkers by SAS Enterprise Miner. This study generated data from ADNI, contained 146 blood biomarkers from 566 participants. Participants include cognitive normal (healthy), mild cognitive impairment (MCI), and patient suffered Alzheimer’s disease (AD). Participants’ samples were separated into two groups, healthy and MCI, healthy and AD, respectively. We used the two groups to compare important biomarkers of AD and MCI. In preprocessing, we used a t-test to filter 41/47 features between the two groups (healthy and AD, healthy and MCI) before using machine learning algorithms. Then we have built model with 4 machine learning methods, the best AUC of two groups separately are 0.991/0.709. We want to stress the importance that the simple, less invasive, common blood (plasma) test may also early diagnose AD. As our opinion, the result will provide evidence that blood-based biomarkers might be an alternative diagnostics tool before further examination with CSF and medical imaging. A comprehensive study on the differences in blood-based biomarkers between AD patients and healthy subjects is warranted. Early detection of AD progression will allow physicians the opportunity for early intervention and treatment.

Keywords: Alzheimer's disease, blood-based biomarkers, diagnostics, early detection, machine learning

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Authors: Lawrence Babatunde Ogunsanya

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The university has been in existence, in one form or another, for over a thousand years and has contributed in multiple ways to modern society. It is considered a center of culture, aesthetic direction, and moral forces shaping the civilized society. Universities also contribute in important ways to the economic health and physical landscape of neighborhoods and cities, serving as permanent fixtures of the urban economy and the built environment. Due to the size and location of university campuses, they put demands on the urban character, systems, and infrastructure of the neighboring communities. These demands or impacts have substantial implications for the built environment. It is important to understand the impacts university campuses have on their surrounding communities and urban environments because the destiny of the university is inextricably linked to the destiny of the adjacent neighborhoods. This paper identifies the diverse factors generated by universities in shaping the built environments of their local communities within different spatial contexts such as urban, rural, and township regions situated in South Africa.By applying a mixed methods approach in four university campuses within the province of KwaZulu-Natal in South Africa. Several data collection instruments were used, such as in-depth interviews, a survey, remote sensing, and onsite observations. The thematic findings revealed numerous factors which influence the morphology of neighbourhood built environments and the myriad of relationships the university has with its local community. This paper also reveals that the university campus is more than a precinct which accommodates buildings and academic endeavours, the role of the university in this century has changed dramatically from its traditional roots of being an elite enclave of academics to a more inclusive and engaged entity that is concerned about providing relevant holistic solutions to society’s current challenges in the built environment.

Keywords: university campus, built environment, architecture, neighborhood planning

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Authors: Anca-Diana Bibiri, Mihaela Mocanu, Adrian Turculeț

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In a language the inventory of greetings is dynamic with frequent input and output, although this is hardly noticed by the speakers. In this register, there are a number of constant, conservative elements that survive different language models (among them, the classic formulae: bună ziua! (good afternoon!), bună seara! (good evening!), noapte bună! (good night!), la revedere! (goodbye!) and a number of items that fail to pass the test of time, according to language use at a time (ciao!, pa!, bai!). The source of innovation depends both of internal factors (contraction, conversion, combination of classic formulae of greetings), and of external ones (borrowings and calques). Their use imposes their frequencies at once, namely the elimination of the use of others. This paper presents a sociolinguistic approach of contemporary Romanian greetings, based on prosodic surveys in two research projects: AMPRom, and SoRoEs. Romanian language presents a rich inventory of questions (especially partial interrogatives questions/WH-Q) which are used as greetings, alone or, more commonly accompanying a proper greeting. The representative of the typical formulae is Ce mai faci? (How are you?), which, unlike its English counterpart How do you do?, has not become a stereotype, but retains an obvious emotional impact, while serving as a mark of sociolinguistic group. The analyzed corpus consists of structures containing greetings recorded in the main Romanian cultural (urban) centers. From the methodological point of view, the acoustic analysis of the recorded data is performed using software tools (GoldWave, Praat), identifying intonation patterns related to three sociolinguistics variables: age, sex and level of education. The intonation patterns of the analyzed statements are at the interface between partial questions and typical greetings.

Keywords: acoustic analysis, greetings, Romanian language, sociolinguistics

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Authors: Aïcha Ben Salem, Faouzi Boufares, Sebastiao Correia

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The subject covered in this paper aims at assisting the user in its quality approach. The goal is to better extract, mix, interpret and reuse data. It deals with the semantic schema recognition of a data source. This enables the extraction of data semantics from all the available information, inculding the data and the metadata. Firstly, it consists of categorizing the data by assigning it to a category and possibly a sub-category, and secondly, of establishing relations between columns and possibly discovering the semantics of the manipulated data source. These links detected between columns offer a better understanding of the source and the alternatives for correcting data. This approach allows automatic detection of a large number of syntactic and semantic anomalies.

Keywords: schema recognition, semantic data profiling, meta-categorisation, semantic dependencies inter columns

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Authors: Steven Sam, Ximena Schmidt, Hugh Dickinson, Jens Jensen

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The goal of this study is to explore the potential of data science and citizen science approaches to develop an interactive, digital, open infrastructure that pulls together African indigenous agriculture and food systems data from multiple sources, making it accessible and reusable for policy, research and practice in modern food production efforts. The World Bank has recognised that African Indigenous Knowledge (AIK) is innovative and unique among local and subsistent smallholder farmers, and it is central to sustainable food production and enhancing biodiversity and natural resources in many poor, rural societies. AIK refers to tacit knowledge held in different languages, cultures and skills passed down from generation to generation by word of mouth. AIK is a key driver of food production, preservation, and consumption for more than 80% of citizens in Africa, and can therefore assist modern efforts of reducing food insecurity and hunger. However, the documentation and dissemination of AIK remain a big challenge confronting librarians and other information professionals in Africa, and there is a risk of losing AIK owing to urban migration, modernisation, land grabbing, and the emergence of relatively small-scale commercial farming businesses. There is also a clear disconnect between the AIK and scientific knowledge and modern efforts for sustainable food production. The study combines data science and citizen science approaches through active community participation to generate and share AIK for facilitating learning and promoting knowledge that is relevant for policy intervention and sustainable food production through a curated digital platform based on FAIR principles. The study adopts key informant interviews along with participatory photo and video elicitation approach, where farmers are given digital devices (mobile phones) to record and document their every practice involving agriculture, food production, processing, and consumption by traditional means. Data collected are analysed using the UK Science and Technology Facilities Council’s proven methodology of citizen science (Zooniverse) and data science. Outcomes are presented in participatory stakeholder workshops, where the researchers outline plans for creating the platform and developing the knowledge sharing standard framework and copyrights agreement. Overall, the study shows that learning from AIK, by investigating what local communities know and have, can improve understanding of food production and consumption, in particular in times of stress or shocks affecting the food systems and communities. Thus, the platform can be useful for local populations, research, and policy-makers, and it could lead to transformative innovation in the food system, creating a fundamental shift in the way the North supports sustainable, modern food production efforts in Africa.

Keywords: Africa indigenous agriculture knowledge, citizen science, data science, sustainable food production, traditional food system

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Authors: Lucia Billeci, Gennaro Tartarisco, Maurizio Varanini

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Fetal cardiac monitoring by fetal electrocardiogram (fECG) can provide significant clinical information about the healthy condition of the fetus. Despite this potentiality till now the use of fECG in clinical practice has been quite limited due to the difficulties in its measuring. The recovery of fECG from the signals acquired non-invasively by using electrodes placed on the maternal abdomen is a challenging task because abdominal signals are a mixture of several components and the fetal one is very weak. This paper presents an approach for fECG extraction from abdominal maternal recordings, which exploits the characteristics of pseudo-periodicity of fetal ECG. It consists of devising a quality index (fQI) for fECG and of finding the linear combinations of preprocessed abdominal signals, which maximize these fQI (quality index optimization - QIO). It aims at improving the performances of the most commonly adopted methods for fECG extraction, usually based on maternal ECG (mECG) estimating and canceling. The procedure for the fECG extraction and fetal QRS (fQRS) detection is completely unsupervised and based on the following steps: signal pre-processing; maternal ECG (mECG) extraction and maternal QRS detection; mECG component approximation and canceling by weighted principal component analysis; fECG extraction by fQI maximization and fetal QRS detection. The proposed method was compared with our previously developed procedure, which obtained the highest at the Physionet/Computing in Cardiology Challenge 2013. That procedure was based on removing the mECG from abdominal signals estimated by a principal component analysis (PCA) and applying the Independent component Analysis (ICA) on the residual signals. Both methods were developed and tuned using 69, 1 min long, abdominal measurements with fetal QRS annotation of the dataset A provided by PhysioNet/Computing in Cardiology Challenge 2013. The QIO-based and the ICA-based methods were compared in analyzing two databases of abdominal maternal ECG available on the Physionet site. The first is the Abdominal and Direct Fetal Electrocardiogram Database (ADdb) which contains the fetal QRS annotations thus allowing a quantitative performance comparison, the second is the Non-Invasive Fetal Electrocardiogram Database (NIdb), which does not contain the fetal QRS annotations so that the comparison between the two methods can be only qualitative. In particular, the comparison on NIdb was performed defining an index of quality for the fetal RR series. On the annotated database ADdb the QIO method, provided the performance indexes Sens=0.9988, PPA=0.9991, F1=0.9989 overcoming the ICA-based one, which provided Sens=0.9966, PPA=0.9972, F1=0.9969. The comparison on NIdb was performed defining an index of quality for the fetal RR series. The index of quality resulted higher for the QIO-based method compared to the ICA-based one in 35 records out 55 cases of the NIdb. The QIO-based method gave very high performances with both the databases. The results of this study foresees the application of the algorithm in a fully unsupervised way for the implementation in wearable devices for self-monitoring of fetal health.

Keywords: fetal electrocardiography, fetal QRS detection, independent component analysis (ICA), optimization, wearable

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Authors: Yang Ye, Hongfei Qiu, Yaqi Li

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The planning and construction of traditional cities are oriented by cars, which leads to the problems of insufficient urban public space, fragmentation, and low utilization efficiency. With the development of driverless technology, the urban structure will change from the traditional single-core grid structure to the multi-core model. In terms of traffic organization, with the release of land for traffic facilities, public space will become more continuous and integrated with traffic space. In the context of driverless technology, urban public reconstruction is characterized by modularization and high efficiency, and its planning and layout features accord with points (service facilities), lines (smart lines), surfaces (activity centers). The public space of driverless urban roads will provide diversified urban public facilities and services. The intensive urban layout makes the commercial public space realize the functions of central activities and style display, respectively, in the interior (building atrium) and the exterior (building periphery). In addition to recreation function, urban green space can also utilize underground parking space to realize efficient dispatching of shared cars. The roads inside the residential community will be integrated into the urban landscape, providing conditions for the community public activity space with changing time sequence and improving the efficiency of space utilization. The intervention of driverless technology will change the thinking of traditional urban construction and turn it into a human-oriented one. As a result, urban public space will be richer, more connected, more efficient, and the urban space justice will be optimized. By summarizing the frontier research, this paper discusses the impact of unmanned driving on cities, especially urban public space, which is beneficial for landscape architects to cope with the future development and changes of the industry and provides a reference for the related research and practice.

Keywords: driverless, urban public space, construction strategy, urban design

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Authors: Nutthan Inkhong, Sutthiphong Ruangchante

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Chiang Rai province is located at the northern border of Thailand. Most of the geography there is the northern continental highlands, and the population has many types of inhabitants, including Thai people, immigrants and ethnic groups such as Akha, Lahu, Lisu, Yao, etc. Most of these ethnic groups migrated from neighbouring countries such as Myanmar, Laos, China, etc. and settled in the mountains. Each ethnic group has their unique traditions, culture, and ways of life, including the musical culture that the ancestors of each ethnic group brought with them. In the present, the Akha have the largest population in the region and still live together in numerous villages in many districts. Thus, Akha musical culture still appears in the community traditions and cultural events of Chiang Rai province regularly. This article presents the situations of Akha musical culture in the present and the predictions for the future. The study method involves the analysis of music information and the related social contexts, which were collected from the fieldwork of ethnomusicological methodology by in-depth interviews, observations, audio and visual recordings, and related documents. The results found that the important persons who are related with Akha musical culture include (1) a musical instrument maker (lives in Mae Chan district) who produces various Akha musical instruments, including gourd mouth organs, Akha drums, two-way flutes, three-hole flutes, Jew’s harps (the sound of teenage love), buffalo horns (the sound symbol of hunting) and bird call instruments (the imitation of bird sounds), (2) a folk philosopher (lives in Mae Pha Luang district) who can teach music to the new generation of Akha people as well as lecture and demonstrate music to academics and tourists, and (3) a community leader (lives in Mae Chan district) who conserves Akha performances, singing and music through various activities of the students in an informal school. Because of the changes to the social contexts and ways of life of the Akha people, such as the educational system, religion, social media, etc., including the popularity of both Thai and international popular music among the new generation of Akha people, changes to and the fading away of Akha musical culture in the future may likely occur. Therefore, the conservation and inheritance of Akha music is an issue that should be resolved quickly. This primary study leads to the next step of the ethnomusicological work and plays a part in preventing or reducing the problems impacting Akha musical culture survival by the recording of Akha music in all of its dimensions, such as producing musical instruments, playing musical instruments, analysis of tuning systems, recording Akha music as musical notation using symbols, researching related social contexts, etc. and the transcription of this information to create lessons that can be returned to the Akha community.

Keywords: Akha music, Chiang Rai, ethnic music in Thailand, ethnomusicology

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Authors: Ju-Hong Lee, Yi-Lin Shieh

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Two-dimensional (2-D) quadrature mirror filter (QMF) banks have been widely considered for high-quality coding of image and video data at low bit rates. Without implementing subband coding, a 2-D QMF bank is required to have an exactly linear-phase response without magnitude distortion, i.e., the perfect reconstruction (PR) characteristics. The design problem of 2-D QMF banks with the PR characteristics has been considered in the literature for many years. This paper presents a transformation approach for designing 2-D two-channel QMF banks. Under a suitable one-dimensional (1-D) to two-dimensional (2-D) transformation with a specified decimation/interpolation matrix, the analysis and synthesis filters of the QMF bank are composed of 1-D causal and stable digital allpass filters (DAFs) and possess the 2-D doubly complementary half-band (DC-HB) property. This facilitates the design problem of the two-channel QMF banks by finding the real coefficients of the 1-D recursive DAFs. The design problem is formulated based on the minimax phase approximation for the 1-D DAFs. A novel objective function is then derived to obtain an optimization for 1-D minimax phase approximation. As a result, the problem of minimizing the objective function can be simply solved by using the well-known weighted least-squares (WLS) algorithm in the minimax (L∞) optimal sense. The novelty of the proposed design method is that the design procedure is very simple and the designed 2-D QMF bank achieves perfect magnitude response and possesses satisfactory phase response. Simulation results show that the proposed design method provides much better design performance and much less design complexity as compared with the existing techniques.

Keywords: Quincunx QMF bank, doubly complementary filter, digital allpass filter, WLS algorithm

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Authors: Roseliza Mat Alipiah, David Raffaelli, J. C. R. Smart

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This research is a new approach as it integrates information from both environmental and social sciences to inform effective management of the wetlands. A three-stage research framework was developed for modelling the drivers and pressures imposed on the wetlands and their impacts to the ecosystem and the local communities. Firstly, a Bayesian Belief Network (BBN) was used to predict the probability of anthropogenic activities affecting the delivery of different key wetland ecosystem services under different management scenarios. Secondly, Choice Experiments (CEs) were used to quantify the relative preferences which key wetland stakeholder group (aquaculturists) held for delivery of different levels of these key ecosystem services. Thirdly, a Multi-Criteria Decision Analysis (MCDA) was applied to produce an ordinal ranking of the alternative management scenarios accounting for their impacts upon ecosystem service delivery as perceived through the preferences of the aquaculturists. This integrated ecosystem management approach was applied to a wetland ecosystem in Setiu, Terengganu, Malaysia which currently supports a significant level of aquaculture activities. This research has produced clear guidelines to inform policy makers considering alternative wetland management scenarios: Intensive Aquaculture, Conservation or Ecotourism, in addition to the Status Quo. The findings of this research are as follows: The BBN revealed that current aquaculture activity is likely to have significant impacts on water column nutrient enrichment, but trivial impacts on caged fish biomass, especially under the Intensive Aquaculture scenario. Secondly, the best fitting CE models identified several stakeholder sub-groups for aquaculturists, each with distinct sets of preferences for the delivery of key ecosystem services. Thirdly, the MCDA identified Conservation as the most desirable scenario overall based on ordinal ranking in the eyes of most of the stakeholder sub-groups. Ecotourism and Status Quo scenarios were the next most preferred and Intensive Aquaculture was the least desirable scenario. The methodologies developed through this research provide an opportunity for improving planning and decision making processes that aim to deliver sustainable management of wetland ecosystems in Malaysia.

Keywords: Bayesian belief network (BBN), choice experiments (CE), multi-criteria decision analysis (MCDA), aquaculture

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Authors: Gbone Akou Sophie

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Lome state in Togo is considered to have the highest HIV prevalence in Togo according to NAIIS 2023, with the prevalence of 5.5%, Female Sex Workers (FSW) are one of the most vulnerable population, and they are vital in HIV programming. They have the highest HIV prevalence compared to others such as HRM, PWID and Transgender in lome State, Togo. Evidence from Integrated Biological Behavioral Surveillance Survey shows increasing burden of HIV infection from 13.7% in 20018 to 17.2% in 2020 and now 22.9% in 2021 among Female Sex Workers (FSW). This shows their HIV prevalence has been rising over time. The vulnerability status of the FSW in the riverine areas of lome is heightened because of cultural and economic issues where there is exchange of sex for commodities with cross border traders as well as limited access to HIV prevention information. Methods:A cross sectional study which recruited 120 FSW from two Riverine LGAs of Agoe and Kpehenou LGA of Lome State using both snowballing and simple random sampling technique. While semi-structured questionnaire was used as an instrument for data collection among the 120 FSW respondents. Additional information was also elicited from 10 FSW key opinion leaders and community members through in-depth interviews (IDI). Results: 44(36%) of respondents were willing to receive regular HIV care and services as well as visit for STI check-ups at any service point. However, 47(40%) were willing to receive services at private facilities alone, 10 (8%) were willing to receive services at public facilities, 6 (5%) were willing to access services in their homes rather than in the health facility. 13 (11%) were also willing to have peers assist in getting HIV testing services. Conclusion: integrated differentiated model of care for HIV services helps improve HIV services uptake among FSW community especially in the hard- to reach riverine areas which will further lead to epidemic control. Also targeted HIV information should be designed to suit the learning needs of the hard-to reach communities like the riverine areas. More peer educators should be engaged to ensure information and other HIV services reach the riverine communities.

Keywords: female sex workers ( FSW), human immuno-deficiency virus(HIV), prevanlence, service delivery

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Authors: Y. Melis, E. Apicella, E. Dozio, L. Mendolicchio

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Introduction: Feeding and Eating Disorders (FED) represent the psychiatric pathology with the highest mortality rate and one of the major disorders with the highest psychiatric and clinical comorbidity. The vagus nerve represents one of the main components of the sympathetic and parasympathetic nervous system and is involved in important neurophysiological functions. In FED, there is a spectrum of symptoms which with TaVNS (Transcutaneous Auricular Vagus Nerve Stimulation) therapy, is possible to have a therapeutic efficacy. Materials and Methods: Sample subjects are composed of 15 female subjects aged > 18 ± 51. Admitted to a psychiatry community having been diagnosed according to DSM-5: anorexia nervosa (AN) (N= 9), bulimia nervosa (BN) (N= 5), binge eating disorder (BED) (N= 1). The protocol included 9 weeks of Ta-VNS stimulation at a frequency of 1.5-3.5 mA for 4 hours per day. The variables detected are the following: Heart Rate Variability (HRV), Hamilton Depression Rating Scale (HAMD-HDRS-17), Body Mass Index (BMI), Beck Anxiety Index (BAI). Results: Data analysis showed statistically significant differences between recording times (p > 0.05) in HAM-D (t0 = 18.28 ± 5.31; t4 = 9.14 ± 7.15), in BAI (t0 = 24.7 ± 10.99; t4 = 13.8 ± 7.0). The reported values show how during (T0-T4) the treatment there is a decay of the degree in the depressive state, in the state of anxiety, and an improvement in the value of BMI. In particular, the BMI in the AN-BN sub-sample had a minimum gain of 5% and a maximum of 11%. The analysis of HRV did not show a clear change among subjects, thus confirming the discordance of the activity of the sympathetic and parasympathetic nervous system in FED. Conclusions: Although the sample does not possess a relevant value to determine long-term efficacy of Ta-VNS or on a larger population, this study reports how the application of neuro-stimulation in FED may become a further approach therapeutic. Indeed, substantial improvements are highlighted in the results and confirmed hypotheses proposed by the study.

Keywords: feeding and eating disorders, neurostimulation, anxiety disorders, depression

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Authors: Jun Li, Wenze Wang, Zhanggui Hu, Yongwei Zhu, Dunwen Zuo

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Non-water based fixed abrasive polishing was adopted to manufacture LBO crystal for nano precision surface quality because of its deliquescent. Ethyl alcohol was selected as the non-water based slurry solvent and ethanediamine, lactic acid, hydrogen peroxide were add in the slurry as a chemical additive, respectively. Effect of different additives with non-water based slurry on material removal rate, surface topography, microscopic appearances and surface roughness were investigated in fixed abrasive polishing of LBO crystal. The results show the best surface quality of LBO crystal with surface roughness Sa 8.2 nm and small damages was obtained by non-water based slurry with lactic acid. Non-water based fixed abrasive polishing can achieve nano precision surface quality of LBO crystal with high material removal.

Keywords: non-water based slurry, LBO crystal, fixed abrasive polishing, surface roughness

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Authors: Abdelkader Mendas

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The integration of MultiCriteria Decision Making (MCDM) approaches in a Geographical Information System (GIS) provides a powerful spatial decision support system which offers the opportunity to efficiently produce the land suitability maps for agriculture. Indeed, GIS is a powerful tool for analyzing spatial data and establishing a process for decision support. Because of their spatial aggregation functions, MCDM methods can facilitate decision making in situations where several solutions are available, various criteria have to be taken into account and decision-makers are in conflict. The parameters and the classification system used in this work are inspired from the FAO (Food and Agriculture Organization) approach dedicated to a sustainable agriculture. A spatial decision support system has been developed for establishing the land suitability map for agriculture. It incorporates the multicriteria analysis method ELECTRE Tri (ELimitation Et Choix Traduisant la REalité) in a GIS within the GIS program package environment. The main purpose of this research is to propose a conceptual and methodological framework for the combination of GIS and multicriteria methods in a single coherent system that takes into account the whole process from the acquisition of spatially referenced data to decision-making. In this context, a spatial decision support system for developing land suitability maps for agriculture has been developed. The algorithm of ELECTRE Tri is incorporated into a GIS environment and added to the other analysis functions of GIS. This approach has been tested on an area in Algeria. A land suitability map for durum wheat has been produced. Through the obtained results, it appears that ELECTRE Tri method, integrated into a GIS, is better suited to the problem of land suitability for agriculture. The coherence of the obtained maps confirms the system effectiveness.

Keywords: multicriteria decision analysis, decision support system, geographical information system, land suitability for agriculture

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Authors: Zhaopeng Xuan, Yuan Bian, C. Cailleaux, Jing Qin, S. Traore

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Traditional software engineering allows engineers to propose to their clients multiple specialized software distributions assembled from a shared set of software assets. The management of these assets however requires a trade-off between client satisfaction and software engineering process. Clients have more and more difficult to find a distribution or components based on their needs from all of distributed repositories. This paper proposes a software engineering for a user-driven software product line in which engineers define a feature model but users drive the actual software distribution on demand. This approach makes the user become final actor as a release manager in software engineering process, increasing user product satisfaction and simplifying user operations to find required components. In addition, it provides a way for engineers to manage and assembly large software families. As a proof of concept, a user-driven software product line is implemented for eclipse, an integrated development environment. An eclipse feature model is defined, which is exposed to users on a cloud-based built platform from which clients can download individualized Eclipse distributions.

Keywords: software product line, model-driven development, reverse engineering and refactoring, agile method

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Authors: Nitaya Buntao

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This paper considers the problems of interval estimation for rainfall mean of the lognormal distribution and the delta-lognormal distribution in Northeastern Thailand. We present here the modified generalized pivotal approach (MGPA) compared to the modified method of variance estimates recovery (MMOVER). The performance of each method is examined in term of coverage probabilities and average lengths by Monte Carlo simulation. An extensive simulation study indicates that the MMOVER performs better than the MGPA approach in terms of the coverage probability; it results in highly accurate coverage probability.

Keywords: rainfall mean, interval estimation, lognormal distribution, delta-lognormal distribution

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Authors: Ying-Lun Tseng Chiu-Ying Chen

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Taiwan's Mental Health Act was amended at the end of 2022; they added regulations regarding refusing compulsory treatment by patients with mental illnesses. In addition, not only by an examination committee, the judge must also assess the patient's need for compulsory treatment. Additionally, the maximum of compulsory hospitalization has been reduced from an unlimited period to a maximum of 60 days. They aim to promote the healthcare autonomy of individuals with mental illnesses in Taiwan and prevent their silenced voice in medical decision-making while they still possess rationality. Furthermore, they plan to use community support and social care networks to replace the current practice of compulsory treatment in Taiwan. This study uses qualitative research methodology, utilizing interview guidelines to inquire about the experiences of Taiwanese who have undergone compulsory hospitalization, compulsory community treatment, and compulsory medical care. The interviews aimed to explore their feelings when they were subjected to compulsory medical intervention, the inside of their illness, their opinions after treatments, and whether alternative medical interventions proposed by them were considered. Additionally, participants also asked about their personal life history and their support networks in their lives. We collected 12 Taiwanese who had experienced compulsory medical interventions and were interviewed 14 times. The findings indicated that participants still possessed rationality during the onset of their illness. However, when they have other treatments to replace compulsory medical, they sometimes diverge from those of the doctors and their families. Finally, doctors prefer their professional judgment and patients' families' option. Therefore, Taiwanese mental health patients' power of decision-making still needs to improve. Because this research uses qualitative research, so difficult to find participants, and the sample size rate was smaller than Taiwan's population, it may have biases in the analysis. So, Taiwan still has significant progress in enhancing the decision-making rights of participants in the study.

Keywords: medical decision making, compulsory treatment, medical ethics, mental health act

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Authors: O. Ekrami, P. Claes, S. Van Dongen

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Fluctuating asymmetry (FA) has been studied for many years as an indicator of developmental stability or ‘genetic quality’ based on the assumption that perfect symmetry is ideally the expected outcome for a bilateral organism. Further studies have also investigated the possible link between FA and attractiveness or levels of masculinity or femininity. These hypotheses have been mostly examined using 2D images, and the structure of interest is usually presented using a limited number of landmarks. Such methods have the downside of simplifying and reducing the dimensionality of the structure, which will in return increase the error of the analysis. In an attempt to reach more conclusive and accurate results, in this study we have used high-resolution 3D scans of human faces and have developed an algorithm to measure and localize FA, taking a spatially-dense approach. A symmetric spatially dense anthropometric mask with paired vertices is non-rigidly mapped on target faces using an Iterative Closest Point (ICP) registration algorithm. A set of 19 manually indicated landmarks were used to examine the precision of our mapping step. The protocol’s accuracy in measurement and localizing FA is assessed using simulated faces with known amounts of asymmetry added to them. The results of validation of our approach show that the algorithm is perfectly capable of locating and measuring FA in 3D simulated faces. With the use of such algorithm, the additional captured information on asymmetry can be used to improve the studies of FA as an indicator of fitness or attractiveness. This algorithm can especially be of great benefit in studies of high number of subjects due to its automated and time-efficient nature. Additionally, taking a spatially dense approach provides us with information about the locality of FA, which is impossible to obtain using conventional methods. It also enables us to analyze the asymmetry of a morphological structures in a multivariate manner; This can be achieved by using methods such as Principal Components Analysis (PCA) or Factor Analysis, which can be a step towards understanding the underlying processes of asymmetry. This method can also be used in combination with genome wide association studies to help unravel the genetic bases of FA. To conclude, we introduced an algorithm to study and analyze asymmetry in human faces, with the possibility of extending the application to other morphological structures, in an automated, accurate and multi-variate framework.

Keywords: developmental stability, fluctuating asymmetry, morphometrics, 3D image processing

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Authors: Tumelo Jaquiline Ntsopi

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This study explored the academic writing challenges of first-year students who use English as an Additional Language (EAL) registered in the EAW101 module at an ODeL institution. Research shows that academic writing is a challenge for EAL teaching and learning contexts across the globe in higher education institutions (HEIs). Academic writing is an important aspect of academic literacy in any institution of higher learning, more so in an ODeL institution. This has probed research that shows that academic writing is and continues to pose challenges for EAL teaching and learning contexts in higher education institutions. This study stems from the researcher’s experience in teaching academic writing to first-year students in the EAW101 module. The motivation for this study emerged from the fact that EAW101 is a writing module that has a high number of students in the Department of English Studies with an average of between 50-80 percent pass rate. These statistics elaborate on the argument that most students registered in this module struggle with academic writing, and they need intervention to assist and support them in achieving competence in the module. This study is underpinned by Community of Inquiry (CoI) framework and Transactional distance theory. This study adopted a qualitative research methodology and utilised a case study approach as a research design. Furthermore, the study gathered data from first year students and the EAW101 module’s student support initiatives. To collect data, focus group discussions, structured open-ended evaluation questions, and an observation schedule were used to gather data. The study is vital towards exploring academic writing challenges that first-year students in EAW101 encounter so that lecturers in the module may consider re-evaluating their methods of teaching to improve EAL students’ academic writing skills. This study may help lecturers towards enhancing academic writing in a ODeL context by assisting first year students through using student support interventions.

Keywords: academic writing, academic writing challenge, ODeL, EAL

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Authors: Sandra Carrillo Hoyos

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Mining activities in South America have significantly grown during the last decades, given the abundance of natural resources, the implemented governmental policies to incentivize foreign investment as well as the boom in international prices for metals and oil between 2002 and 2008. While this context allowed the region to occupy a leading position between the top producers of minerals around the world, it has also meant an increase in socio-environmental conflicts which have generated costs and negative impacts not only for the companies but especially for the governments and local communities.During the latest decade, the mining sector in Peru has faced with the social resistance of a large number of communities, which began organizing actions against the implementation of high investing projects. The dissatisfaction has derived in the prevalence of socio-environmental conflicts associated with mining activities, some of them never solved into an agreement. In order to prevent those socio-environmental conflicts and obtain the social license from local communities, most of the mining companies have developed diverse initiatives within the framework of policies and practices of corporate social responsibility (CSR). This paper has assessed the mining sector’s contribution toward the local development management along the last decade, as part of CSR strategies as well as the policies promoted by the Peruvian State. This assessment found that, in the beginning, these initiatives have been based on a philanthropic approach and were reacting to pressures from local stakeholders to maintain the consent to operate from the surrounding communities as well as to create, as a result, a harmonious atmosphere for operations. Due to the weak State presence, such practices have increased the expectations of communities related to the participation of mining companies in solving structural development problems, especially those related to primary needs, infrastructure, education, health, among others. In other words, this paper was focused on analyze in what extent these initiatives have promoted local empowerment for development planning and integrated management of natural resources from a territorial approach. From this perspective, the analysis demonstrates that, while the design and planning of social investment initiatives have improved due to the sector´s sustainability approach, many companies have developed actions beyond their competence during this process. In some cases, the referenced actions have generated dependency with communities, even though this relationship has not exempted the companies of conflict situations with unfortunate consequences. Furthermore, the social programs developed have not necessarily generated a significant impact in improving the quality of life of affected populations. In fact, it is possible to identify that those regions with high mining resources and investment are facing with a situation of poverty and high dependency on mining production. In spite of the revenues derived from mining industry, local governments have not been able to translate the royalties into sustainable development opportunities. For this reason, the proposed paper suggests some challenges for the mining sector contribution to local development based on the best practices and lessons learnt from a benchmarking for the leading mining companies.

Keywords: corporate social responsibility, local development, mining, socio-environmental conflict

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