Exploring Enablers and Barriers to Municipal Solid Waste (MSW) Management Technologies Adoption in the Kingdom of Bahrain

Municipal Solid Waste (MSW) becomes a point of focus globally due to its harmful effects on the environment and human health if it is not managed properly. MSW was proved globally to be used as a resource, and it has a major opportunity in the realm of conversion technologies. Accordingly, this study aims to explore the enablers and barriers to six different MSW management (MSWM) technologies adopted in the Kingdom of Bahrain, in order to ensure the successful adoption of these technologies that are Anaerobic Digestion (AD), Incineration, Pyrolysis, Gasification, Composting and Refused Derived Fuel (RDF). This study provides sufficient information to the decision and policy-makers for the processes of selection and adoption of the MSWM technologies in Bahrain, using a qualitative approach “mainly semi-structured interviews” with experts and then Thematic Analysis using nvivo12 software. The results show that the main themes that enablers and barriers fall under are: political (e.g. national waste management strategy), technical (e.g. segregation at source), managerial (e.g. capacity building), social (e.g. public awareness), economic (e.g. incentives to investment) and environmental (e.g. air quality). This study concluded that in order to succeed in the MSWM technologies adoption, the resulted barriers should be overcome. Furthermore, Incineration was recommended as the best solution to manage MSW which has the least barriers and most enablers in Bahrain as per the experts.

Enablers, Difficulties and Features to Implement E-Learning Technology in Conventional Higher Education: Case Study and Prototype Implication

Supporting higher education with modern technologies like E-Learning is very important for one country to improve quality of education, to meet student’s expectations and to continue teaching-learning and training when face to face education is impossible. However, it is in its preliminary stage in developing countries like Ethiopia. This study examined the enabling factors, difficulties and opportunities of E-Learning implementation in Assosa University (ASU), Ethiopia. Its purpose is to find the enabling factors, difficulties and opportunities of E-Learning implementation in ASU and developing prototype of E-Learning system to show its practicality and to identify approaches of students’ and lecturers towards E-Learning. The study employed questionnaires, observation and interview to gather the required information. A sample of 309 students and 64 Lecturers randomly selected from 7 colleges and two schools as well as ICT workers and other concerned bodies in the university. Also prototyping as a methodology was used to implement and test the proposed system for proof of concept. This study investigating the possibility of implementing E-Learning in ASU and important enablers, difficulties and opportunities is identified. Also the E-Learning platform of the university is developed and introduced for students and lecturers to show its practicality. Most students and lecturers showed good motivation in E-Learning implementation and they assumed that it is useful for the university. The result shows that although there are difficulties to implement E-Learning in ASU, the possibility of fully implementing E-Learning in the University is relatively high with mixed method. With this, the approaches of students and lecturers are positively viewed and the opportunities are very noticeable in the University. So, conventional higher education can practically implement E-Learning with mixed approach to use as supportive tool for educational improvements and to reduce physical presence.

Dendritic Deep Learning for Medical Segmentation

Dear Editor,This letter presents a novel segmentation approach that leverages dendritic neurons to tackle the challenges of medical imaging segmentation.In this study,we enhance the segmentation accuracy based on a SegNet variant including an encoder-decoder structure,an upsampling index,and a deep supervision method.Furthermore,we introduce a dendritic neuron-based convolutional block to enable nonlinear feature mapping,thereby further improving the effectiveness of our approach.

Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

Osteoarthritis(OA)is a debilitating degenerative disease affecting multiple joint tissues,including cartilage,bone,synovium,and adipose tissues.OA presents diverse clinical phenotypes and distinct molecular endotypes,including inflammatory,metabolic,mechanical,genetic,and synovial variants.Consequently,innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches.Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints,causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues.This issue has led to standardization difficulties and hindered the success of clinical trials.Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues,encompassing DNA,RNA,metabolites,and proteins,as well as their chemical properties,elemental composition,and mechanical attributes,can contribute to a more comprehensive understanding of the disease subtypes.Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment,providing a more holistic view of cellular function.Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various-omics lenses,such as genomics,transcriptomics,proteomics,and metabolomics,with spatial data.This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates.Furthermore,advanced imaging techniques,including high-resolution microscopy,hyperspectral imaging,and mass spectrometry imaging,enable the visualization and analysis of the spatial distribution of biomolecules,cells,and tissues.Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes.This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis.It explores their applications,challenges,and potential opportunities in the field of OA.Additionally,this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.

Implantable probe with integrated reference electrode for in situ neural signal and calcium ion monitoring

Monitoring the electrophysiology activity of neurons and blood calcium signals can enable a better understanding of disease-related neural system circuits.However,currently,in situ calcium ion monitoring tools are scarce and exhibit low integration and limited sensitivity.In this letter,we propose an implantable probe with an integrated in situ Ag/AgCl reference electrode(ISA/ARE)that can monitor action potential(AP)and Ca^(2+) concentrations.

Proteins as nanomagnets and magnetoreceptors

It is an appealing notion that a protein molecule could act as a nanomagnet.A genetically encodable biomolecule with a permanent magnetic moment at room temperature could have a range of applications:a magnetogenetic actuator,a magnetic tag for purifying and immobilizing enzymes,a contrast agent for magnetic resonance imaging,and a basis for a biomimetic magnetic sensing device,to name just a few.A magnetic protein could perhaps also function as the sensor in the magnetic compass that enables small songbirds to navigate the huge distances between their breeding and wintering grounds.Attractive though such possibilities may be,how realistic are they?

Current-Induced Magnetization Switching Behavior in Perpendicular Magnetized L1_(0)-MnAl/B2-CoGa Bilayer

Rare-earth-free Mn-based binary alloy L1_(0)-MnAl with bulk perpendicular magnetic anisotropy(PMA) holds promise for high-performance magnetic random access memory(MRAM) devices driven by spin-orbit torque(SOT). However, the lattice-mismatch issue makes it challenging to place conventional spin current sources, such as heavy metals, between L1_(0)-MnAl layers and substrates. In this work, we propose a solution by using the B2-CoGa alloy as the spin current source. The lattice-matching enables high-quality epitaxial growth of 2-nm-thick L1_(0)-MnAl on B2-CoGa, and the L1_(0)-MnAl exhibits a large PMA constant of 1.04 × 10^(6)J/m^(3). Subsequently, the considerable spin Hall effect in B2-CoGa enables the achievement of SOT-induced deterministic magnetization switching. Moreover, we quantitatively determine the SOT efficiency in the bilayer. Furthermore, we design an L1_(0)-MnAl/B2-CoGa/Co_(2)MnGa structure to achieve field-free magnetic switching. Our results provide valuable insights for achieving high-performance SOT-MRAM devices based on L1_(0)-MnAl alloy.

Deep Reinforcement Learning or Lyapunov Analysis?A Preliminary Comparative Study on Event-Triggered Optimal Control

Dear Editor,This letter develops a novel method to implement event-triggered optimal control(ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning(DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions.

Effective extraction of polyribosomes from astrocytes enables future discoveries on translation regulation

Translation regulation is an important layer of gene expression:Generation of genome-wide expression datasets at multi-omics levels in spatial,temporal,and cell-type resolution is essential for deciphering brain complexity.Regulation of gene expression is a highly dynamic process aiming at the production of precise levels of gene products to guarantee optimal cellular function,in response to physiological cues.Speedy advances in next-generation sequencing enabled the understanding of epigenomic and transcriptomic dynamic landscapes of different brain regions along development,aging,and disease progression.However,the correlation of the“transcriptome”with protein levels is poor because numerous mRNAs are subjected to manipulation of their translation efficiency,to warrant a favorable result under certain conditions.Hence,it is widely accepted that regulation at the translation level is a vital layer of gene expression.Quantification of actively translated mRNA populations(i.e.,“translatome”)is a more reliable predictor of the“proteome”(Wang et al.,2020).

How to manage the malposition of deep vein catheterization into the artery?

Central venous catheterization establishes temporary,efficient,and rapid use of deep venous access in patients,which provides high flow rate fluid perfusion,enables measurement of central venous pressure,and acts as an important reference for clinical decision-making.However,various complications such as pneumothorax,hemothorax,hematoma,and puncture failure can easily occur during the puncture and catheterization process.

Building mm Wave on the Evolving C-V2X:mm Wave NR-V2X

Since the evolving standardization of cellular V2X(C-V2X)technologies is continuously developed by 3GPP,research on new radio(NR-V2X)has been on schedule by academic and industries.Though millimeter wave(mm Wave)frequency band is supposed to provide large transmission bandwidth,yet the development of mm Wave band in NR-V2X is still in preliminary stage.In this article,a comprehensive discussion of mm Wave NR-V2X is given covering trends,standardization landscape,and enabling technologies,aiming at tackling the challenge of channel modeling,directional channel access,beamforming and blockage management.The vision of mm Wave NR-V2X is to fully support the development of automated driving,holographic control display and improved in-car infotainment for the future.

Global AI Governance:Realistic Difficulties,Mode Selection,and Chinese Solutions

Today,global AI governance is still in the exploratory stage,and the complex nature and uncertainty of technology require global collaboration.THE rapid advancement of artificial intelligence(AI)technology has undoubtedly become the center of global attention in recent years,especially generative AI technology.This technology is rapidly shaping the trends of digital society,while its risks are also spreading,thus making it imperative to strengthen global AI governance so as to enable effective risk control.

The COVID-19 vaccination campaign in Bhutan: strategy and enablers

Bhutan has reported a total of 2596 COVID-19 cases and three deaths as of September 15,2021.With support from India,the United States,Denmark,the People’s Republic of China,Croatia and other countries,Bhutan was able to conduct two rounds of nationwide vaccination campaign.While many countries struggle to overcome vaccine refusal or hesitancy due to complacency,a lack of trust,inconvenience and fear,escalated in some countries by anti-vaccine groups,Bhutan managed to inoculate more than 95%of its eligible populations in two rounds of vaccination campaign.Enabling factors of this successful vaccination campaign were strong national leadership,a well-coordinated national preparedness plan,and high acceptability of vaccine due to effective mass communication and social engagement led by religious figures,volunteers and local leaders.In this short report,we described the national strategic plan and enabling factors that led to the success of this historical vaccination campaign.

A multilevel process view of organizational knowledge transfer: enablers versus barriers

The importance of knowledge as a strategic asset for organizations has been recognized by both researchers and practitioners.To gain a competitive advantage,firms are required to effectively manage their knowledge resources.The most central activity in managing knowledge is to ensure its transfer within and between organizations.Knowledge transfer(KT)has thus been recognized as a key component of the knowledge management processes.The purpose of this research is to provide a holistic view of the KT barriers and enablers within an organization,from a multilevel and process-based perspectives.We first review the extant literature to identify the key enablers and barriers to KT.Second,we develop a multilevel conceptualization of enablers and barriers that can influence KT at different levels–individual,team/exchange and organization.The proposed model improves current understanding of KT by offering a holistic and integrated view of enablers and barriers.

CoRE:Constrained Robustness Evaluation of Machine Learning-Based Stability Assessment for Power Systems

Dear Editor,Machine learning(ML) approaches have been widely employed to enable real-time ML-based stability assessment(MLSA) of largescale automated electricity grids. However, the vulnerability of MLSA to malicious cyber-attacks may lead to wrong decisions in operating the physical grid if its resilience properties are not well understood before deployment. Unlike adversarial ML in prior domains such as image processing, specific constraints of power systems that the attacker must obey in constructing adversarial samples require new research on MLSA vulnerability analysis for power systems.

Spin-polarized electron beam generation in the colliding-pulse injection scheme

Employing colliding-pulse injection has been shown to enable the generation of high-quality electron beams from laser-plasma accelerators.Here,by using test particle simulations,Hamiltonian analysis,and multidimensional particle-in-cell simulations,we lay the theoretical framework for spin-polarized electron beam generation in the colliding-pulse injection scheme.Furthermore,we show that this scheme enables the production of quasi-monoenergetic electron beams in excess of 80%polarization and tens of pC charge with commercial 10-TW-class laser systems.

Special issue on machine learning-driven big data and blockchain techniques for communication

Guest Editorial Currently,the rapid development of storage technologies combined with some potential factors such as mobile networks,Internet of Things(IoT),cloud computing and the emergence of new technologies pose some problems for big data processing and blockchain security in the communication domain.Moreover,the complexity of network security and data processing has increased dramatically,making it more difficult and challenging to solve various problems in the communication domain.Therefore,Machine Learning(ML)algorithms have been proposed to process big data and enhance blockchain security and further enable intelligent analysis in the communication domain.

Health Phys.Abstracts,Volume 123,Number 5

Practical Tools for Patient-specific Characterization and Dosimetry of Radiopharmaceutical Extravasation Extravasation during radiopharmaceutical injection may occur with a frequency of more than 10%.In these cases,radioactivity remains within tissue and deposits unintended radiation dose.Characterization of extravasations is a necessary step in accurate dosimetry,but a lack of free and publicly available tools hampers routine standardized analysis.Our objective was to improve existing extravasation characterization and dosimetry methods and to create and validate tools to facilitate standardized practical dosimetric analysis in clinical settings.Using Monte Carlo simulations,we calculated dosimetric values for sixteen nuclear medicine isotopes:^(11 )C,^(64) Cu,^(18) F,^(67 )Ga,^(68) Ga,^(123) I,^(131) I,111 In,177 Lu,13 N,15 O,82 Rb,153 Sm,89 Sr,99m Tc,and 90 Y.We validated our simulation results against five logical alternative dose assessment methods.We then created three new characterization tools:a worksheet,a spreadsheet,and a web application.We assessed each tool by recalculating extravasation dosimetry results found in the literature and used each of the tools for patient cases to show clinical practicality.Average variation between our simulation results and alternative methods was 3.1%.Recalculation of published dosimetry results indicated an average error of 7.9%.Time required to use each characterization tool ranged from 1 to 5 min,and agreement between the three tools was favorable.We improved upon existing methods by creating new tools for characterization and dosimetry of radiopharmaceutical extravasation.These free and publicly available tools will enable standardized routine clinical analysis and benefit patient care,clinical follow-up,documentation,and event reporting.

Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems.Moreover,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers.Solve the privacy problem.The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes.It can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s disease.In addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices.The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.

Massimo D'Alema,Former Prime Minister of Italy&President of the Italianieuropei Foundation

What do we mean when we talk about development that places people and nations at its centre?First and foremost,we want economic growth to enable every woman,man,and child to have access to the basic,common goods for a dignified life.We want to reduce the intolerable social distances and inequalities that produce unjust and socially unsustainable societies.We want to ensure harmony between economic growth and the protection of the natural environment.For ten years,the Belt and Road Initiative has worked in this direction.