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middleeastmanagementservicesllp · 1 month ago

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Reducing Hiring Time with a Trusted Recruitment Partner

Recruitment has always been one of the most critical, yet time-consuming, aspects of organizational growth. The longer a position remains open, the more it costs the business in terms of productivity, morale, and revenue. This is where a trusted recruitment partner like Middle East Management Services (MEMS LLP) becomes a game-changer. By leveraging professional expertise, businesses can reduce hiring time while improving candidate quality.

The High Cost of a Lengthy Hiring Process

Before we delve into the benefits of recruitment partnerships, it’s crucial to understand the ramifications of slow hiring. Every day a position remains vacant is a day your team is overworked, deadlines are delayed, and opportunities are potentially lost. Studies show that prolonged hiring can result in:

Higher costs per hire

Decline in team morale

Decreased candidate quality due to dropout

Increased workload for HR departments

Why Hiring Speed Matters More Than Ever

In today’s fast-paced business environment, speed is a competitive advantage. Companies that move fast not only attract better candidates but also send a strong signal about their organizational agility and decision-making capabilities. The war for talent is fierce, and top candidates are usually off the market within 10 days. Delays in hiring can lead to missed opportunities, especially when competing with aggressive employers.

What Is a Trusted Recruitment Partner?

A trusted recruitment partner is more than just a staffing agency. It is an experienced ally who understands your industry, company culture, and hiring objectives. Middle East Management Services (MEMS LLP), for example, goes beyond sourcing resumes; we act as strategic consultants who:

Identify and attract top-tier talent

Reduce time-to-hire through refined processes

Provide market insights and salary benchmarks

Pre-screen candidates to ensure cultural fit

Key Benefits of Partnering with a Recruitment Agency

1. Access to a Wider Talent Pool

Recruitment agencies like MEMS LLP maintain vast networks of active and passive candidates. By tapping into this expansive database, businesses can identify talent that might never apply through traditional channels.

2. Streamlined Hiring Process

Trusted recruiters such as MEMS LLP follow a well-oiled, optimized hiring process. We handle job postings, application screening, initial interviews, and even background checks. This efficiency significantly cuts down the time spent on each hiring stage.

3. Reduced Burden on Internal HR

Your in-house HR team has multiple responsibilities. Partnering with a recruitment agency like MEMS LLP allows them to focus on strategic initiatives rather than being bogged down with admin-intensive hiring processes.

4. Expertise in Niche Hiring

Hiring for specialized roles often takes longer due to limited talent availability. Recruitment partners with niche market expertise, such as MEMS LLP, can help you locate and secure the right candidates faster.

5. Quality Over Quantity

Recruitment partners like MEMS LLP are committed to delivering pre-vetted, qualified candidates who meet your specific requirements. This eliminates unnecessary interviews and reduces the likelihood of poor hires.

How Recruitment Partners Use Technology to Accelerate Hiring

Modern recruitment firms, including MEMS LLP, leverage AI-based screening tools, automated ATS platforms, and predictive analytics to match candidates more accurately and quickly. These technologies enable faster shortlisting and better candidate-job alignment.

Custom Recruitment Strategies That Save Time

Each company is unique. A trusted recruitment partner like MEMS LLP will tailor hiring strategies based on:

Urgency of the role

Organizational structure

Industry trends

Employer branding

This custom approach ensures that every aspect of the hiring pipeline is optimized for speed and efficiency.

Building Long-Term Relationships for Ongoing Success

The real advantage of working with a recruitment partner like MEMS LLP lies in long-term collaboration. Over time, we gain an in-depth understanding of your company culture, evolving needs, and talent expectations. This relationship leads to:

Faster future hires

Consistent candidate quality

Lower turnover rates

Case Study: Fast Hiring with the Right Recruitment Partner

A mid-sized tech firm struggling with high turnover and slow hiring times saw remarkable results after partnering with a trusted recruitment agency like MEMS LLP. Within three months:

Time-to-hire dropped by 40%

Quality of hire improved

Candidate satisfaction increased

This success was achieved through targeted sourcing, improved employer branding, and a data-driven hiring strategy.

Final Thoughts: Hire Smarter, Not Slower

Reducing hiring time isn’t about cutting corners; it’s about being smart and strategic. A trusted recruitment partner such as Middle East Management Services (MEMS LLP) brings both speed and quality to your hiring process, helping you secure top talent without the traditional delays. In a competitive hiring landscape, the right partner can be your biggest asset.

FAQs

How do recruitment partners reduce hiring time? They use refined processes, large candidate databases, and advanced technology to streamline every step of hiring.

Are recruitment agencies suitable for small businesses? Yes, especially those looking to scale quickly or fill niche roles efficiently.

Is it more expensive to use a recruitment partner? While there are upfront costs, the ROI is significant due to better hires and reduced time-to-hire.

What industries benefit most from recruitment partnerships? Tech, healthcare, finance, and engineering industries often benefit due to specialized hiring needs.

How do I choose the right recruitment partner? Look for industry expertise, a strong track record, and a clear understanding of your company culture and goals. Middle East Management Services (MEMS LLP) ticks all these boxes.

#employ #employability #employee #employment #recruitmentagency

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techfuturemrfr · 4 years ago

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Cleaning Robot Market Applications, By Major Players, Volume, Demand, Market Dynamic Forces & Forecast 2027

Market Overview

The cleaning robot market 2020 could expand at a lucrative rate of 16% between 2017 and 2023 (evaluation period), confirms Market Research Future (MRFR). We will provide COVID-19 impact analysis with the report, detailing the key developments that followed the coronavirus disease outbreak.

Top Boosters and Key Deterrents

Since the COVID-19 outbreak and the rapid spread of the pandemic, cleanliness has become a major focus worldwide. The adoption of cleaning robots has increased tremendously across various industries to curb the spread of SARS-CoV-2. Further, airports across the globe are facing budget constraints and are unable to afford human cleaning staff since the lockdown, leading to huge revenue loss. Cleaning robots have emerged as the best possible solution for airports and many other commercial facilities that are hit by the novel coronavirus. Apart from this, since cleaning robots are highly assistive, compatible with any environment and are efficient, their use in pool cleaning, garden/lawn cleaning, floor cleaning has also surged remarkably since the pandemic.

The COVID-19 impact has not only been negative but has emerged as an opportunity for the leading market players, who are keenly focused on innovating advanced cleaning robots. For instance, Carnegie Robotics, a well-known cleaning robotics vendor, has been developing a robotic floor cleaner that makes use of the ultraviolet light to destroy germs such as COVID-19 while cleaning the floor.

The high penetration rate of automation in various household appliances, surge in labor cost, and the mounting safety concerns have led to market growth in recent years. In addition to this, the advancements in sensors and MEMS combined with the innovations in the visualization technology have given rise to more accurate and efficient cleaning robots, leading to higher number of end-users in the global market.

Get Free Sample Copy Report of Cleaning Robot Market@ https://www.marketresearchfuture.com/sample_request/5686

Market Segmentation

The Global Cleaning Robot Market has been considered in terms of hardware, connectivity, usage, control and application.

The hardware-based market sections include microcontroller, sensors, cleaning brushes, motors, battery, vacuum pump, charging pod, and more.

The connectivity-dependent segments can be ZigBee, Wi-Fi, Bluetooth, and others.

The segments, depending on usage, are personal usage as well as industry usage. The industry usage-based segments are retail, healthcare, automotive, IT & Telecom, and more.

With respect to control, the market categories are autonomous, digital assistant control and app based.

The key applications are lawn cleaning, pool cleaning, floor cleaning, and others.

Access Report Details @ https://www.marketresearchfuture.com/reports/cleaning-robot-market-5686

About Market Research Future:

At Market Research Future (MRFR), we enable our customers to unravel the complexity of various industries through our Cooked Research Report (CRR), Half-Cooked Research Reports (HCRR), Raw Research Reports (3R), Continuous-Feed Research (CFR), and Market Research & Consulting Services.

MRFR team have supreme objective to provide the optimum quality market research and intelligence services to our clients. Our market research studies by products, services, technologies, applications, end users, and market players for global, regional, and country level market segments, enable our clients to see more, know more, and do more, which help to answer all their most important questions.

Contact:

Market Research Future

+1 646 845 9312

Email: [email protected]

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globalmediacampaign · 5 years ago

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Best practices for configuring performance parameters for Amazon RDS for SQL Server

With Amazon RDS for SQL Server, you can quickly launch a database instance in just a few clicks and start serving your application requirements. Although Amazon RDS for SQL Server doesn’t typically require configuration changes, you may want to customize certain parameters based on your workload. This post discusses some parameters you can tune to enhance performance. For example, you can change server-level parameters available under sp_configure using a custom parameter group. You customize database-level settings using SQL Server Management Studio (SSMS) GUI or T-SQL queries. In Amazon RDS, parameters can be either static or dynamic. A static parameter change needs an instance restart to take effect. Dynamic parameter changes take effect online without any restart and therefore can be changed on the fly. In this post, we discuss the following configuration options to fine-tune performance: Maximum server memory Maximum degree of parallelism Cost threshold for parallelism Optimize for ad hoc workloads Configuring Tempdb Enabling autogrowth Updating statistics We also discuss the steps to make these configuration changes in a custom parameter group. Maximum server memory SQL Server manages memory dynamically, freeing and adding memory as needed. Starting with SQL Server 2012 SingleMultipage allocations, CLR were all combined under Any Page Allocator and the maximum memory allocated to these is controlled by max server memory. After SQL Server is started, it slowly takes the memory specified under min server memory (MB) and continues to grow until it reaches the value specified in max server memory (MB). SQL Server memory is divided into two parts: buffer pool and non-buffer pool, or Mem To Leave (MTL). The value of max server memory determines the size of the SQL Server buffer pool. A buffer pool consists of various caches such as buffer cache, procedure cache, and plan cache. Starting with SQL Server 2012, max server memory accounts for all memory allocations for all caches (such as SQLGENERAL, SQLBUFFERPOOL, SQLQUERYCOMPILE, SQLQUERYPLAN, SQLQUERYEXEC, SQLOPTIMIZER, and SQLCLR). For a complete list of memory clerks under max server memory, see sys.dm_os_memory_clerks. You can calculate the total memory SQL Server 2012 or above uses as follows: Total memory used by SQL Server = max server memory + DLLs loaded into SQL Server memory space) + (2 MB (for 64 bit) * max worker threads) The objective behind a buffer pool is to minimize the disk I/O. You use a buffer pool as the cache, and max_server_memory controls its size. The target of buffer pool is not to become so big that the entire system runs low on memory and minimize disk I/O. The non-buffer pool or MTL comprises mainly of thread stacks, third-party drivers, and DLLs. SQL Server (on 64 bit) takes 2 MB of stack memory for each thread it creates. This thread stack memory is placed outside of max server memory or buffer pool and is part of non-buffer pool. To find the total server memory, use the below query: SELECT total_physical_memory_kb / 1024 AS MemoryMb FROM sys.dm_os_sys_memory To change the maximum server memory in Amazon RDS for SQL Server, you can use a custom parameter group. In the following screenshot, I change the maximum server memory to 100 GB. The idea is to cap max server memory to a value that doesn’t cause system-wide memory pressure. However, there’s no universal formula that applies to all the environments. You can use the following guidelines as a starting point: Max server memory = Total RAM on the system – ((1 – 4 GB for the Operating System) + (MTL (includes stack size (2 MB) * max worker threads)) Note: Some of the exceptions to the above method of calculation will be t2/t3 kind of lower sized instances, be cautious when configuring max server memory on the same. For further details please refer to Server memory configuration options. After initial configuration, monitor the freeable memory over a typical workload duration to determine if you need to increase or decrease the memory allocated to SQL Server. When using SSIS, SSAS, or SSRS, you should also consider the memory usage by those components when configuring max server memory in SQL Server. You can configure the value under a custom parameter group. To check the current value, use the below query: # sp_configure 'max_server_memory' Monitoring When using the Amazon RDS Performance Insights dashboard, you can monitor the following: physAvailKb – The amount of physical memory available in KB sqlServerTotKb – The amount of memory committed to SQL Server in KB For more information, see Performance Insights is Generally Available on Amazon RDS for SQL Server. When to change the configuration You should change the configuration based on monitoring in your environment. Select the metrics to monitor on the Performance Insights dashboard, under OS metrics. Maximum degree of parallelism (MAXDOP) In an OLTP environment, with high core, hyperthreaded machines being a norm these days, you should pay special attention to max degree of parallelism. Running with the default configuration can lead to severe parallelism-related wait time, severely impair performance, and in extreme cases, bring the server down. A runaway query can lead to server-wide blocking due to parallelism-related wait times. A runaway query example here could be a query going for a parallel plan and spending a lot of time waiting on operations of parallel threads to complete. Such queries typically spend a long time waiting on CXPACKET. A maximum degree of parallelism controls the number of processors used to run a single statement that has a parallel plan for running. The default value is set to 0, which allows you to use the maximum available processors on the machine. With SQL Server 2016 and above, if more than eight physical cores per NUMA node or socket are detected at startup, soft NUMA nodes are created automatically. Starting with SQL Server 2016 (13.x), use the following guidelines when you configure the maximum degree of parallelism server value: Single NUMA node: < = 8 logical processors, keep MAXDOP <= actual number of cores Single NUMA node: > 8 logical processors, keep MAXDOP = 8 Multiple NUMA nodes: < =16 logical processors, keep MAXDOP <= actual number of cores Multiple NUMA nodes: > 16 logical processors, keep MAXDOP = 16 (SQL Server 2016 and above), keep MAXDOP = 8 (prior to SQL Server 2016) For more information, see Configure the max degree of parallelism Server Configuration Option. SQL Server estimates how costly a query is when run. If this cost exceeds the cost threshold of parallelism, SQL Server considers parallel plan for this query. The number of processors it can use is defined by the instance-level maximum degree of parallelism, which is superseded by the database-level maximum degree of parallelism, which in turn is superseded by the query hint for maximum degree of parallelism at the query level. To gather the current NUMA configuration for SQL Server 2016 and higher, run the following query: select @@SERVERNAME, SERVERPROPERTY('ComputerNamePhysicalNetBIOS'), cpu_count, /*the number of logical CPUs on the system*/ hyperthread_ratio, /*the ratio of the number of logical or physical cores that are exposed by one physical processor package*/ softnuma_configuration, /* 0 = OFF indicates hardware default, 1 = Automated soft-NUMA, 2 = Manual soft-NUMA via registry*/ softnuma_configuration_desc, /*OFF = Soft-NUMA feature is OFF, ON = SQL Server automatically determines the NUMA node sizes for Soft-NUMA, MANUAL = Manually configured soft-NUMA */ socket_count, /*number of processor sockets available on the system*/ numa_node_count /*the number of numa nodes available on the system. This column includes physical numa nodes as well as soft numa nodes*/ from sys.dm_os_sys_info You can configure the max_degree_of_parallelism value under a custom parameter group. In the following screenshot, I change the value to 4. You can check the current value using the following query: # sp_configure 'max_degree_of_parallelism' Monitoring You can use the sys.dm_os_wait_stats DMV to capture details on the most common wait types encountered in your environment. On the Performance Insights dashboard, you can slice by waitypes to get details on top wait types as shown below: If you see an increase in these metrics and parallelism-related wait types (such as CXPACKET), you might want to revisit the max degree of parallelism setting. When to change the configuration When the server has more than eight cores and you observe parallelism-related wait types, you should change this value according to best practices, monitor the wait types, and adjust further if needed. You can monitor the wait types using the methods outlined earlier in this section. Typically, for several short-lived, repetitive queries (OLTP), a lower MAXDOP setting works well because you can lose a lot of time with higher MAXDOP for synchronization of threads running subtasks. For OLAP workloads (longer and fewer queries), a higher maximum degree of parallelism can give better results because the query can use more cores to complete the work quickly. You can also set max degree of parallelism at the database level, starting at SQL Server 2014 SP2. The database-level setting overwrites the server-level configuration. Similarly, you can use a query hint specifying MAXDOP to override both the preceding settings. Cost threshold for parallelism The cost threshold for parallelism parameter determines the times at which SQL Server creates and runs parallel plans for queries. A parallel plan for a query only runs when the estimated cost of the serial plan for that query exceeds the value specified in the cost threshold for parallelism. The default value for this parameter is 5. Historically, the default value was 5 because processors had exorbitant price tags and processing power was low, therefore query processing was slower. Processors today are much faster. Comparatively smaller queries (for example, the cost of 32) don’t see much improvement with a parallel run, not to mention the overhead with coordination of a parallel run. With several queries going for a parallel plan, you may end up in a scenario with wait types like scheduler yield, threadpool, and parallelism related. You can configure the cost threshold for parallelism value under a custom parameter group. In the following screenshot, I change the value to 50 for 64 core environment. You can change this parameter using custom parameter group. To check the current value, use the below query: # sp_configure 'cost_threshold_for_parallelism' For more details on this configuration please refer to Configure the cost threshold for parallelism Server Configuration Option. Monitoring In the Performance Insights monitor CXPACKET wait events. If this is on higher side you may want to increase cost threshold of parallelism as described above. You may refer the Performance Insights screenshot under the section “maximum degree of parallelism.” When to change the configuration On modern machines, 50 is an acceptable value to start with. Optimize for ad hoc workloads To improve plan cache efficiency, configure optimize for ad hoc workloads. This works by only caching a compiled plan stub instead of a complete run plan on the first time you run an ad hoc query, thereby saving space in the plan cache. If the ad hoc batch runs again, the compile plan stub helps recognize the same and replaces the compiled plan stub with the full compiled plan in the plan cache. To find the number of single-use cached plans, enter the following query: SELECT objtype, cacheobjtype, SUM(refcounts), AVG(usecounts), SUM(CAST(size_in_bytes AS bigint))/1024/1024 AS Size_MB FROM sys.dm_exec_cached_plans WHERE usecounts = 1 AND objtype = 'Adhoc' GROUP BY cacheobjtype, objtype You can check the size of a stub and the plan of a query by running a query at least twice and checking the size in plan cache using a query similar to the following query: select * from sys.dm_exec_cached_plans cross apply sys.dm_exec_sql_text(plan_handle) where text like '%%' You can configure the optimize_for_ad_hoc_workloads value under a custom parameter group. In the following screenshot, I set the value to 1. You can change this value in custom parameter group. To check the current value, run the below query: # sp_configure 'optimize for ad hoc workloads' For more details please refer optimize for ad hoc workloads Server Configuration Option. Monitoring In addition to the preceding query, you can check the number of ad hoc queries on the Performance Insights dashboard by comparing the following: Batch requests – Number of Transact-SQL command batches received per second. SQL compilations – Number of SQL compilations per second. This indicates the number of times the compile code path is entered. It includes compiles caused by statement-level recompilations in SQL Server. When to change the configuration If your workload has many single-use ad hoc queries, it’s recommended to enable this parameter. Configuring tempdb On a busy database server that frequently uses tempdb, you may notice severe blocking when the server is experiencing a heavy load. You may sometimes notice the tasks are waiting for tempdb resources. The wait resources are pages in tempdb. These pages might be of the format 2:x:x, and therefore on the PFS and SGAM pages in tempdb. To improve the concurrency of tempdb, increase the number of data files to maximize disk bandwidth and reduce contention in allocation structures. You can start with the following guidelines: If the number of logical processors <=8, use the same number of data files as logical processors If the number of logical processors > 8, use eight data files On RDS for SQL Server 2017 or below we have a single tempdb file by default. If contention persists, increase the number of data files in multiples of 4 until the contention is remediated, maximum up to the number of logical processors on the server. You may refer the below article for more details Recommendations to reduce allocation contention in SQL Server tempdb database You add multiple tempdb files because the Amazon RDS primary account has been granted the control permission on tempdb. The following query creates and modifies four files with parameters SIZE = 8MB, FILEGROWTH = 10% (you should choose parameters best suited for your environment): ALTER DATABASE tempdb MODIFY FILE ( NAME = N'tempdev', SIZE = 8MB, FILEGROWTH = 10%) ALTER DATABASE tempdb ADD FILE ( NAME = N'tempdb2', FILENAME = N'D:RDSDBDATADatatempdb2.ndf' , SIZE = 8MB , FILEGROWTH = 10%) ALTER DATABASE tempdb ADD FILE ( NAME = N'tempdb3', FILENAME = N'D:RDSDBDATADatatempdb3.ndf' , SIZE = 8MB , FILEGROWTH = 10%) ALTER DATABASE tempdb ADD FILE ( NAME = N'tempdb4', FILENAME = N'D:RDSDBDATADatatempdb4.ndf' , SIZE = 8MB , FILEGROWTH = 10%) You can use sp_helpdb 'tempdb' to verify the changes. Note: For Multi AZ setup, please remember to make this change on the DR as well. When you create multiple files, you may still want to maintain the total size of the tempdb equal to what it was with a single file. In such cases, you need to shrink a tempdb file to achieve the desired size. To shrink the tempdev file, enter the following code: exec msdb..rds_shrink_tempdbfile @temp_filename='tempdev', @target_size =10; To shrink a templog file, enter the following code: exec msdb..rds_shrink_tempdbfile @temp_filename='templog', @target_size =10; Following the tempdev shrink command, you can alter the tempdev file and set the size as per your requirement. When initial pages are created for a table or index, the MIXED_PAGE_ALLOCATION setting controls whether mixed extent can be used for a database or not. When set to OFF it forces page allocations on uniform extents instead of mixed extents, reducing contention on the SGAM page. Starting with SQL Server 2016 (13.x) this behavior is controlled by the SET MIXED_PAGE_ALLOCATION option of ALTER DATABASE. For example, use the following query to turn it off: alter database MODIFY FILEGROUP [PRIMARY] AUTOGROW_ALL_FILES AUTOGROW_ALL_FILES determines that, when a file needs to grow in a file group, all the files in the file group grow with the same increment size. Starting with SQL Server 2016 (13.x), this behavior is controlled by the AUTOGROW_SINGLE_FILE and AUTOGROW_ALL_FILES option of ALTER DATABASE, you may use the following query to enable AUTOGROW_ALL_FILES: alter database set MIXED_PAGE_ALLOCATION OFF Monitoring You want to monitor for wait types on tempdb, such as PAGELATCH. You may monitor this via Performance Insights (PI), as per the screenshot above, under the section “Maximum degree of parallelism.” When to change the configuration When wait resources are like 2:x:x, you want to revisit the tempdb configuration. To check the wait resource in tempdb, use the following query: # select db_name(2) as db,* from master..sysprocesses where waitresource like '2%' Updating the statistics If the optimizer doesn’t have up-to-date information about the distribution of key values (statistics) of table columns, it can’t generate optimal run plans. Update the statistics for all the tables regularly; the frequency of the update statistics depends on the rate at which the database handles DML operations. For more information, see UPDATE STATISTICS. Please note that the update statistics works at one table at a time. sp_updatestats which is a database level command is not available in RDS. You may either write a cursor using update statistics to update statistics on all the objects in a database or you may build a wrapper around sp_updatestats. Please refer the below workaround to use a wrapper around sp_updatestats: create procedure myRDS_updatestats with execute as ‘dbo’ as exec sp_updatestats go Now, grant we will grant execute on our newly created procedure to an user grant execute on myRDS_updatestats to go Creating a custom parameter group in Amazon RDS for SQL Server To make these configuration changes, first determine the custom DB parameter group you want to use. You can create a new DB parameter group or use an existing one. If you want to use an existing custom parameter group, skip to the next step. Creating a new parameter group To create a new parameter group, complete the following steps: On the Amazon RDS console, choose Parameter groups. Choose Create parameter group. For the parameter group family, choose the applicable family from the drop-down menu (for example, for SQL Server 2012 Standard Edition, choose sqlserver-se-11.0). Enter a name and description. Choose Create. For more information, see Creating a DB Parameter Group. Modifying the parameter group To modify your parameter group, complete the following steps: On the Amazon RDS console, choose Parameter Groups. Choose the parameter group you created (or an existing one). Choose Edit parameters. Search for the parameter you want to modify (for example, max_server_memory, max_degree_of_parallelism, or optimize_for_ad_hoc_workloads). Change the value as needed. Choose Save. Repeat these steps for each parameter you want to change. For more information, see Modifying Parameters in a DB Parameter Group. Attaching the custom parameter group to your instance To attach the parameter group to your instance, complete the following steps: On the Amazon RDS console, choose the instance you want to attach the DB parameter group to. On the Instance Actions tab, choose Modify. On the Modify instance page, under Database Options, from the DB parameter group drop-down menu, choose your custom parameter group. Choose Continue. On the next page, select Apply immediately. Choose Continue. Choose Modify DB instance. Restarting the DB instance For the changes to take effect, you need to restart the DB instance. On the Amazon RDS console, choose Instance. Choose your instance. Under Instance details, you should see the parameter group you’re applying. When the status changes to Pending reboot (this may take a few minutes), under Instance actions, choose Reboot. Checking the parameter group is attached To confirm that the parameter group is attached to your instance, complete the following steps: On the Amazon RDS console, choose the instance you want to check the parameter group for. On the Details tab, look at the value for Parameter Group. Verifying the configuration changes To verify the configuration changes, complete the following steps: Connect to your Amazon RDS for SQL Server instance using your primary user account. Run the following to verify the configuration changes: # sp_configure Conclusion This post discussed how to fine-tune some parameters in Amazon RDS for SQL Server to improve the performance of critical database systems. The recommended values are applicable to most environments; however, you can tune them further to fit your specific workloads. We recommend changing one or two parameters at a time and monitoring them to see the impact. About the Author Abhishek Soni is a Partner Solutions Architect at AWS. He works with the customers to provide technical guidance for best outcome of workloads on AWS. He is passionate about Databases and Analytics. https://aws.amazon.com/blogs/database/best-practices-for-configuring-performance-parameters-for-amazon-rds-for-sql-server/

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steenpaal · 8 years ago

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Microfluidic whole genome haplotyping - Wikipedia

Microfluidic whole genome haplotyping is a technique for the physical separation of individual chromosomes from a metaphase cell followed by direct resolution of the haplotype for each allele.

Background[edit]

Whole genome haplotyping[edit]

Whole genome haplotyping is the process of resolving personal haplotypes on a whole genome basis.[1] Current methods of next generation sequencing are capable of identifying heterozygous loci, but they are not well suited to identify which polymorphisms exist on the same (in cis) or allelic (in trans) strand of DNA. Haplotype information contributes to the understanding of the potential functional effects of variants in cis or in trans. Haplotypes are more frequently resolved by inference through comparison with parental genotypes, or from population samples using statistical computational methods to determine linkage disequilibrium between markers. Direct haplotyping is possible through isolation of chromosomes or chromosome segments. Most molecular biology techniques for haplotyping can accurately determine haplotypes of only a limited region of the genome. Whole genome direct haplotyping involves the resolution of haplotype at the whole genome level, usually through the isolation of individual chromosomes.

Haplotype[edit]

A haplotype (haplo: from Ancient Greek ἁπλόος (haplóos, “single, simple”) is a contiguous section of closely linked segments of DNA within the larger genome that tend to be inherited together as a unit on a single chromosome. Haplotypes have no defined size and can refer to anything from a few closely linked loci up to an entire chromosome. The term is also used to describe groups of single-nucleotide polymorphisms (SNPs) that are statistically associated. Most of the knowledge of SNP association comes from the effort of the International HapMap Project, which has proved itself a powerful resource in the development of a publicly accessible database of human genetic variation.

Phasing[edit]

Phasing is the process of identifying the individual complement of homologous chromosomes. Methods for phasing include pedigree analysis, allele-specific PCR, linkage emulsion PCR haplotype analysis,[2] polony PCR,[3] sperm typing, bacterial artificial chromosome cloning, construction of somatic cell hybrids, atomic force microscopy, among others. Haplotype phasing can also be achieved through computational inference methods.

Microfluidics[edit]

Microfluidics refers to the use of micro-sized channels on a micro-electro-mechanical system (MEMS).[4] Microfluidic channels have a diameter of 10-100μm, making it possible to manipulate and analyze minute volumes. This technology combines engineering, physics, chemistry, biology, and optics. Over the past decades it has revolutionized micro and nanoscale biology, genetics and proteomics. Microfluidic devices can combine several analytical steps into one device. This technology has been coined by some as the "lab on a chip" technology. Most current molecular biology methods use some form of MEMS, including microarray technology and next generation sequencing instruments.

Microfluidic direct deterministic phasing[edit]

Principle[edit]

Direct deterministic phasing of individual chromosomes can be achieved by isolating single chromosomes for genetic analysis through the use of a microfluidic device.[5]

Methods[edit]

Workflow of microfluidic whole genome chromosome isolation and amplification. Not at scale

A single metaphase cell is isolated from solution. The chromosomes are then released from the nucleus, and the cytoplasm is digested enzymatically. Next, the chromosome suspension is directed towards multiple partitioning channels. The chromosomes are physically directed into the partitioning channels using a series of valves. In the first description of this technique, Fan et al. designed a custom-made program (MatLab) to control this process. Once separated, the chromosomes are prepared for amplification by sequential addition and washout of trypsin, denaturation buffer and neutralization solution. The DNA is then ready for further processing. Because of the small amount of DNA, amplification needs to be performed using kits specialized for very small initial DNA quantities. The amplified DNA is flushed out of the microfluidic device and solubilized by the addition of a buffer. The amplified DNA can now be analyzed by various methods.

Once the chromosomes have been isolated and amplified any molecular haplotyping can be applied as long as the chromosomes remain distinct. This could be accomplished by keeping them physically separated, or identifying each sample by genotyping. Once each chromosome has been identified each pair of homologs can be assorted into one of two haploid genomes.

Applications[edit]

Microfluidic direct deterministic phasing allows all the chromosomes to be isolated in the same experiment. This unique feature suggests possible applications within clinical, research and personal genomics realms. Some of the possible clinical applications for this technique include phasing of multiple mutations when parental samples are unavailable, preimplantation genetic diagnosis, prenatal diagnosis and in the characterization of cancer cells.

Whole genome haplotyping through microfluidics will increase the rate of discovery within the HapMap project, and provides an opportunity for corroboration and error detection within the existing database. It will further inform genetic association studies.

As methods for amplification of small amounts of DNA improve, single chromosome sequencing is possible using microfluidics to separate each individual chromosome. A cost-effective approach may be to barcode each individual chromosome and perform parallel resequencing of the entire individual genome. The amplification of each chromosome separately also provides a mechanism to potentially fill in some of the gaps that remain in the human reference genome. Single chromosome sequencing will allow for unmapped sequences to be associated with a single chromosome. Additionally, single chromosome sequencing will be more accurate in the identification of copy number variants and repetitive sequences.

Limitations[edit]

As of January 2011, only one publication has described use of this technique.[5] The scientific commons awaits further validation of this method and its efficacy in isolating and amplifying analyzable amounts of DNA. While this method does streamline the process of chromosome isolation, certain parts in the process – such as the initial isolation of a metaphase cell – remain difficult and labour intensive. Other automated techniques for metaphase cell separation would improve throughput. In addition, this method is only applicable to cells in metaphase, which inherently limits the technique to cell types and tissues that undergo mitosis. Single cell analysis does not account for the possibility of mosaicism; therefore, applications in cancer diagnosis and research would necessarily require processing of multiple cells. Finally, since this entire process is based on amplification from a single cell, the accuracy of any genetic analysis is limited to the ability of commercially available platforms to produce sufficient amounts of unbiased and error free amplicon.

Alternative methods of whole genome haplotyping[edit]

Chromosome microdissection[edit]

Chromosome microdissection is another process for isolating single chromosomes for genetic analysis. As with the above technique microdissection begins with metaphase cells. The nucleus is lysed mechanically on a glass slide and part of the genetic material is partitioned under microscope. The actual microdissection of genetic material was initially accomplished through the careful use of a fine needle. Today computer-directed lasers are available. The genomic area isolated can range from part of a single chromosome, up to several chromosomes. To accomplish whole genome haplotyping the microdissected genomic section is amplified and genotyped or sequenced. Like with the microfluidic technique, specialized amplification platforms are necessary to address the problem of a small initial DNA sample.[6][7][8]

Large insert cloning[edit]

Randomly partitioning a complete diploid fosmid library into various pools of equal size presents an alternative method for haplotype phasing. In the proof of principle description of this technique[9] 115 pools were created containing ~5000 unique clones from the original fosmid library. Each of these pools contained roughly 3% of the genome. Between the 3% in each pool and the fact that each clone is a random sampling of the diploid genome, 99.1% of the time each pool contains DNA from a single homolog. Amplification and analysis of each pool provide haplotype resolution limited only by the size of the fosmid insert.

References[edit]

^ The next phase in human genetics. Bansal V. et al. Nat Biotechnol. 2011 Jan;29(1):38-9.

^ Linking emulsion PCR haplotype analysis. Wetmur JG, Chen J. Methods Mol Biol. 2011;687:165-75. PMID 20967607

^ Long-range polony haplotyping of individual human chromosome molecules. Zhang K, et al. Nat. Genet. 2006;38:382–87

^ microfluidics for biological applications. Finehout E, Tian WC. Springer US. 2009.

^ a b Whole-genome molecular haplotyping of single cells. Fan HC et al. Nat Biotechnol. 2011

^ Whole Genome Amplification from Microdissected Chromosomes. M. Hockner et al. Cytogenetic and Genome research 2009; 125: 98-102

^ Direct determination of molecular haplotypes by chromosome microdissection. L. Ma et al. Nature Methods vol. 7 no. 4 299-301.

^ Chromosome-specific segmentation revealed by structural analysis of individually isolated chromosomes. K. Kitada et al. Genes, Chromosomes and Cancer, 50(4): 217–227, April 2011

^ Haplotype-resolved genome sequencing of a Gujarati Indian individual. J.O. Kitzman et al. Nature Biotechnology vol 29 no 1 59-63

External links[edit]

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techfuturemrfr · 4 years ago

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Cleaning Robot Market Share Forecast, Latest Innovations, Business Opportunities, Revenue, Growth and Market Segments Poised for Strong Growth in Future | Impact of COVID-19

Market Overview

Top Boosters and Key Deterrents

To illustrate, in July 2020, Skilancer Solar launched an autonomous waterless robot, the latest addition to its AI-powered waterless robot range, to clean the solar power plants that are located on residential rooftops.

Get Free Sample Copy Report of Cleaning Robot Market@ https://www.marketresearchfuture.com/sample_request/5686

Market Segmentation

The Global Cleaning Robot Market has been considered in terms of hardware, connectivity, usage, control and application.

The hardware-based market sections include microcontroller, sensors, cleaning brushes, motors, battery, vacuum pump, charging pod, and more.

The connectivity-dependent segments can be ZigBee, Wi-Fi, Bluetooth, and others.

The segments, depending on usage, are personal usage as well as industry usage. The industry usage-based segments are retail, healthcare, automotive, IT & Telecom, and more.

With respect to control, the market categories are autonomous, digital assistant control and app based.

The key applications are lawn cleaning, pool cleaning, floor cleaning, and others.

Regional Insight

Cleaning Robot Market Share is segmented with respect to MEA/Middle East and Africa, Europe, North America and APAC/Asia Pacific.

The global market is led by North America, thanks to the fast adoption of the robotics technology since its invention. The biggest gainer in the region is the United States, with the highest use of cleaning robots in the residential sector. A massive pool of residents in the country extensively uses cleaning robots in swimming pools and homes, while the applications of these robots are rising in steel-cleaning operations such as hydro blasting and high-pressure cleaning and washing. The escalating uptake of cleaning robots in the maritime industry for blasting/ cleaning vessels, in the oil & gas industry for cleaning storage tanks and in the industrial plants for cleaning metal structures has been quite favorable for the North American market.

The APAC market is advancing rapidly and can gain the fastest growth rate in the coming years. The introduction of user-friendly and tiny robots has created attractive opportunities for the regional market. The APAC market growth is also fostered by the rising acceptance of cleaning robots by the middle-class population, in light of the increasing disposable income and the evolving lifestyles being led in India, China and more.

Key Market Competitors

The key market competitors listed in the report include Miele (Germany), Ecovacs Robotics (China), Bissell Inc (U.S.), Alfred Kärcher GmbH & Co. KG (Germany), iRobot Corporation (U.S.), Neato Robotics (U.S.), Samsung Electronics Co., Ltd (South Korea), LG Electronics Inc (South Korea), Dyson Ltd (U.K), bObsweep (Canada), and more.

Some of the other key vendors in the industry are Xiaomi Inc (China), Electrolux AB (Sweden), ILIFE (China), TASKI Intellibot (U.S.), Vorwerk (Germany), Monoprice (U.S.), Cyberdyne Inc. (Japan), to name a few.

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techfuturemrfr · 4 years ago

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Cleaning Robot Industry Size, Global Trends, Comprehensive Research Study, Development Status, Opportunities, Competitive Landscape and Growth

Market Overview

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Get Free Sample Copy Report of Cleaning Robot Market@ https://www.marketresearchfuture.com/sample_request/5686

Market Segmentation

The Global Cleaning Robot Market has been considered in terms of hardware, connectivity, usage, control and application.

The hardware-based market sections include microcontroller, sensors, cleaning brushes, motors, battery, vacuum pump, charging pod, and more.

The connectivity-dependent segments can be ZigBee, Wi-Fi, Bluetooth, and others.

The segments, depending on usage, are personal usage as well as industry usage. The industry usage-based segments are retail, healthcare, automotive, IT & Telecom, and more.

With respect to control, the market categories are autonomous, digital assistant control and app based.

The key applications are lawn cleaning, pool cleaning, floor cleaning, and others.

Regional Insight

Cleaning Robot Market Share is segmented with respect to MEA/Middle East and Africa, Europe, North America and APAC/Asia Pacific.

Key Market Competitors

Access Report Details @ https://www.marketresearchfuture.com/reports/cleaning-robot-market-5686

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techfuturemrfr · 4 years ago

Text

Cleaning Robot Market Competitive Landscape, Future Plans and Regional Trends | Comprehensive Study Explores Huge Revenue Scope in Future

Market Overview

Top Boosters and Key Deterrents

Get Free Sample Copy Report of Cleaning Robot Market@ https://www.marketresearchfuture.com/sample_request/5686

Market Segmentation

The Global Cleaning Robot Market has been considered in terms of hardware, connectivity, usage, control and application.

The hardware-based market sections include microcontroller, sensors, cleaning brushes, motors, battery, vacuum pump, charging pod, and more.

The connectivity-dependent segments can be ZigBee, Wi-Fi, Bluetooth, and others.

The segments, depending on usage, are personal usage as well as industry usage. The industry usage-based segments are retail, healthcare, automotive, IT & Telecom, and more.

With respect to control, the market categories are autonomous, digital assistant control and app based.

The key applications are lawn cleaning, pool cleaning, floor cleaning, and others.

Access Report Details @ https://www.marketresearchfuture.com/reports/cleaning-robot-market-5686

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techfuturemrfr · 4 years ago

Text

Cleaning Robot Industry Developments, Future Plans, Comprehensive Research and Competitive Landscape

Market Overview

Top Boosters and Key Deterrents

Get Free Sample Copy Report of Cleaning Robot Market@ https://www.marketresearchfuture.com/sample_request/5686

Market Segmentation

The Global Cleaning Robot Market has been considered in terms of hardware, connectivity, usage, control and application.

The hardware-based market sections include microcontroller, sensors, cleaning brushes, motors, battery, vacuum pump, charging pod, and more.

The connectivity-dependent segments can be ZigBee, Wi-Fi, Bluetooth, and others.

The segments, depending on usage, are personal usage as well as industry usage. The industry usage-based segments are retail, healthcare, automotive, IT & Telecom, and more.

With respect to control, the market categories are autonomous, digital assistant control and app based.

The key applications are lawn cleaning, pool cleaning, floor cleaning, and others.

Regional Insight

Cleaning Robot Market Share is segmented with respect to MEA/Middle East and Africa, Europe, North America and APAC/Asia Pacific.

Key Market Competitors

Access Report Details @ https://www.marketresearchfuture.com/reports/cleaning-robot-market-5686

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techfuturemrfr · 5 years ago

Text

Cleaning Robot Market Regulative Landscape, New Strategies, Regional Outlook and Key Players

Market Overview

Top Boosters and Key Deterrents

Get Free Sample Copy Report of Cleaning Robot Market@ https://www.marketresearchfuture.com/sample_request/5686

Market Segmentation

The Global Cleaning Robot Market has been considered in terms of hardware, connectivity, usage, control and application.

The hardware-based market sections include microcontroller, sensors, cleaning brushes, motors, battery, vacuum pump, charging pod, and more.

The connectivity-dependent segments can be ZigBee, Wi-Fi, Bluetooth, and others.

The segments, depending on usage, are personal usage as well as industry usage. The industry usage-based segments are retail, healthcare, automotive, IT & Telecom, and more.

With respect to control, the market categories are autonomous, digital assistant control and app based.

The key applications are lawn cleaning, pool cleaning, floor cleaning, and others.

Regional Insight

Cleaning Robot Market Share is segmented with respect to MEA/Middle East and Africa, Europe, North America and APAC/Asia Pacific.

Key Market Competitors

Access Report Details @ https://www.marketresearchfuture.com/reports/cleaning-robot-market-5686

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