By default, your iPhone automatically connects to known wi-fi networks. (To stop an iPhone from automatically connecting, you can tell your iPhone to forget a wi-fi network.) But what happens if you take your iPhone to a new location? You'll need to manually connect your iPhone to a wi-fi network.

That's a hassle. But if you have the foresight and inclination, you can save yourself time in the future by making your iPhone ask to join wi-fi networks when no known networks are available. Instead of having to open settings to join a network, you'll be able to easily select a network from an on-screen prompt.

Here's how to make your iPhone ask to join wi-fi networks:

1. From the home screen, tap Settings.
   
   
2. Tap Wi-Fi. The window shown below appears.
   
   
3. Move the Ask to Join Networks slider to the On position.
   
   
4. The next time you're in a location with no known networks, your iPhone will prompt you to connect to an available wi-fi network, as shown below.
   
   

In the future, this prompt will be displayed when no known networks are available. (To actually see the prompt, you'll need to do something that requires network access, like try to check your email or open a webpage.) To connect to a wi-fi network, select a network and enter a password, if one is required.

Related Articles

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Meet Your Macinstructor

Matt Cone, the author of Master Your Mac, has been a Mac user for over 20 years. A former ghost writer for some of Apple's most notable instructors, Cone founded Macinstruct in 1999, a site with OS X tutorials that boasts hundreds of thousands of unique visitors per month. You can email him at: matt@macinstruct.com.

Seer Enters Into a Co-Marketing and Sales Agreement With Thermo Fisher Scientific  Technology Networks

New approaches to unleashing and mobilizing the potential of female leadership and the role of men as partners in achieving gender equality were the focuses of the International Forum of Women Leaders "Equal Opportunities for a Better Future", co-organized by the OSCE Office for Democratic Institutions and Human Rights (ODIHR) in Minsk on 21 and 22 June 2016.

More than 100 participants from 21 OSCE participating States, including representatives from all of the Commonwealth of Independent States (CIS) countries, discussed the possibilities for creating networks for women’s empowerment, explored ways of institutionalizing gender analysis, and outlined good practices for promoting women’s roles and influence in politics. A Minsk Declaration of Women Leaders was also adopted, acknowledging the main obstacles in reaching gender equality and calling for action to challenge the status quo.

The forum was co-organized with Belarusian State University, Council of Europe Information Point in Minsk, the Executive Committee of the Commonwealth of Independent States, United Nations Development Programme (UNDP) in Belarus, United Nations Population Fund (UNFPA) in Belarus, and Raoul Wallenberg Institute of Human Rights and Humanitarian Law.

"All actors, from political parties to governmental structures, should think about how they can involve women and create gender-sensitive platforms where women can substantively contribute on an equal footing with men," said Marcin Walecki, Head of ODIHR’s Democratization Department.

Elena Shamal, a Member of the House of Representatives of Belarus National Assembly, said: "The 2015 Global Gender Gap Report of the World Economic Forum points out that there is not a single state in the world that could provide absolute gender equality. Today’s Forum has once again underlined the need to strengthen international, state and public co-operation for further promoting women’s participation in political and public life."

Nadezda Shvedova, of the Russian Academy of Science, said: "To achieve gender equality, we need to enhance co-operation in the OSCE area and the CIS region, in particular. We are calling for the establishment of regional networks of women leaders and women’s organizations to further advance women’s participation in political and public life, for the benefit of all."

This forum was organized as part of ODIHR’s programme to advance women’s political participation in the OSCE region, and with the support of the Belarus Ministries of Labour and Social Protection and of Foreign Affairs.

Related Stories

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• OSCE/ODIHR final report on early parliamentary elections in Serbia recommends comprehensive review of legislation and ensuring the level playing field for contestants
• OSCE/ODIHR event in Kyiv supports dialogue platform in Verkhovna Rada on parliamentary ethics

Having a bit of a problem with my laptop lately, and thought I’d write up the problem in case it’s affecting anyone else:

So my MacBook Air (mid-2009, OS X 10.8.3) When my computer wakes from sleep, it doesn’t immediately reconnect to my wireless network. What’s more, if I open up the wireless menu in OS X’s menu bar, it doesn’t show any networks nearby. Zip. Zero. Zilch. It’ll scan for new networks repeatedly, but won’t see a single one.

But here’s where this gets really, really annoying: if I open the Network panel in System Preferences, all nearby networks are immediately visible without delay.

Given the weird inconsistency between the two menus, and that I can reproduce this issue consistently, I figure this is a bug: either with 10.8.3, or with my aging little laptop. Either way, I’d love to fix it. So if you’ve come across this problem and know how a workaround, suggestions via email or Twitter would be most welcome.

Update: Charles Gaudette suggested on Twitter that it might be a couple , and pointed me toward a page showing how to clear out corrupted plist files. Deleting the com.apple.network.identification.plist and com.apple.airport.preferences.plist files seems to have done the trick—thanks, Charles! And thanks to everyone else who wrote in or twittered suggestions at me.

World Trade Center is a global network of office complexes that has so far connected 330 cities in 100 countries across the world. It is managed and regulated by renowned World Trade Center Association...

Emerging Markets and Developing Economies (EMDE) have been driving the growth in electricity grid length with rapid expansion from 2001, on increasing electrification. China leads the incremental transmission network put up in the last decade while India leads on the distribution side. India leads in Technical grid losses in T&D when compared with other countries. High-Voltage Direct Current (HVDC) transmission lines, thus, emerge as the most preferred transmission lines considering their lower losses and their suitability for long-distance power transfer

Netbur Networks es especialista en el desarrollo integral de aplicaciones web, en un proceso que engloba: Diseño gráfico. Diseño de páginas web. Maquetación profesional. Programación HTML, DHTML, Javascript, PHP, Servlets, Java.... Administración BB.DD. SQL Server, Oracle, MySQL. Alta y posicionamiento en buscadores.

NetworkSleuth is a fast file search tool, that allows search for files located on local or network computers and supports searches for documents, image, MP3, music and video files, allows to search for files in Local Area Network(LAN) based on various criteria.

(Telecompaper) Ziply Fiber has agreed to acquire the Pacific Northwest assets of Unite Private Networks (UPN) from owner Cox Communications, for an undisclosed amount...

(Telecompaper) Canadian vendor Vecima Networks has announced the successful completion of a first-phase linear ad insertion deployment for US fibre and IPTV carrier Hotwire Communications. The first phase introduces linear parity ad insertion, enabling zonal ad placements in IPTV streams. In the next phase,...

What difference would it make to be able to unlock ocean data at scale? How would deploying hundreds of marine sensing platforms improve marine weather predictability and accuracy? A company named Sofar is answering some of those questions these days due to their capacity to use real-time data to improve ... [continued]

The post How Networks Of Ocean Sensors Can Improve Marine Weather Predictability appeared first on CleanTechnica.

Small and Medium Enterprises (SMEs) face new challenges in the global market as customers require more complete and flexible solutions and continue to drastically reduce the number of suppliers. SMEs are trying to address these challenges through cooperation within collaborative enterprise networks (CENs). Human aspects constitute a fundamental issue in these networks as people, as opposed to organizations or Information Technology (IT) systems, cooperate. Since there is a lack of empirical studies on the role of human factors in IT-supported collaborative enterprise networks, this paper addresses the major human aspects encountered in this type of organization. These human aspects include trust issues, knowledge and know-how sharing, coordination and planning activities, and communication and mutual understanding, as well as their influence on the business processes of CENs supported by IT tools. This paper empirically proves that these aspects constitute key factors for the success or the failure of CENs. Two case studies performed on two different CENs in Switzerland are presented and the roles of human factors are identified with respect to the IT support systems. Results show that specific human factors, namely trust and communication and mutual understanding have to be well addressed in order to design and develop adequate software solutions for CENs.

This paper presents a sequential edge swap (SQES) mechanism to design a robust network for a healthcare system utilising energy and communication range of nodes. Two operations: sequential degree difference operation (SQDDO) and sequential angle sum operation (SQASO) are performed to enhance the robustness of network. With equivalent degrees of nodes from the network's centre to its periphery, these operations build a robust network structure. Disaster attacks that have a substantial impact on the network are carried out using the network information. To identify a link between the malicious and disaster attacks, the Pearson coefficient is employed. SQES creates a robust network structure as a single objective optimisation solution by changing the connections of nodes based on the positive correlation of these attacks. SQES beats the current methods, according to simulation results. When compared to hill-climbing algorithm, simulated annealing, and ROSE, respectively, the robustness of SQES is improved by roughly 26%, 19% and 12%.

TCP BBR is one of the most powerful congestion control algorithms. In this article, we provide a comprehensive review of BBR analysis, expanding on existing knowledge across various fronts. Utilising ns3 simulations, we evaluate BBR's performance under diverse conditions, generating graphical representations. Our findings reveal flaws in the probe's RTT phase duration estimation and unequal bandwidth sharing between BBR and CUBIC protocols. Specifically, we demonstrated that the probe's RTT phase duration estimation algorithm is flawed and that BBR and CUBIC generally do not share bandwidth equally. Towards the end of the article, we propose a new improved version of TCP BBR which minimises these problems of inequity in bandwidth sharing and corrects the inaccuracies of the two key parameters RTprop and cwnd. Consequently, the BBR' protocol maintains very good fairness with the Cubic protocol, with an index that is almost equal to 0.98, and an equity index over 0.95.

The supply chains of the Canadian forest products industry are largely dependent on accurate demand forecasts. The USA is the major export market for the Canadian forest products industry, although some Canadian provinces are also exporting forest products to other global markets. However, it is very difficult for each province to develop accurate demand forecasts, given the number of factors determining the demand of the forest products in the global markets. We develop multi-layer feed-forward artificial neural network (ANN) models for demand forecasting of the Canadian forest products industry. We find that the ANN models have lower prediction errors and higher threshold statistics as compared to that of the traditional models for predicting the demand of the Canadian forest products. Accurate future demand forecasts will not only help in improving the short-term profitability of the Canadian forest products industry, but also their long-term competitiveness in the global markets.

To better explore the application of unsupervised learning neural networks in psychological interventions for college students, this study investigates the relationships among latent psychological variables from the perspective of neural networks. Firstly, college students' psychological crisis and intervention systems are analysed, identifying several shortcomings in traditional psychological interventions, such as a lack of knowledge dissemination and imperfect management systems. Secondly, employing the Human-Computer Interaction (HCI) approach, a structural equation model is constructed for unsupervised learning neural networks. Finally, this study further confirms the effectiveness of unsupervised learning neural networks in psychological interventions for college students. The results indicate that in psychological intervention for college students. Additionally, the weightings of the indicators at the criterion level are calculated to be 0.35, 0.27, 0.19, 0.11 and 0.1. Based on the results of HCI, an emergency response system for college students' psychological crises is established, and several intervention measures are proposed.

A hybrid network system for mitigating errors due to jamming attacks in cognitive radio networks (CRNs) is named LeNet deep stacked autoencoder (LDSAE) and is developed. In this exploration, the sensing stage and decision-making are considered. The sensing unit is composed of four steps. First, the detected signal is forwarded to filtering progression. Here, BPF is utilised to filter the detected signal. The filtered signal is squared in the second phase. Third, signal samples are combined and jamming attacks occur by including false energy levels. Last, the attack is maliciously affecting the FC decision in the fourth step. On the other hand, FC initiated the decision-making and also recognised jamming attacks that affect the link amidst PU and SN in decision-making stage and it is accomplished by employing LDSAE-based trust model where the proposed module differentiates the malicious and selfish users. The analytic measures of LDSAE gained 79.40%, 79.90%, and 78.40%.

The purpose of the study is to identify influential factors in the use of collaborative networks within the context of manufacturing. The study aims to investigate factors that influence employees’ learning, and to bridge the gap between theory and praxis in collaborative networks in manufacturing. The study further extends the boundary of a collaborative network beyond enterprises to include suppliers, customers, and external stakeholders. It provides a holistic perspective of collaborative networks within the complexity of the manufacturing environment, based on empirical evidence from a questionnaire survey of 246 respondents from diverse manufacturing industries. Drawing upon the socio-technical systems (STS) theory, the study presents the theoretical context and interpretations through the lens of manufacturing. The results show significant influences of organizational support, promotive interactions, positive interdependence, internal-external learning, perceived effectiveness, and perceived usefulness on the use of collaborative networks among manufacturing employees. The study offers a basis of empirical validity for measuring collaborative networks in organizational learning and knowledge/information sharing in manufacturing.

Aim/Purpose: The understanding of disease spread dynamics in the context of air travel is crucial for effective disease detection and epidemic intelligence. The Susceptible-Exposed-Infectious-Recovered-Hospitalized-Critical-Deaths (SEIR-HCD) model proposed in this research work is identified as a valuable tool for capturing the complex dynamics of disease transmission, healthcare demands, and mortality rates during epidemics. Background: The spread of viral diseases is a major problem for public health services all over the world. Understanding how diseases spread is important in order to take the right steps to stop them. In epidemiology, the SIS, SIR, and SEIR models have been used to mimic and study how diseases spread in groups of people. Methodology: This research focuses on the integration of air traffic network data into the SEIR-HCD model to enhance the understanding of disease spread in air travel settings. By incorporating air traffic data, the model considers the role of travel patterns and connectivity in disease dissemination, enabling the identification of high-risk routes, airports, and regions. Contribution: This research contributes to the field of epidemiology by enhancing our understanding of disease spread dynamics through the application of the SIS, SIR, and SEIR-HCD models. The findings provide insights into the factors influencing disease transmission, allowing for the development of effective strategies for disease control and prevention. Findings: The interplay between local outbreaks and global disease dissemination through air travel is empirically explored. The model can be further used for the evaluation of the effectiveness of surveillance and early detection measures at airports and transportation hubs. The proposed research contributes to proactive and evidence-based strategies for disease prevention and control, offering insights into the impact of air travel on disease transmission and supporting public health interventions in air traffic networks. Recommendations for Practitioners: Government intervention can be studied during difficult times which plays as a moderating variable that can enhance or hinder the efficacy of epidemic intelligence efforts within air traffic networks. Expert collaboration from various fields, including epidemiology, aviation, data science, and public health with an interdisciplinary approach can provide a more comprehensive understanding of the disease spread dynamics in air traffic networks. Recommendation for Researchers: Researchers can collaborate with international health organizations and authorities to share their research findings and contribute to a global understanding of disease spread in air traffic networks. Impact on Society: This research has significant implications for society. By providing a deeper understanding of disease spread dynamics, it enables policymakers, public health officials, and practitioners to make informed decisions to mitigate disease outbreaks. The recommendations derived from this research can aid in the development of effective strategies to control and prevent the spread of infectious diseases, ultimately leading to improved public health outcomes and reduced societal disruptions. Future Research: Practitioners of the research can contribute more effectively to disease outbreaks within the context of air traffic networks, ultimately helping to protect public health and global travel. By considering air traffic patterns, the SEIR-HCD model contributes to more accurate modeling and prediction of disease outbreaks, aiding in the development of proactive and evidence-based strategies to manage and mitigate the impact of infectious diseases in the context of air travel.

Aim/Purpose: This paper aims to empirically quantify the financial distress caused by the COVID-19 pandemic on companies listed on Amman Stock Exchange (ASE). The paper also aims to identify the most important predictors of financial distress pre- and mid-pandemic. Background: The COVID-19 pandemic has had a huge toll, not only on human lives but also on many businesses. This provided the impetus to assess the impact of the pandemic on the financial status of Jordanian companies. Methodology: The initial sample comprised 165 companies, which was cleansed and reduced to 84 companies as per data availability. Financial data pertaining to the 84 companies were collected over a two-year period, 2019 and 2020, to empirically quantify the impact of the pandemic on companies in the dataset. Two approaches were employed. The first approach involved using Multiple Discriminant Analysis (MDA) based on Altman’s (1968) model to obtain the Z-score of each company over the investigation period. The second approach involved developing models using Artificial Neural Networks (ANNs) with 15 standard financial ratios to find out the most important variables in predicting financial distress and create an accurate Financial Distress Prediction (FDP) model. Contribution: This research contributes by providing a better understanding of how financial distress predictors perform during dynamic and risky times. The research confirmed that in spite of the negative impact of COVID-19 on the financial health of companies, the main predictors of financial distress remained relatively steadfast. This indicates that standard financial distress predictors can be regarded as being impervious to extraneous financial and/or health calamities. Findings: Results using MDA indicated that more than 63% of companies in the dataset have a lower Z-score in 2020 when compared to 2019. There was also an 8% increase in distressed companies in 2020, and around 6% of companies came to be no longer healthy. As for the models built using ANNs, results show that the most important variable in predicting financial distress is the Return on Capital. The predictive accuracy for the 2019 and 2020 models measured using the area under the Receiver Operating Characteristic (ROC) graph was 87.5% and 97.6%, respectively. Recommendations for Practitioners: Decision makers and top management are encouraged to focus on the identified highly liquid ratios to make thoughtful decisions and initiate preemptive actions to avoid organizational failure. Recommendation for Researchers: This research can be considered a stepping stone to investigating the impact of COVID-19 on the financial status of companies. Researchers are recommended to replicate the methods used in this research across various business sectors to understand the financial dynamics of companies during uncertain times. Impact on Society: Stakeholders in Jordanian-listed companies should concentrate on the list of most important predictors of financial distress as presented in this study. Future Research: Future research may focus on expanding the scope of this study by including other geographical locations to check for the generalisability of the results. Future research may also include post-COVID-19 data to check for changes in results.

Understanding of social network structure and user behavior has important implications for site design, applications (e.g., ad placement policies), accurate modeling for social studies, and design of next-generation infrastructure and content distribution systems. Currently, characterizations of social networks have been dominated by topological studies in which graph representations are analyzed in terms of connectivity using techniques such as degree distribution, diameter, average degree, clustering coefficient, average path length, and cycles. The problem is that these parameters are not completely satisfactory in the sense that they cannot account for individual events and have only limited use, since one can produce a set of synthetic graphs that have the exact same metrics or statistics but exhibit fundamentally different connectivity structures. In such an approach, a node drawn as a small circle represents an individual. A small circle reflects a black box model in which the interior of the node is blocked from view. This paper focuses on the node level by considering the structural interiority of a node to provide a more fine-grained understanding of social networks. Node interiors are modeled by use of six generic stages: creation, release, transfer, arrival, acceptance, and processing of the artifacts that flow among and within nodes. The resulting description portrays nodes as comprising mostly creators (e.g., of data), receivers/senders (e.g., bus boys), and processors (re-formatters). Two sample online social networks are analyzed according to these features of nodes. This examination points to the viability of the representational method for characterization of social networks.

Recently published the GEO Handbook on Biodiversity Observation Networks presents a powerful resource that will provide valuable guidance to those committed to protecting, sustaining and preserving biodiversity across the planet. The practical experience which GEO BON has accumulated through its own actions, and through the efforts of its network partners, is a valuable resource to biodiversity information systems everywhere—from those just starting out in places where there has previously been little information, to large operations holding vii enormous amounts of data and wishing to know how better to use it. 

The Group on Earth Observations (GEO) is a voluntary international partnership of 102 governments and 92 participating organisations which share a vision of a future in which decisions and actions for the benefit of humankind are informed by coordinated, comprehensive and sustained Earth observations. GEO achieves its mission largely through self-organising communities focused on important Earth observation domains where decision-making will benefit from data that is shared broadly and openly. These communities form connected systems and networks, creating a Global Earth Observation System of Systems (GEOSS).

During its first ten-year implementation period, 2005–2015, GEO identified biodiversity as a key ‘Societal Benefit Area’, resulting in the formation of the GEO Biodiversity Observation Network, GEO BON. As GEO moves into its second, ten-year implementation period, GEO BON is recognised as one of its strongest communities. It has helped to mobilise and coordinate the data and information needed for an effective response to the global threats faced by organisms, species and ecosystems. In collaboration with international treaty bodies such as the Convention on Biological Diversity (CBD) and the Ramsar Convention on Wetlands of International Importance, GEO BON has worked with national conservation agencies and non-governmental organisations at scales from regional to global. These efforts have revealed both the benefits of working together and the challenges of such a complex, but urgent task, not least of which is filling the remaining large gaps in data and information. 

European Geosciences Union
General Assembly 2015
Vienna | Austria | 12 – 17 April 2015

http://www.egu2015.eu/home.html

One Workshop partiicluarly relevant for EU BON: ESSI2.17 Aggregation, consolidation and coordination of Earth observation networks. Harmonization and gaps

Convener: Joan Masó
Co-Convener: Ivette Serral

Abstract
We are investing in many efforts in creating pan-European or global EO thematic networks but are managed independently and coordination between them is limited. Europe is investing in the Sentinel constellation an at the same time, several initiatives are setting out to create, maintain and operationalize networks of in-situ sensors. These observation networks are usually conceived with a specific purpose in mind (e.g., air quality monitoring in the main cities or coastal water contamination), and they often lack a general coverage, are scattered irregularly in the territory, and sometimes are removed when the measurement campaign ends. There is a need for integrating systems and coordinating them more efficiently, explore synergies and make progress in harmonized and extend them.
Some initiatives aim to coordinate several themes into a single observation set. This is the case of the Critical Zone Exploration (the Earth’s outer layer from vegetation canopy to the soil and groundwater that sustains human life). The CZEN (Critical Zone Exploration Network; http://www.czen.org) is a network of field sites investigating processes within the Critical Zone.

This session is asking for presentations on the coordination between observation network examples and solutions to overcome technical and political barriers that help to reduce the cost and increase value by combining and sharing structures. Papers discussing gaps or redundancies in the current Earth observation networks are also welcome.

http://meetingorganizer.copernicus.org/EGU2015/session/18560

Eagle Eye Networks, the global leader in cloud video surveillance, is previewing Eagle Eye Camera Direct Complete, as well as showcasing the newly enhanced Enterprise Edition and new artificial intelligence (AI)-powered tools for enterprise businesses at ISC West 2023.

Ascent Capital Group announced that its primary operating subsidiary, Monitronics International Inc., established a deal to acquire Security Networks for a total transaction price of $507.5 million.

Using generative AI, video snapshot images are transformed into a searchable database, allowing users to find key operational issues by simply speaking commands.

This new offering provides commercial customers with affordable access to professional body camera services, which improve staff safety and accountability, trigger immediate response and provide valuable evidence, all while protecting assets and keeping communities safe.

The Eagle Eye Cloud VMS true cloud video surveillance platform is designed to safeguard businesses, while delivering cybersecurity assurance, business intelligence, flexibility and scalability. 

The integration brings the distinct advantages of cloud, including greatly simplified installation, robust end-to-end cybersecurity, centralized management of multiple locations, and AI-filtering to dramatically reduce false-positive activity to the monitoring center or command center professional.

This collaboration expands Comcast Smart Solutions’ existing video analytics expertise while giving clients more flexibility to choose the right smart video solution, according to the announcement.

The shift toward automation has radically changed the infrastructure requirements for food manufacturers.

Children's Geographies; 01/13/2022
(AN 154620403); ISSN: 14733285
Academic Search Premier

Health Care Provider Boot Camp Day 7: Certified Workers’ Compensation Health Care Networks

The hydro­thermal synthesis and structural characterization of four novel 3D pillared-layer metal–organic frameworks are studied, revealing how the malleability of copper coordination geometries drives diverse supramolecular isomerism. The findings provide new insights into designing advanced hybrid materials with tailored properties, emphasizing the significant role of reaction conditions and metal ion flexibility in determining network topologies.

The well-known copper car­box­yl­ate dimer, with four car­box­yl­ate ligands ex­ten­ding outwards towards the corners of a square, has been employed to generate a series of crystalline com­pounds. In particular, this work centres on the use of the 4-hy­droxy­benzoate anion (Hhba−) and its deprotonated phe­nol­ate form 4-oxidobenzoate (hba2−) to obtain complexes with the general formula [Cu2(Hhba)4–x(hba)xL2–y]x−, where L is an axial coligand (including solvent mol­ecules), x = 0, 1 or 2, and y = 0 or 1. In some cases, short hy­dro­gen bonds result in complexes which may be represented as [Cu2(Hhba)2(H0.5hba)2L2]−. The main focus of the investigation is on the formation of a variety of extended networks through hy­dro­gen bonding and, in some crystals, coordinate bonds when bridging coligands (L) are employed. Crystals of [Cu2(Hhba)4(di­ox­ane)2]·4(di­ox­ane) consist of the expected Cu dimer with the Hhba− anions forming hy­dro­gen bonds to 1,4-di­ox­ane mol­ecules which block network formation. In the case of crystals of com­position [Et4N][Cu2(Hhba)2(H0.5hba)2(CH3OH)(H2O)]·2(di­ox­ane), Li[Cu2(Hhba)2(H0.5hba)2(H2O)2]·3(di­ox­ane)·4H2O and [Cu2(Hhba)2(H0.5hba)2(H0.5DABCO)2]·3CH3OH (DABCO is 1,4-di­aza­bicyclo­[2.2.2]octa­ne), square-grid hy­dro­gen-bonded networks are generated in which the complex serves as one type of 4-con­necting node, whilst a second 4-con­necting node is a hy­dro­gen-bonding motif assembled from four phenol/phenolate groups. Another two-dimensional (2D) network based upon a related square-grid structure is formed in the case of [Et4N]2[Cu2(Hhba)2(hba)2(di­ox­ane)2][Cu2(Hhba)4(di­ox­ane)(H2O)]·CH3OH. In [Cu2(Hhba)4(H2O)2]·2(Et4NNO3), a square-grid structure is again apparent, but, in this case, a pair of nitrate anions, along with four phenolic groups and a pair of water mol­ecules, combine to form a second type of 4-con­necting node. When 1,8-bis­(di­methyl­amino)­naphthalene (bdn, `proton sponge') is used as a base, another square-grid network is generated, i.e. [Hbdn]2[Cu2(Hhba)2(hba)2(H2O)2]·3(di­ox­ane)·H2O, but with only the copper dimer complex serving as a 4-con­necting node. Complex three-dimensional networks are formed in [Cu2(Hhba)4(O-bipy)]·H2O and [Cu2(Hhba)4(O-bipy)2]·2(di­ox­ane), where the potentially bridging 4,4'-bi­pyridine N,N'-dioxide (O-bipy) ligand is employed. Rare cases of mixed car­box­yl­ate copper dimer complexes were obtained in the cases of [Cu2(Hhba)3(OAc)(di­ox­ane)]·3.5(di­ox­ane) and [Cu2(Hhba)2(OAc)2(DABCO)2]·10(di­ox­ane), with each structure possessing a 2D network structure. The final com­pound re­por­ted is a simple hy­dro­gen-bonded chain of com­position (H0.5DABCO)(H1.5hba), formed from the reaction of H2hba and DABCO.

The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X-ray free-electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real-time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python-based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics-based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis.

Recent developments in synchrotron radiation facilities have increased the amount of data generated during acquisitions considerably, requiring fast and efficient data processing techniques. Here, the application of dense neural networks (DNNs) to data treatment of X-ray diffraction computed tomography (XRD-CT) experiments is presented. Processing involves mapping the phases in a tomographic slice by predicting the phase fraction in each individual pixel. DNNs were trained on sets of calculated XRD patterns generated using a Python algorithm developed in-house. An initial Rietveld refinement of the tomographic slice sum pattern provides additional information (peak widths and integrated intensities for each phase) to improve the generation of simulated patterns and make them closer to real data. A grid search was used to optimize the network architecture and demonstrated that a single fully connected dense layer was sufficient to accurately determine phase proportions. This DNN was used on the XRD-CT acquisition of a mock-up and a historical sample of highly heterogeneous multi-layered decoration of a late medieval statue, called `applied brocade'. The phase maps predicted by the DNN were in good agreement with other methods, such as non-negative matrix factorization and serial Rietveld refinements performed with TOPAS, and outperformed them in terms of speed and efficiency. The method was evaluated by regenerating experimental patterns from predictions and using the R-weighted profile as the agreement factor. This assessment allowed us to confirm the accuracy of the results.

The number of IoT devices (bots) engaged in botnet-driven DDoS attacks rose from around 200,000 a year ago to approximately 1 million devices, generating more than 40% of all DDoS traffic today, according to the report.