Claims:
1. A content optimizing system (200) implemented in a small screen device (102), the content optimizing system (200) comprising:
a hardware processor; and
a machine-readable storage medium storing instructions that, when executed by the hardware processor, cause the hardware processor to:
retrieve long form content associated with a large screen device (104);
parse the long form content;
configure the long form content into an array of arrays of numbers;
tag each array of the long form content through a learning ensemble;
recombine the tagged arrays;
classify the recombined arrays into pre-set content layouts;
store the recombined arrays as small form content optimized for the small screen device (102).
2. The system (200) as claimed in claim 1, wherein the long form content includes any or a combination of plurality of long sentences and media.
3. The system (200) as claimed in claim 2, wherein each array of the arrays of numbers represents a sentence of the plurality of long sentences, and each element of the array represents an attribute associated with content of the sentence.
4. The system (200) as claimed in claim 2, wherein, based upon configuring, each sentence of the plurality of sentences is converted into a vector of values representing parameters related to the sentence.
5. The system (200) as claimed in claim 2, wherein each sentence is converted into a collection of a series of numbers representing attributes of a sentence, wherein the visual hierarchy and visual attributes of the long form content, as viewed by humans, is taken into account, by associating each sentence with numbers representing the visual elements like font sizes, color, font-weight and number of words.
6. The system (200) as claimed in claim 2, wherein, based upon configuring, the media associated with the long form content is uploaded in a server (106).
7. The system (200) as claimed in claim 2, wherein the recombined arrays being classified into pre-set content layouts includes the media retrieved from the server (106).
8. The system (200) as claimed in claim 1, wherein the long form content is classified into a plurality of classes.
9. The system (200) as claimed in claim 1, wherein the pre-set content layouts are a collection of pre-generated layouts suitable for the small screen device (102) viewing.
10. A content optimizing method (500) comprising:
retrieving long form content associated with a large screen device (104);
parsing the long form content;
configuring the long form content into an array of arrays of numbers;
tagging each array of the long form content through a learning ensemble;
recombining the tagged arrays;
classifying the recombined arrays into pre-set content layouts;
storing the recombined arrays as small form content optimized for the small screen device (102).
, Description:TECHNICAL FIELD
[0001] The present disclosure generally relates to the field of data processing and, more particularly, to a system and method for optimizing content designed for large screen device, to be displayed on a small screen device.

BACKGROUND
[0002] There is a massive amount of content that has been created for consumption in large screen formats for computers, laptops, etc., owned by universities, companies and other organizations alike. This content can be a few years to a few decades old. Converting this content for readability in a small screen device like a mobile is a time consuming, human act and therefore extremely expensive. Consequently, most of this content is fit into a mobile optimized view by compressing fewer words in a single line. As shown in FIG.1, content (90) associated with a large screen is provided on a small screen. Consequently, one sentence runs into multiple lines leading to large number of vertical scrolls. This has led to poor consumption of content. Since mobile has become the primary source of digital content in the developing world, instead of a large screen computer, this poses a major challenge to content publishers, because their content does not get fully consumed by their audience.
[0003] Generally, storage and computing abilities of a small screen device are limited compared to a fully capable computer. If large screen format has a relatively large size, data processed in reading the large screen format by the small screen device may occupy a lot of storage resources, thus causing operation efficiency of the parsing step and the full paging step to decrease when reading the large screen format, and prolonging the time for the small screen device to read large screen format. Moreover, the larger the screen format size is, the more memory resources consumed in the parsing step and the full paging step are, such that the operation efficiency of the small screen device decreases. In a severe situation, the small screen device may not display the large screen format smoothly or even crash.
[0004] United States Patent Publication US20150106697A1 provides a method for processing an e-book document, includes: obtaining an e-book document; dividing content of the e-book document into a plurality of segments in accordance with a preset segmentation manner; composing the plurality of segments into an ordered segment group; selecting one segment from the segment group as a current segment; parsing the content of the current segment to generate layout data; and generating a page image in accordance with the layout data.
[0005] The present invention avoids the problems/disadvantages noted above and overcome other problems encountered in conventional methods. The objects, advantages and novel features of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the detailed description.

OBJECTS OF THE PRESENT DISCLOSURE
[0006] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0007] It is an object of the present disclosure to overcome problems associated with the conventional prior art.
[0008] It is an object of the present disclosure to provide a system and method for optimizing content designed for large screen device, to be displayed on a small screen device
[0009] It is an object of the present disclosure to lays down systems and methods to convert long form content into short mobile optimized “bites” without human intervention, which are easy to consume by the target audience using mobile devices.
[00010] It is an object of the present disclosure to convert long form content, typically containing thousands of words, into multiple short form content units, containing fewer words.
[00011] It is an object of the present disclosure to convert long form content into short form content, without losing the intent and meaning of the context.
[00012] It is an object of the present disclosure to provide small form content as pre-set content layouts on small screen devices for avoiding scrolling.

SUMMARY
[00013] The present disclosure generally relates to the field of data processing and, more particularly, to a system and method for optimizing content designed for large screen device, to be displayed on a small screen device.
[00014] According to an aspect of the present disclosure, a content optimizing system (200) implemented in a small screen device (102) is provided. The system (200) is configured to retrieve long form content associated with a large screen device (104); parse the long form content; configure the long form content into an array of arrays of numbers; tag each array of the long form content through a learning ensemble; recombine the tagged arrays; classify the recombined arrays into pre-set content layouts; store the recombined arrays as small form content optimized for the small screen device (102).
[00015] In an embodiment, the long form content includes any or a combination of plurality of long sentences and media.
[00016] In an embodiment, based upon configuring, each sentence of the plurality of sentences is converted into a vector of values representing parameters related to the sentence.
[00017] In an embodiment, each sentence is converted into a collection of a series of numbers representing attributes of a sentence, wherein the visual hierarchy and visual attributes of the long form content, as viewed by humans, is taken into account, by associating each sentence with numbers representing the visual elements like font sizes, color, font-weight and number of words.
[00018] In an embodiment, based upon configuring, the media associated with the long form content is uploaded in a server (106).
[00019] In an embodiment, the recombined arrays being classified into pre-set content layouts include the media retrieved from the server (106).
[00020] In an embodiment, the long form content is classified into a plurality of classes.
[00021] In an embodiment, the pre-set content layouts are a collection of pre-generated layouts suitable for the small screen device (102) viewing.
[00022] According to another aspect of the present disclosure, a content optimizing method (500) includes retrieving long form content associated with a large screen device (104); parsing the long form content; configuring the long form content into an array of arrays of numbers; tagging each array of the long form content through a learning ensemble; recombining the tagged arrays; classifying the recombined arrays into pre-set content layouts; storing the recombined arrays as small form content optimized for the small screen device (102).
[00023] Various objects, features, aspects and advantages of the present disclosure will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like features.
[00024] Within the scope of this application it is expressly envisaged that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, in the claims and/or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. Features described in connection with one embodiment are applicable to all embodiments, unless such features are incompatible.

BRIEF DESCRIPTION OF DRAWINGS
[00025] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The diagrams are for illustration only, which thus is not a limitation of the present disclosure.
[00026] FIG. 1 illustrates a content (90) associated with a large screen device (104), and the same content (90) being displayed on a small screen device (102).
[00027] FIG. 2 illustrates an environment showing the small screen device (102), and the large screen device (104) being communicably coupled to a server (106) in accordance with an embodiment of the present disclosure.
[00028] FIG. 3 illustrates a schematic diagram associated with a content optimizing system (200) implemented in the small screen device (102), in accordance with an embodiment of the present disclosure.
[00029] FIG. 4 illustrates different pre-set content layouts (402, 404, and 406) in accordance with an embodiment of the present disclosure.
[00030] FIG. 5 illustrates a content optimizing method (500) in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[00031] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[00032] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[00033] Embodiments of the present invention include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, and firmware and/or by human operators.
[00034] Various methods described herein may be practiced by combining one or more machine-readable storage media containing the code according to the present invention with appropriate standard computer hardware to execute the code contained therein. An apparatus for practicing various embodiments of the present invention may involve one or more computers (or one or more processors within a single computer) and storage systems containing or having network access to computer program(s) coded in accordance with various methods described herein, and the method steps of the invention could be accomplished by modules, routines, subroutines, or subparts of a computer program product.
[00035] If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[00036] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[00037] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. These exemplary embodiments are provided only for illustrative purposes and so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. The invention disclosed may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Various modifications will be readily apparent to persons skilled in the art. The general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure). Also, the terminology and phraseology used is for the purpose of describing exemplary embodiments and should not be considered limiting. Thus, the present invention is to be accorded the widest scope encompassing numerous alternatives, modifications and equivalents consistent with the principles and features disclosed. For purpose of clarity, details relating to technical material that is known in the technical fields related to the invention have not been described in detail so as not to unnecessarily obscure the present invention.
[00038] Thus, for example, it will be appreciated by those of ordinary skill in the art that the diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating systems and methods embodying this invention. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the entity implementing this invention. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular named element.
[00039] Embodiments of the present invention may be provided as a computer program product, which may include a machine-readable storage medium tangibly embodying thereon instructions, which may be used to program a computer (or other electronic devices) to perform a process. The term “machine-readable storage medium” or “computer-readable storage medium” includes, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware).A machine-readable medium may include a non-transitory medium in which data may be stored and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections. Examples of a non-transitory medium may include, but are not limited to, a magnetic disk or tape, optical storage media such as compact disk (CD) or digital versatile disk (DVD), flash memory, memory or memory devices. A computer-program product may include code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.
[00040] Furthermore, embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks (e.g., a computer-program product) may be stored in a machine-readable medium. A processor(s) may perform the necessary tasks.
[00041] Systems depicted in some of the figures may be provided in various configurations. In some embodiments, the systems may be configured as a distributed system where one or more components of the system are distributed across one or more networks in a cloud computing system.
[00042] Each of the appended claims defines a separate invention, which for infringement purposes is recognized as including equivalents to the various elements or limitations specified in the claims. Depending on the context, all references below to the "invention" may in some cases refer to certain specific embodiments only. In other cases it will be recognized that references to the "invention" will refer to subject matter recited in one or more, but not necessarily all, of the claims.
[00043] All methods described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
[00044] Various terms as used herein are shown below. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[00045] The present disclosure generally relates to the field of data processing and, more particularly, to a system and method for optimizing content designed for large screen device, to be displayed on a small screen device.
[00046] According to an aspect of the present disclosure, a content optimizing system (200) implemented in a small screen device (102) is provided. The system (200) is configured to retrieve long form content associated with a large screen device (104); parse the long form content; configure the long form content into an array of arrays of numbers; classify the configured long form content into pre-set categories; tag each array of the long form content through a learning ensemble; recombine the tagged arrays; classify the recombined arrays into pre-set content layouts; store the recombined arrays as small form content optimized for the small screen device (102).
[00047] In an embodiment, the long form content includes any or a combination of plurality of long sentences and media.
[00048] In an embodiment, based upon configuring, each sentence of the plurality of sentences is converted into a vector of values representing parameters related to the sentence.
[00049] In an embodiment, each sentence is converted into a collection of a series of numbers representing attributes of a sentence, wherein the visual hierarchy and visual attributes of the long form content, as viewed by humans, is taken into account, by associating each sentence with numbers representing the visual elements like font sizes, color, font-weight and number of words.
[00050] In an embodiment, based upon configuring, the media associated with the long form content is uploaded in a server (106).
[00051] In an embodiment, the recombined arrays being classified into pre-set content layouts include the media retrieved from the server (106).
[00052] In an embodiment, the long form content is classified into a plurality of classes.
[00053] In an embodiment, the pre-set content layouts are a collection of pre-generated layouts suitable for the small screen device (102) viewing.
[00054] According to another aspect of the present disclosure, a content optimizing method (500) includes retrieving long form content associated with a large screen device (104); parsing the long form content; configuring the long form content into an array of arrays of numbers; classifying the configured long form content into pre-set categories; tagging each array of the long form content through a learning ensemble; recombining the tagged arrays; classifying the recombined arrays into pre-set content layouts; storing the recombined arrays as small form content optimized for the small screen device (102).
[00055] FIG. 1 illustrates a content (90) associated with a large screen device (104), and the same content (90) being displayed on a small screen device (102). Evidently, the content (90) is not optimized for the small screen device (102), and one sentence runs into multiple lines leading to large number of vertical scrolls. This lead to poor consumption of content.
[00056] FIG. 2 illustrates an environment showing the small screen device (102), and the large screen device (104) being communicably coupled to a server (106) in accordance with an embodiment of the present disclosure. In an embodiment, a long form content associated with the large screen device (104) will be optimized for the small screen device (102).
[00057] FIG. 3 illustrates a schematic diagram associated with a content optimizing system (200) implemented in the small screen device (102) in accordance with an embodiment of the present disclosure. In an embodiment, the system (200) is configured to optimize the long form content associated with the large screen device (104) for the small screen device (102). In an example, the long form content includes any or a combination of plurality of long sentences and media.
[00058] In an aspect, the system (200) may comprise one or more hardware processor(s) (202). The one or more hardware processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more hardware processor(s) (202) are configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (112). The memory (204) may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[00059] The system (200) also includes an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) (206) may facilitate communication of the system (200) with various devices coupled to the system (200) such as the server (106). The interface(s) (206) may also provide a communication pathway for one or more components of system (200). Examples of such components include, but are not limited to, processing engine(s) (208) and data (210).
[00060] The processing engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the system (200) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to system (112) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.
[00061] The data (210) may comprise data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) (208).
[00062] In an exemplary embodiment, the processing engine(s) (208) may comprise an input module (212), a parsing module (214), an array module (216), a classification module (218), and an output module (220).
[00063] It would be appreciated that modules being described are only exemplary modules and any other module or sub-module may be included as part of the system (200). These modules too may be merged or divided into super-modules or sub-modules as may be configured.
[00064] Functionalities of various modules as mentioned above will now be described in detail in the foregoing description in accordance with an embodiment of the present disclosure. In an embodiment, the various modules mentioned above enables the system (200) for optimizing the long form content associated with the large screen device (104) for the small screen device (102) in accordance with an embodiment of the present disclosure.
[00065] In an embodiment, the input module (212) is configured to retrieve the long form content associated with the large screen device (104) through the server (106). Further, the parsing module (214) is configured to parse the long form content. In an example, the long form content is parsed in the form of power point slides, word documents, pdf documents, or the like. Further, based upon configuring, the media associated with the long form content is uploaded in the server (106). Further, the array module (216) configures the long form content into an array of arrays of numbers, wherein each array of the arrays of numbers represents a sentence of the plurality of long sentences, and each element of the array represents an attribute associated with content of the sentence. In an embodiment, based upon configuring, each sentence of the plurality of sentences is converted into a vector of values representing parameters related to the sentence. In an example, the array module (216) convert each sentence into a collection of a series of numbers representing attributes of a sentence, wherein the visual hierarchy and visual attributes of the long form content, as viewed by humans, is taken into account, by associating each sentence with numbers representing the visual elements like font sizes, color, font-weight and number of words. For example, a statement, representing the long form content, “this is an application” maybe converted to an array like: [1.000, 0.257, 0.219, 0.011, 0.413, 0, 0, 0, 0, 0.088, 0.245] each number representing, for instance, [bold, 25 percentile in terms of length of the sentence, 20 percentile in terms of appearance in the document, 1 percentile in terms of beginning of a paragraph, 41 percentile in terms of font size and so on.
[00066] Further, the classification module (218) is configured to classify the configured long form content, wherein each sentence off the long form content is classified into pre-set categories. Further, the classification module (218) tags each array of the long form content through a learning ensemble, wherein the processor (202) ingests a series of numbers representing different attributes of a sentence and its correlation with other sentences, and associate a label or a tag with the given series of number. Further, the classification module (218) ingests the series and attaches a label thereby classifying the sentence into the pre-set categories. For example, the sentence mentioned earlier may be classified as follows: {“head”:0.77, “body”: “0.23”}, indicating that there is 77% probability that this could be a title.
[00067] Further in an embodiment, the classification module (218) recombines the tagged arrays into matrices of arrays, which represent paragraphs, to recreate the entire content only in terms of content attributes, wherein the processor (202) collects all classified sentences into smaller paragraphs. Further, the classification module (218) classifies the recombined arrays into pre-set content layouts, wherein each of these paragraphs are run through the supervised learning ensemble, and then classified into the pre-set content layouts. In an example, the layouts act as classification categories for different blocks of content. Further, the classification module (218) retrieves the media files stored in the server (106), and position the media files along with text of the smaller paragraphs, such that the intent and meaning of the long form content is maintained.
[00068] FIG. 4 illustrates different pre-set content layouts (402, 404, and 406) in accordance with an embodiment of the present disclosure. In an embodiment, the pre-set content layouts (402, 404, and 406) are suitable for mobile viewing or viewing on the small screen device (102). In an example, the pre-set content layout may be media and text layout (402), a media only layout (404), or a text only layout (406). Further in an embodiment, the output module (220) stores the recombined arrays as small form content optimized for the small screen device (102). Further, the output module (220) may store the small form content in the server (106) for easy retrieving by an end user of the screen device (102).
[00069] FIG. 5 illustrates a content optimizing method (500) in accordance with an embodiment of the present disclosure.
[00070] In an aspect, the proposed method may be described in general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method can also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[00071] The order in which the method as described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method or alternate methods. Additionally, individual blocks may be deleted from the method without departing from the spirit and scope of the subject matter described herein. Furthermore, the method may be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method may be considered to be implemented in the above described system.
[00072] In an embodiment, at step (502) the method (500) includes retrieving long form content associated with a large screen device (104). At step (504) the method (500) includes parsing the long form content. At step (506) the method (500) includes configuring the long form content into an array of arrays of numbers. At step (508) the method (500) includes classifying the configured long form content. At step (510) the method (500) includes tagging each array of the long form content through a learning ensemble. At step (512) the method (500) includes recombining the tagged arrays. At step (514) the method (500) includes classifying the recombined arrays into pre-set content layouts. At step (516) the method (500) includes storing the recombined arrays as small form content optimized for the small screen device (102).
[00073] While some embodiments of the present disclosure have been illustrated and described, those are completely exemplary in nature. The disclosure is not limited to the embodiments as elaborated herein only and it would be apparent to those skilled in the art that numerous modifications besides those already described are possible without departing from the inventive concepts herein. All such modifications, changes, variations, substitutions, and equivalents are completely within the scope of the present disclosure. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims.